Query: Modified timeboxing process model for proper utilization of resources

Relevant passages:
THE EXPERIENCE FACTORY <=SEP=> This article presents an infrastructure, called the experience factory , aimed at capitalization and reuse of life cycle experience and products. The experience factory is a logical and physical organization, and its activities are independent from the ones of the development organization. The activities of the development organization and of the experience factory can be outlined in the following way:

The Timeboxing process model for iterative software development <=SEP=> In today’s business where speed is of essence, an iterative development approach that allows the functionality to be delivered in parts has become a necessity and an effective way to manage risks. In an iterative process, the development of a software system is done in increments, each increment forming of an iteration and resulting in a working system. A common iterative approach is to decide what should be developed in an iteration and then plan the iteration accordingly. A somewhat different iterative is approach is to time box different iterations. In this approach, the length of an iteration is fixed and what should be developed in an iteration is adjusted to fit the time box. Generally, the time boxed iterations are executed in sequence, with some overlap where feasible. In this paper we propose the timeboxing process model that takes the concept of time boxed iterations further by adding pipelining concepts to it for permitting overlapped execution of different iterations. In the timeboxing process model, each time boxed iteration is divided into equal length stages, each stage having a defined function and resulting in a clear work product that is handed over to the next stage. With this division into stages, pipelining concepts are employed to have multiple time boxes executing concurrently, leading to a reduction in the delivery time for product releases. We illustrate the use of this process model through an example of a commercial project that was successfully executed using the proposed model.

Iterative and incremental developments. a brief history <=SEP=> Although many view iterative and incremental development as a modern practice, its application dates as far back as the mid-1950s. Prominent software-engineering thought leaders from each succeeding decade supported IID practices, and many large projects used them successfully. These practices may have differed in their details, but all had a common theme-to avoid a single-pass sequential, document-driven, gated-step approach.

A Methodology for Collecting Valid Software Engineering Data <=SEP=> An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. The data to be collected are based on the changes made to the software during development, and are obtained when the changes are made. To ensure accuracy of the data, validation is performed concurrently with software development and data collection. Validation is based on interviews with those people supplying the data. Results from using the methodology show that data validation is a necessary part of change data collection. Without it, as much as 50 percent of the data may be erroneous. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments. The application showed that the methodology was both feasible and useful.

Quantitative Evaluation of Software Quality <=SEP=> The study reported in this paper establishes a conceptual framework and some key initial results in the analysis of the characteristics of software quality. Its main results and conclusions are:
 • Explicit attention to characteristics of software quality can lead to significant savings in software life-cycle costs.
 • The current software state-of-the-art imposes specific limitations on our ability to automatically and quantitatively evaluate the quality of software.
 • A definitive hierarchy of well-defined, well-differentiated characteristics of software quality is developed. Its higher-level structure reflects the actual uses to which software quality evaluation would be put; its lower-level characteristics are closely correlated with actual software metric evaluations which can be performed.
 • A large number of software quality-evaluation metrics have been defined, classified, and evaluated with respect to their potential benefits, quantifiability, and ease of automation.
 •Particular software life-cycle activities have been identified which have significant leverage on software quality.
 Most importantly, we believe that the study reported in this paper provides for the first time a clear, well-defined framework for assessing the often slippery issues associated with software quality, via the consistent and mutually supportive sets of definitions, distinctions, guidelines, and experiences cited. This framework is certainly not complete, but it has been brought to a point sufficient to serve as a viable basis for future refinements and extensions.

A tutorial on the dynamics and control of wind turbines and wind farms <=SEP=> Wind energy is currently the fastest-growing energy source in the world, with a concurrent growth in demand for the expertise of engineers and researchers in the wind energy field. There are still many unsolved challenges in expanding wind power, and there are numerous problems of interest to systems and control researchers. In this paper, we first review the basic structure of wind turbines and then describe wind turbine control systems and control loops. Of great interest are the generator torque and blade pitch control systems, where significant performance improvements are achievable with more advanced systems and control research. We describe recent developments in advanced controllers for wind turbines and wind farms, and we also outline many open problems in the areas of modeling and control of wind turbines.

To Create What You Tell: Generating Videos from Captions <=SEP=> We are creating multimedia contents everyday and everywhere. While automatic content generation has played a fundamental challenge to multimedia community for decades, recent advances of deep learning have made this problem feasible. For example, the Generative Adversarial Networks (GANs) is a rewarding approach to synthesize images. Nevertheless, it is not trivial when capitalizing on GANs to generate videos. The difficulty originates from the intrinsic structure where a video is a sequence of visually coherent and semantically dependent frames. This motivates us to explore semantic and temporal coherence in designing GANs to generate videos. In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions. Unlike the naive discriminator which only judges pairs as fake or real, our discriminator additionally notes whether the video matches the correct caption. In particular, the discriminator network consists of three discriminators: video discriminator classifying realistic videos from generated ones and optimizes video-caption matching, frame discriminator discriminating between real and fake frames and aligning frames with the conditioning caption, and motion discriminator emphasizing the philosophy that the adjacent frames in the generated videos should be smoothly connected as in real ones. We qualitatively demonstrate the capability of our TGANs-C to generate plausible videos conditioning on the given captions on two synthetic datasets (SBMG and TBMG) and one real-world dataset (MSVD). Moreover, quantitative experiments on MSVD are performed to validate our proposal via Generative Adversarial Metric and human study.

ReLiDSS: Novel lie detection system from speech signal <=SEP=> Lying is among the most common wrong human acts that merits spending time thinking about it. The lie detection is until now posing a problem in recent research which aims to develop a non-contact application in order to estimate physiological changes. In this paper, we have proposed a preliminary investigation on which relevant acoustic parameter can be useful to classify lie or truth from speech signal. Our proposed system in is based on the Mel Frequency Cepstral Coefficient (MFCC) commonly used in automatic speech processing on our own constructed database ReLiDDB (ReGIM-Lab Lie Detection DataBase) for both cases lie detection and person voice recognition. We have performed on this database the Support Vector Machines (SVM) classifier using Linear kernel and we have obtained an accuracy of Lie and Truth detection of speech audio respectively 88.23% and 84.52%.

On Software Implementation of High Performance GHASH Algorithms <=SEP=> There have been several modes of operations available for symmetric key block ciphers, among which Galois Counter Mode (GCM) of operation is a standard. GCM mode of operation provides con dentiality with the help of symmetric key block cipher operating in counter mode. The authentication component of GCM comprises of Galois hash (GHASH) computation which is a keyed hash function. The most important component of GHASH computation is carry-less multiplication of 128bit operands which is followed by a modulo reduction. There have been a number of schemes proposed for e cient software implementation of carry-less multiplication to improve performance of GHASH by increasing the speed of multiplications. This thesis focuses on providing an e cient way of software implementation of high performance GHASH function as being proposed by Meloni et al., and also on the implementation of GHASH using a carry-less multiplication instruction provided by Intel on their Westmere architecture. The thesis work includes implementation of the high performance GHASH and its comparison to the older or standard implementation of GHASH function. It also includes comparison of the two implementations using Intel's carry-less multiplication instruction. This is the rst time that this kind of comparison is being done on software implementations of these algorithms. Our software implementations suggest that the new GHASH algorithm, which was originally proposed for the hardware implementations due to the required parallelization, can't take advantage of the Intel carry-less multiplication instruction PCLMULQDQ. On the other hand, when implementations are done without using the PCLMULQDQ instruction the new algorithm performs better, even if its inherent parallelization is not utilized. This suggest that the new algorithm will perform better on embedded systems that do not support PCLMULQDQ.

Theory of Credit Card Networks : A Survey of the Literature <=SEP=> Credit cards provide benefits to consumers and merchants not provided by other payment instruments as evidenced by their explosive growth in the number and value of transactions over the last 20 years. Recently, credit card networks have come under scrutiny from regulators and antitrust authorities around the world. The costs and benefits of credit cards to network participants are discussed. Focusing on interrelated bilateral transactions, several theoretical models have been constructed to study the implications of several business practices of credit card networks. The results and implications of these economic models along with future research topics are discussed.

Cytoscape.js: a graph theory library for visualisation and analysis <=SEP=> UNLABELLED
Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js.


AVAILABILITY AND IMPLEMENTATION
Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org.


CONTACT
gary.bader@utoronto.ca.

Time series forecasting using a deep belief network with restricted Boltzmann machines <=SEP=> Multi-layer perceptron (MLP) and other artificial neural networks (ANNs) have been widely applied to time series forecasting since 1980s. However, for some problems such as initialization and local optima existing in applications, the improvement of ANNs is, and still will be the most interesting study for not only time series forecasting but also other intelligent computing fields. In this study, we propose a method for time series prediction using Hinton & Salakhutdinov’s deep belief nets (DBN) which are probabilistic generative neural network composed by multiple layers of restricted Boltzmann machine (RBM). We use a 3-layer deep network of RBMs to capture the feature of input space of time series data, and after pretraining of RBMs using their energy functions, gradient descent training, i.e., back-propagation learning algorithm is used for fine-tuning connection weights between “visible layers” and “hidden layers” of RBMs. To decide the sizes of neural networks and the learning rates, Kennedy & Eberhart’s particle swarm optimization (PSO) is adopted during the training processes. Furthermore, “trend removal”, a preprocessing to the original data, is also approached in the forecasting experiment using CATS benchmark data. Additionally, approximating and short-term prediction of chaotic time series such as Lorenz chaos and logistic map were also applied by the proposed method.

Automatic Medical Concept Extraction from Free Text Clinical Reports, a New Named Entity Recognition Approach <=SEP=> Actually in the Hospital Information Systems, there is a wide range of clinical information representation from the Electronic Health Records (EHR), and most of the information contained in clinical reports is written in natural language free text. In this context, we are researching the problem of automatic clinical named entities recognition from free text clinical reports. We are using Snomed-CT (Systematized Nomenclature of Medicine – Clinical Terms) as dictionary to identify all kind of clinical concepts, and thus the problem we are considering is to map each clinical entity named in a free text report with its Snomed-CT unique ID. More in general, we are developed a new approach for the named entity recognition (NER) problem in specific domains, and we have applied it to recognize clinical concepts in free text clinical reports. In our approach we apply two types of NER approaches, dictionary-based and machine learning-based. We use a specific domain dictionary-based gazetteer (using Snomed-CT to get the standard clinical code for the clinical concept), and the main approach that we introduce is using a unsupervised shallow learning neural network, word2vec from Mikolov et al., to represent words as vectors, and then making the recognition based on the distance between candidates and dictionary terms. We have applied our approach on a Dataset with 318.585 clinical reports in Spanish from the emergency service of the Hospital “Rafael Méndez” from Lorca (Murcia) Spain, and preliminary results are encouraging. Key-Words: Snomed-CT, word2vec, doc2vec, clinical information extraction, skipgram, medical terminologies, search semantic, named entity recognition, ner, medical entity recognition

Hierarchical structure and the prediction of missing links in networks <=SEP=> Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that networks often exhibit hierarchical organization, in which vertices divide into groups that further subdivide into groups of groups, and so forth over multiple scales. In many cases the groups are found to correspond to known functional units, such as ecological niches in food webs, modules in biochemical networks (protein interaction networks, metabolic networks or genetic regulatory networks) or communities in social networks. Here we present a general technique for inferring hierarchical structure from network data and show that the existence of hierarchy can simultaneously explain and quantitatively reproduce many commonly observed topological properties of networks, such as right-skewed degree distributions, high clustering coefficients and short path lengths. We further show that knowledge of hierarchical structure can be used to predict missing connections in partly known networks with high accuracy, and for more general network structures than competing techniques. Taken together, our results suggest that hierarchy is a central organizing principle of complex networks, capable of offering insight into many network phenomena.

PROPOSED METHODOLOGY FOR EARTHQUAKE-INDUCED LOSS ASSESSMENT OF INSTRUMENTED STEEL FRAME BUILDINGS : BUILDING-SPECIFIC AND CITY-SCALE APPROACHES <=SEP=> The performance-based earthquake engineering framework utilizes probabilistic seismic demand models to obtain accurate estimates of building engineering demand parameters. These parameters are utilized to estimate earthquake-induced losses in terms of probabilistic repair costs, life-safety impact, and loss of function due to damage to a wide variety of building structural and non-structural components and content. Although nonlinear response history analysis is a reliable tool to develop probabilistic seismic demand models, it typically requires a considerable time investment in developing detailed building numerical model representations. In that respect, the challenge of city-scale damage assessment still remains. This paper proposes a simplified methodology that rapidly assesses the story-based engineering demand parameters (e.g., peak story drift ratios, residual story drift ratios, peak absolute floor accelerations) along the height of a steel frame building in the aftermath of an earthquake. The proposed methodology can be employed at a city-scale in order to facilitate rapid earthquake-induced loss assessment of steel frame buildings in terms of structural damage and monetary losses within a region. Therefore, buildings within an urban area that are potentially damaged after an earthquake event or scenario can be easily identified without a detailed engineering inspection. To illustrate the methodology for rapid earthquake-induced loss assessment at the city-scale we employ data from instrumented steel frame buildings in urban California that experienced the 1994 Northridge earthquake. Maps that highlight the expected structural and nonstructural damage as well as the expected earthquake-induced monetary losses are developed. The maps are developed with the geographical information system for steel frame buildings located in Los Angeles. Seong-Hoon Hwang and Dimitrios G. Lignos

Forecasting the winner of a tennis match <=SEP=> We propose a method to forecast the winner of a tennis match, not only at the beginning of the match, but also (and in particular) during the match. The method is based on a fast and flexible computer program, and on a statistical analysis of a large data set from Wimbledon, both at match and at point level. 2002 Elsevier Science B.V. All rights reserved.

Overconfidence in IT Investment Decisions: Why Knowledge can be a Boon and Bane at the same Time <=SEP=> Despite their strategic relevance in organizations, information technology (IT) investments still result in alarmingly high failure rates. As IT investments are such a delicate high-risk/high-reward matter, it is crucial for organizations to avoid flawed IT investment decision-making. Previous research in consumer and organizational decision-making shows that a decision’s accuracy is often influenced by decisionmakers’ overconfidence and that the magnitude of overconfidence strongly depends on decision-makers’ certainty of their knowledge. Drawing on these strands of research, our findings from a field survey (N=166) show that IT managers’ decisions in IT outsourcing are indeed affected by overconfidence. However, an in-depth investigation of three types of knowledge, namely experienced, objective and subjective knowledge, reveals that different types of knowledge can have contrasting effects on overconfidence and thus on the quality of IT outsourcing decisions. Knowledge can be a boon and bane at the same time. Implications for research and practice are discussed.

Bare bones differential evolution <=SEP=> Article history: Received 22 August 2007 Accepted 29 February 2008 Available online xxxx

Domain Alignment with Triplets <=SEP=> Deep domain adaptation methods can reduce the distribution discrepancy by learning domain-invariant embedddings. However, these methods only focus on aligning the whole data distributions, without considering the classlevel relations among source and target images. Thus, a target embeddings of a bird might be aligned to source embeddings of an airplane. This semantic misalignment can directly degrade the classifier performance on the target dataset. To alleviate this problem, we present a similarity constrained alignment (SCA) method for unsupervised domain adaptation. When aligning the distributions in the embedding space, SCA enforces a similarity-preserving constraint to maintain class-level relations among the source and target images, i.e., if a source image and a target image are of the same class label, their corresponding embeddings are supposed to be aligned nearby, and vise versa. In the absence of target labels, we assign pseudo labels for target images. Given labeled source images and pseudo-labeled target images, the similarity-preserving constraint can be implemented by minimizing the triplet loss. With the joint supervision of domain alignment loss and similarity-preserving constraint, we train a network to obtain domain-invariant embeddings with two critical characteristics, intra-class compactness and inter-class separability. Extensive experiments conducted on the two datasets well demonstrate the effectiveness of SCA.

Sensor fusion for robot control through deep reinforcement learning <=SEP=> Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.

Optimization Criteria, Sensitivity and Robustness of Motion and Structure Estimation <=SEP=> The prevailing efforts to study the standard formulation of motion and structure recovery have been recently focused on issues of sensitivity and and robustness of existing techniques. While many cogent observations have been made and verified experimentally, many statements do not hold in general settings and make a comparison of existing techniques difficult. With an ultimate goal of clarifying these issues we study the main aspects of the problem: the choice of objective functions, optimization techniques and the sensitivity and robustness issues in the presence of noise. We clearly reveal the relationship among different objective functions, such as “(normalized) epipolar constraints”, “reprojection error” or “triangulation”, which can all be be unified in a new “ optimal triangulation” procedure formulated as a constrained optimization problem. Regardless of various choices of the objective function, the optimization problems all inherit the same unknown parameter space, the so called “essential manifold”, making the new optimization techniques on Riemanian manifolds directly applicable. Using these analytical results we provide a clear account of sensitivity and robustness of the proposed linear and nonlinear optimization techniques and study the analytical and practical equivalence of different objective functions. The geometric characterization of critical points of a function defined on essential manifold and the simulation results clarify the difference between the effect of bas relief ambiguity and other types of local minima leading to a consistent interpretations of simulation results over large range of signal-to-noise ratio and variety of configurations. Further more we justify the choice of the linear techniques for (re)initialization and for detection of incorrect local minima.

A comparison of loop closing techniques in monocular SLAM <=SEP=> Loop closure detection systems for monocular SLAM come in three broad categories: (i) map-to-map, (ii) image-to-image and (iii) image-to-map. In this paper, we have chosen an implementation of each and performed experiments allowing the three approaches to be compared. The sequences used include both indoor and outdoor environments and single and multiple loop trajectories. © 2009 Elsevier B.V. All rights reserved.

A Simplified Model for Normal Mode Helical Antennas <=SEP=> ─ Normal mode helical antennas are widely used for RFID and mobile communications applications due to their relatively small size and omni-directional radiation pattern. However, their highly curved geometry can make the design and analysis of helical antennas that are part of larger complex structures quite difficult. A simplified model is proposed that replaces the curved helix with straight wires and lumped elements. The simplified model can be used to reduce the complexity of full-wave models that include a helical antenna. It also can be used to estimate the performance of a helical antenna without fullwave modeling of the helical structure. Index Terms─ Helical Antennas, RFID.

Likelihood-informed dimension reduction for nonlinear inverse problems <=SEP=> The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively lowdimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the “likelihoodinformed subspace” (LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. This identification enables new and more efficient computational methods for Bayesian inference with nonlinear forward models and Gaussian priors. In particular, we approximate the posterior distribution as the product of a lower-dimensional posterior defined on the LIS and the prior distribution marginalized onto the complementary subspace. Markov chain Monte Carlo sampling can then proceed in lower dimensions, with significant gains in computational efficiency. We also introduce a Rao-Blackwellization strategy that de-randomizes Monte Carlo estimates of posterior expectations for additional variance reduction. We demonstrate the efficiency of our methods using two numerical examples: inference of permeability in a groundwater system governed by an elliptic PDE, and an atmospheric remote sensing problem based on Global Ozone Monitoring System (GOMOS) observations.

A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge <=SEP=> We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.

Upgrading Lignocellulosic Biomasses : Hydrogenolysis of Platform Derived Molecules Promoted by Heterogeneous Pd-Fe Catalysts <=SEP=> This review provides an overview of heterogeneous bimetallic Pd-Fe catalysts in the C–C and C–O cleavage of platform molecules such as C2–C6 polyols, furfural, phenol derivatives and aromatic ethers that are all easily obtainable from renewable cellulose, hemicellulose and lignin (the major components of lignocellulosic biomasses). The interaction between palladium and iron affords bimetallic Pd-Fe sites (ensemble or alloy) that were found to be very active in several sustainable reactions including hydrogenolysis, catalytic transfer hydrogenolysis (CTH) and aqueous phase reforming (APR) that will be highlighted. This contribution concentrates also on the different synthetic strategies (incipient wetness impregnation, deposition-precipitaion, co-precipitaion) adopted for the preparation of heterogeneous Pd-Fe systems as well as on the main characterization techniques used (XRD, TEM, H2-TPR, XPS and EXAFS) in order to elucidate the key factors that influence the unique catalytic performances observed.

Building Consumer Trust Online <=SEP=> M oving some Web consumers along to the purchase click is proving to be difficult, despite the impressive recent growth in online shopping. Consumer online shopping revenues and related corporate profits are still meager, though the industry is optimistic, thanks to bullish forecasts of cyberconsumer activity for the new millennium. In 1996, Internet shopping revenues for U.S. users, excluding cars and real estate, were estimated by Jupiter Communications , an e-commerce consulting firm in New York, at approximately $707 million but are expected to How merchants can win back lost consumer trust in the interests of e-commerce sales. reach nearly $37.5 billion by 2002 [1]. Meanwhile, the business-to-business side is taking off with more than $8 billion in revenues for 1997 and $327 billion predicted by 2002 just in the U.S., according to For-rester Research, an information consulting firm in Cambridge, Mass. [4]. On the consumer side, a variety of barriers are invoked to explain the continuing difficulties. There are, to be sure, numerous barriers. Such factors as the lack of standard technologies for secure payment, and the lack of profitable business models play important roles in the relative dearth of commercial activity by businesses and consumers on the Internet compared to what analysts expect in the near future. Granted, the commercial development of the Web is still in its infancy, so few expect these barriers to commercial development to persist. Still, commercial development of the Web faces a far more formidable barrier—consumers' fear of divulging their personal data—to its ultimate com-mercialization. The reason more people have yet to shop online or even provide information to Web providers in exchange for access to information, is the fundamental lack of faith between most businesses and consumers on the Web today. In essence, consumers simply do not trust most Web providers enough to engage in " relationship exchanges " involving money and personal information with them. Our research reveals that this lack of trust arises from the fact that cyberconsumers feel they lack control over the access that Web merchants have to their

Perception-Guided Multimodal Feature Fusion for Photo Aesthetics Assessment <=SEP=> Photo aesthetic quality evaluation is a challenging task in multimedia and computer vision fields. Conventional approaches suffer from the following three drawbacks: 1) the deemphasized role of semantic content that is many times more important than low-level visual features in photo aesthetics; 2) the difficulty to optimally fuse low-level and high-level visual cues in photo aesthetics evaluation; and 3) the absence of a sequential viewing path in the existing models, as humans perceive visually salient regions sequentially when viewing a photo.
 To solve these problems, we propose a new aesthetic descriptor that mimics humans sequentially perceiving visually/semantically salient regions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local aesthetic descriptors (graphlets in this work) into a low-dimensional semantic space. Thereafter, each graphlet can be described by multiple types of visual features, both at low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent graphlets in a photo. They are sequentially linked into a path that simulates the process of humans actively viewing. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos that are marked as aesthetically pleasing by multiple users. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data; and 2) our photo aesthetic measure outperforms many of its competitors.

Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models <=SEP=> Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the driving context from both inside and outside of the car. We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80% F1-score in real-time.

Pattern recognition based techniques for fruit sorting : A survey <=SEP=> In most of the food industries, farms and fruit markets the quality sorting of the fruits is done manually; only a few automated sorting systems are developed for this. Each fruit changes its color in its life span; including stages not ripe, semi ripped, completely ripped or at the end rotten. Hence, we can predict the quality of the fruits by processing its color images and then applying pattern recognition techniques on those images. By quality of fruit we mean size, ripeness, sweetness, longetivity, diseased/ rotten etc. Some advance technologies which are used for classification of fruits are Infrared Imaging, Magnetic Resonance Imaging, Thermal Sensing, Electronic Nose sensing etc; but these are costlier as compared to color image processing and pattern recognition techniques. This paper summarizes and compares various techniques of Pattern Recognition which are used effectively for fruit sorting. All the fruits have their unique ways of ripening stages; hence the pattern recognition techniques used for one fruit does not necessarily match for the other fruit. This paper reviews the high quality research work done for the quality classification of Apples, Dates, Mangoes, Cranberry, Jatropha, Tomatoes, and some other fruits using pattern recognition techniques. KeywordsClassification & predication, Pattern Recognition, Image Processing, Fruit Quality __________________________________________________*****_________________________________________________