Query: Finding Rough Set Reducts with Ant Colony Optimization

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The Ant System Applied to the Quadratic Assignment Problem <=SEP=> In recent years there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures which can solve complex problems. In this article we introduce a distributed heuristic algorithm which was inspired by the observation of the behavior of ant colonies and we propose its use for the Quadratic Assignment Problem. Finally the results obtained in solving some classical instances of the problem are compared with those obtained from other evolutionary heuristics to evaluate the quality of the system proposed. ^Dipartimento di Scienze dell'Informazione, Università di Bologna, Via Sacchi 3, 47023, Cesena, Italy, EU, maniezzo@csr.unibo.it & Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio 34/5, 20133, Milano, Italy, EU, colorni@elet.polimi.it #IRIDIA, Université Libre de Bruxelles, Av. Franklin Roosevelt 50, CP 194/6, 1050 Brussels, Belgium, EU, mdorigo@ulb.ac.be

Programs for Machine Learning. Part I <=SEP=> 

Optimization, Learning and Natural Algorithms <=SEP=> 

Optimization by simulated annealing <=SEP=> 

1 Positive Feedback as a Search Strategy <=SEP=> A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.

A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic <=SEP=> This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic with data obtained from both online newspaper commentary and Twitter. Most Arabic corpora are small and focus on Modern Standard Arabic (MSA). There has been recent interest, however, in the construction of dialectal Arabic corpora (Zaidan and Callison-Burch, 2011a; Al-Sabbagh and Girju, 2012). This work differs from previously constructed corpora in two ways. First, we include coverage of five dialects of Arabic: Egyptian, Gulf, Levantine, Maghrebi and Iraqi. This is the most complete coverage of any dialectal corpus known to the authors. In addition to data, we provide results for the Arabic dialect identification task that outperform those reported in Zaidan and Callison-Burch (2011a).

Building Rome on a Cloudless Day <=SEP=> This paper introduces an approach for dense 3D reconstruction from unregistered Internet-scale photo collections with about 3 million images within the span of a day on a single PC (“cloudless”). Our method advances image clustering, stereo, stereo fusion and structure from motion to achieve high computational performance. We leverage geometric and appearance constraints to obtain a highly parallel implementation on modern graphics processors and multi-core architectures. This leads to two orders of magnitude higher performance on an order of magnitude larger dataset than competing state-of-the-art approaches.

Device-free gesture tracking using acoustic signals: demo <=SEP=> In this demo, we present LLAP, a hand tracking system that uses ultrasound to localize the hand of the user to enable device-free gesture inputs. LLAP utilizes speakers and microphones on Commercial-Off-The-Shelf (COTS) mobile devices to play and record sound waves that are inaudible to humans. By measuring the phase of the sound signal reflected by the hands or fingers of the user, we can accurately measure the gesture movements. With a single pair of speaker/microphone, LLAP can track hand movement with accuracy of 3.5 mm. For devices with two microphones, LLAP enables drawing-in-the air capability with tracking accuracy of 4.6 mm. Moreover, the latency for LLAP is smaller than 15 ms for both the Android and the iOS platforms so that LLAP can be used for real-time applications.

Data Preparation for Data Mining <=SEP=> Senior Editor: Diane D. Cerra Director of Production & Manufacturing: Yonie Overton Production Editor: Edward Wade Editorial Assistant: Belinda Breyer Cover Design: Wall-To-Wall Studios Cover Photograph: © 1999 PhotoDisc, Inc. Text Design & Composition: Rebecca Evans & Associates Technical Illustration: Dartmouth Publishing, Inc. Copyeditor: Gary Morris Proofreader: Ken DellaPenta Indexer: Steve Rath Printer: Courier Corp.

Compressive coded aperture imaging <=SEP=> Nonlinear image reconstruction based upon sparse representations of images has recently received widespread attention with the emerging framework of compressed sensing (CS). This theory indicates that, when feasible, judicious selection of the type of distortion induced by measurement systems may dramatically improve our ability to perform image reconstruction. However, applying compressed sensing theory to practical imaging systems poses a key challenge: physical constraints typically make it infeasible to actually measure many of the random projections described in the literature, and therefore, innovative and sophisticated imaging systems must be carefully designed to effectively exploit CS theory. In video settings, the performance of an imaging system is characterized by both pixel resolution and field of view. In this work, we propose compressive imaging techniques for improving the performance of video imaging systems in the presence of constraints on the focal plane array size. In particular, we describe a novel yet practical approach that combines coded aperture imaging to enhance pixel resolution with superimposing subframes of a scene onto a single focal plane array to increase field of view. Specifically, the proposed method superimposes coded observations and uses wavelet-based sparsity recovery algorithms to reconstruct the original subframes. We demonstrate the effectiveness of this approach by reconstructing with high resolution the constituent images of a video sequence.

A multimodal feature learning approach for sentiment analysis of social network multimedia <=SEP=> In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.

Child murder by parents: a psychiatric review of filicide. <=SEP=> 

An overview of audio information retrieval <=SEP=> The problem of audio information retrieval is familiar to anyone who has returned from vacation to find an answering machine full of messages. While there is not yet an “AltaVista” for the audio data type, many workers are finding ways to automatically locate, index, and browse audio using recent advances in speech recognition and machine listening. This paper reviews the state of the art in audio information retrieval, and presents recent advances in automatic speech recognition, word spotting, speaker and music identification, and audio similarity with a view towards making audio less “opaque”. A special section addresses intelligent interfaces for navigating and browsing audio and multimedia documents, using automatically derived information to go beyond the tape recorder metaphor.

A lifespan database of adult facial stimuli. <=SEP=> Faces constitute a unique and widely used category of stimuli. In spite of their importance, there are few collections of faces for use in research, none of which adequately represent the different ages of faces across the lifespan. This lack of a range of ages has limited the majority of researchers to using predominantly young faces as stimuli even when their hypotheses concern both young and old participants. We describe a database of 575 individual faces ranging from ages 18 to 93. Our database was developed to be more representative of age groups across the lifespan, with a special emphasis on recruiting older adults. The resulting database has faces of 218 adults age 18-29, 76 adults age 30-49, 123 adults age 50-69, and 158 adults age 70 and older. These faces may be acquired for research purposes from http://agingmind.cns.uiuc.edu/facedb/. This will allow researchers interested in using facial stimuli access to a wider age range of adult faces than has previously been available.

Quantifying Trading Behavior in Financial Markets Using Google Trends <=SEP=> Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as "early warning signs" of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.

Low-power high-performance SAR ADC with redundancy and digital background calibration <=SEP=> As technology scales, the improved speed and energy efficiency make the successiveapproximation-register (SAR) architecture an attractive alternative for applications that require high-speed and high-accuracy analog-to-digital converters (ADCs). In SAR ADCs, the key linearity and speed limiting factors are capacitor mismatch and incomplete digital-to-analog converter (DAC)/reference voltage settling. In this thesis, a sub-radix-2 SAR ADC is presented with several new contributions. The main contributions include investigation of using digital error correction (redundancy) in SAR ADCs for dynamic error correction and speed improvement, development of two new calibration algorithms to digitally correct for manufacturing mismatches, design of new architecture to incorporate redundancy within the architecture itself while achieving 94% better energy efficiency compared to conventional switching algorithm, development of a new capacitor DAC structure to improve the SNR by four times with improved matching, joint design of the analog and digital circuits to create an asynchronous platform in order to reach the targeted performance, and analysis of key circuit blocks to enable the design to meet noise, power and timing requirements. The design is fabricated in standard 1P9M 65nm CMOS technology with 1.2V supply. The active die area is 0.083mm with full rail-to-rail input swing of 2.4VP−P . A 67.4dB SNDR, 78.1dB SFDR, +1.0/-0.9 LSB12 INL and +0.5/-0.7 LSB12 DNL are achieved at 50MS/s at Nyquist rate. The total power consumption, including the estimated calibration and reference power, is 2.1mW, corresponding to 21.9fJ/conv.step FoM. This ADC achieves the best FoM of any ADCs with greater than 10b ENOB and 10MS/s sampling rate. Thesis Supervisor: Duane S. Boning Title: Professor of Electrical Engineering and Computer Science Thesis Supervisor: Hae-Seung Lee Title: Professor of Electrical Engineering and Computer Science

Effects of time-to-trigger parameter on handover performance in SON-based LTE systems <=SEP=> In this paper, we evaluate handover (HO) performance when we apply various time-to-trigger (TTT) methods in self-organizing network (SON)-based long term evolution (LTE) systems. Although TTT can mitigate the wasteful ping-pong HO effect, it also can cause undesirable radio link failure (RLF) due to delayed HO. The optimal HO timings that produce the lowest ping-pong rate within allowable RLF rate vary depending on user equipment (UE) speeds and neighboring cell configurations. To achieve efficient HO timings, we propose and investigate two methods: “adaptive” and “grouping.” In the “adaptive method,” we select the adaptive TTT value for each UE speed based on RLF rate of 2%. The “grouping method” classifies UE speeds into three ranges and assigns the proper TTT value to each range. To apply the LTE specification more effectively, we suggest the criteria of grouping, and propose the proper TTT value for each range. We consider HO in two neighboring cell configurations: from a macro cell to either a macro cell or a pico cell. Simulation results show that the HO performance of the “adaptive method” is greatly improved compared to that of applying fixed TTT values. The results also show that the performance of the “grouping method”, using proposed criteria and proper TTT values, is comparable to that of the “adaptive method.”

A new linearization method of unbalanced electrical distribution networks <=SEP=> With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. As DN control and operation strategies are mostly based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages, line currents, power loss) and control variables (e.g., power injections, transformer tap positions), efficient and precise calculation of these sensitivity coefficients, i.e. linearization of DN, is of fundamental importance. In this paper, the derivation of the node voltages and power loss as functions of the nodal power injections and transformers' tap-changers positions is presented, and then solved by a Gauss-Seidel method. Compared to other approaches presented in the literature, the proposed method takes into account different load characteristics (e.g., constant PQ, constant impedance, constant current and any combination of above) of a generic multi-phase unbalanced DN and improves the accuracy of linearization. Numerical simulations on both IEEE 13 and 34 nodes test feeders show the efficiency and accuracy of the proposed method.

A compact filtering power divider based on SIW triangular cavities <=SEP=> A compact substrate-integrated waveguide (SIW) filtering power divider with high isolation is presented in this paper based on SIW triangular cavities. An isolation resistance is exploited to achieve high isolation between two output ports. The design parameters can be realized by determining proper offset positions of the feeding ports and coupling windows, which can be fixed by the synthesized Qe and Mij. By using three triangular cavities as the basic elements, a filtering power divider is designed and fabricated on a single-layer substrate with the standard printed circuit boards (PCB) process. This filtering power divider operates at the center frequency of 11.4 GHz with the bandwidths of 0.75 GHz. There are good agreements in the simulated and measured results.

Rethinking the fear circuit: the central nucleus of the amygdala is required for the acquisition, consolidation, and expression of Pavlovian fear conditioning. <=SEP=> In the standard model of pavlovian fear learning, sensory input from neutral and aversive stimuli converge in the lateral nucleus of the amygdala (LA), in which alterations in synaptic transmission encode the association. During fear expression, the LA is thought to engage the central nucleus of the amygdala (CE), which serves as the principal output nucleus for the expression of conditioned fear responses. In the present study, we reexamined the roles of LA and CE. Specifically, we asked whether CE, like LA, might also be involved in fear learning and memory consolidation. Using functional inactivation methods, we first show that CE is involved not only in the expression but also the acquisition of fear conditioning. Next, we show that inhibition of protein synthesis in CE after training impairs fear memory consolidation. These findings indicate that CE is not only involved in fear expression but, like LA, is also involved in the learning and consolidation of pavlovian fear conditioning.

Readings in information visualization - using vision to think <=SEP=> Visualization: The use of computer-supported, interactive, visual representations of data to amplify cognition. (6) Cognition is the acquisition or use of knowledge. This definition has the virtue of focusing as much on the purpose of visualization as the means. Hamming (1973) saud, “the purpose of computation is insight, not numbers.” Likewise for visualization, “the purpose of visualization is insight not pictures.” The main goals of this insight are discovery, decision  making, and explanation. (6)

Deconstructing the Blockchain to Approach Physical Limits <=SEP=> The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger for an electronic payment system, Bitcoin. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput, confirmation latency and confirmation reliability. Existing systems operate far away from these physical limits. In this work we introduce Prism , a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in the bandwidth-delay product CD; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing the blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits.

A comparative study of classification algorithms for forecasting rainfall <=SEP=> India is an agricultural country which largely depends on monsoon for irrigation purpose. A large amount of water is consumed for industrial production, crop yield and domestic use. Rainfall forecasting is thus very important and necessary for growth of the country. Weather factors including mean temperature, dew point temperature, humidity, pressure of sea and speed of wind and have been used to forecasts the rainfall. The dataset of 2245 samples of New Delhi from June to September (rainfall period) from 1996 to 2014 has been collected from a website named Weather Underground. The training dataset is used to train the classifier using Classification and Regression Tree algorithm, Naive Bayes approach, K nearest Neighbour and 5-10-1 Pattern Recognition Neural Network and its accuracy is tested on a test dataset. Pattern Recognition networks has given 82.1% accurate results, KNN with 80.7% correct forecasts ranks second, Classification and Regression Tree(CART) gives 80.3% while Naive Bayes provides 78.9% correctly classified samples.

Vertical ridge augmentation in the esthetic zone. <=SEP=> The reconstruction of deficient alveolar ridges using vertical and/or horizontal guided bone regeneration techniques allows for ideal implant placement, which is crucial for function and also for esthetically successful outcomes. Unlike in the past, when meeting a patient's functional demands was sufficient, many patients now have greater expectations from their implant restoration. Hence, it is no longer enough simply to restore the edentulous space with a functioning tooth or teeth. It has been suggested that patients now measure their final restoration using the contralateral natural tooth as the gold standard. Both subjective and objective levels of patient information on dental implants have increased significantly in the last decade. As a result of this demand, implant literature has inherited and developed specific esthetic parameters and patient-centered outcomes from studies in the restorative field. Unfortunately, studies reporting on guided bone regeneration in the esthetic zone entirely lack such parameters and outcomes. Currently, there is a strong need for a consensus on objective and well-defined parameters to assess the esthetics in bone regeneration and subsequently on implant dentistry.

A New Discrete Particle Swarm Optimization Algorithm <=SEP=> Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms. In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.

Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery <=SEP=> Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features Remote Sens. 2015, 7 14681 from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the lowand mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.

Memory Leak Analysis by Contradiction <=SEP=> We present a novel leak detection algorithm. To prove the absence of a memory leak, the algorithm assumes its presence and runs a backward heap analysis to disprove this assumption. We have implemented this approach in a memory leak analysis tool and used it to analyze several routines that manipulate linked lists and trees. Because of the reverse nature of the algorithm, the analysis can locally reason about the absence of memory leaks. We have also used the tool as a scalable, but unsound leak detector for C programs. The tool has found several bugs in larger programs from the SPEC2000 suite.

Sign language recognition using Microsoft Kinect <=SEP=> In last decade lot of efforts had been made by research community to create sign language recognition system which provide a medium of communication for differently-abled people and their machine translations help others having trouble in understanding such sign languages. Computer vision and machine learning can be collectively applied to create such systems. In this paper, we present a sign language recognition system which makes use of depth images that were captured using a Microsoft Kinect® camera. Using computer vision algorithms, we develop a characteristic depth and motion profile for each sign language gesture. The feature matrix thus generated was trained using a multi-class SVM classifier and the final results were compared with existing techniques. The dataset used is of sign language gestures for the digits 0-9.

Injury Risk Quantification for Industrial Robots in Collaborative Operation with Humans <=SEP=> While the topic of human-safe robots has an appreciable academic history, only today are various solutions to this challenge becoming economically relevant. We are witnessing a maturing of technology and market expectations, paired with initiatives in the area of standardization. One of the remaining hurdles to routine deployment of robots for collaborative operation with humans is obtaining a well-founded understanding of low-level injuries of the type that such robots can potentially inflict. For the assessment of collaborative robot designs and systems, the low-level injury potential must be unambiguously quantified. A classification scheme for holding such information is presented and an outlook on its use is given.

Acceptance Model in the Domains of LIS and Education : A Review of Selected Literature <=SEP=> The Technology Acceptance Model (TAM) is a theoretical framework that is most extensively utilized in explaining an individual’s acceptance of an information technology or an information system. This study reviews numerous literature related to the TAM focusing on TAM applications in the domains of Library and Information Science (LIS) and Education. The different studies in these areas were evaluated to understand the modifications incorporated into this model. The study attempts provide insight on future trends in the technology acceptance model as well to help identify gaps in literature where future research could be conducted.