Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization

Bilevel optimization problems are a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Despite the efforts, there hardly exists any effective methodology, which is capable of handling a complex bilevel problem. In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. The approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations. Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for generic bilevel problems. The efficacy of the algorithm has been shown on two sets of test problems. The first set is a recently proposed SMD test set, which contains problems with controllable complexities, and the second set contains standard test problems collected from the literature. The proposed method has been evaluated against two benchmarks, and the performance gain is observed to be significant.


A new method for pulse oximetry possessing inherent insensitivity to artifact

A new method for pulse oximetry is presented that possesses an inherent insensitivity to corruption by motion artifact, a primary limitation in the practical accuracy and clinical applicability of current technology. Artifact corruption of the underlying photoplethysmographic signals is reduced in real time, using an electronic processing methodology that is based upon inversion of a physical artifact model. This fundamental approach has the potential to provide uninterrupted output and superior accuracy under conditions of sustained subject motion, therefore, widening the clinical scope of this useful measurement. A new calibration technique for oxygen saturation is developed for use with these processed signals, which is shown to be a generalization of the classical Interpretation. The detailed theoretical and practical issues of Implementation are then explored, highlighting important engineering simplifications implicit in this new approach. A quantitative investigation of the degree of insensitivity to artifact is also undertaken, with the aid of a custom electronic system and commercial pulse oximeter probes, which is compared and contrasted with the performance of a conventional implementation. It is demonstrated that this new methodology results in a reduced sensitivity to common classes of motion artifact, while retaining the generality to be combined with conventional signal processing techniques.


Intrinsic Motivation in Museums : Why Does One Want to Learn ?

One often meets successful adults, professionals, or scientists who recall that their lifelong vocational interest was first sparked by a visit to a museum. In these accounts the encounter with a real, concrete object from a different world an exotic animal, a strange dress, a beautiful artifact is the kernel from which a n entire career of learning grew For others with an already developed curiosity about some field such as zoology, anthropology, or art, the museum provided an essential link in the cultivation of knowledge a place where information lo'it its abstractness and became concrete. In either case, many people ascribe powerful motivation to a museum visit, claiming that their desire to learn more about some aspect of the world was directly caused by it. Granted that these accounts of "crystallizing experiences" (Waiters and Gaidner 1986) attributed to museums might often be embellished and exaggerated in retrospect, i t would be rash to dismiss them entirely, for the fascination of museums seems to be a very real psychological phenomenon. The question rather becomes. How do museums motivate viewers to learn? Is there a unique, sui generis "museum experience" that helps viewers start on the long journey of learning? How do museums present information in a meaningful way, a way that deepens a person's experience and promotes further learning? To begin answering these questions, it will be useful to review what we know about human motivation in relation to learning. Children are born with a desire for knowledge, and some of the most stupendous feats of learning to walk, talk, get along with others, to take care of oneself are accomplished without seeming effort in the first few years of life. It would be difficult to see how a species as dependent on learning as we are could have


The Language of Deceivers: Linguistic Features of Crowdfunding Scams

Crowdfunding sites with recent explosive growth are equally attractive platforms for swindlers or scammers. Though the growing number of articles on crowdfunding scams indicate that the fraud threats are accelerating, there has been little knowledge on the scamming practices and patterns. The key contribution of this research is to discover the hidden clues in the text by exploring linguistic features to distinguish scam campaigns from non-scams. Our results indicate that by providing less information and writing more carefully (and less informally), scammers deliberately try to deceive people; (i) they use less number of words, verbs, and sentences in their campaign pages. (ii) scammers make less typographical errors, 4.5-4.7 times lower than non-scammers.(iii) Expressivity of scams is 2.6-8.5 times lower as well.


Distributed-system Features and Challenges Shiviz Is a New Distributed System Debugging Visualization Tool Debugging Distributed Systems

D istributed systems pose unique challenges for software developers. Reasoning about concurrent activities of system nodes and even understanding the system’s communication topology can be difficult. A standard approach to gaining insight into system activity is to analyze system logs. Unfortunately, this can be a tedious and complex process. This article looks at several key features and debugging challenges that differentiate distributed systems from other kinds of software. The article presents several promising tools and ongoing research to help resolve these challenges.


Wireless Industrial Monitoring and Control Networks: The Journey So Far and the Road Ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks.


Trend Analysis in Social Networking using Opinion Mining A Survey

The popularity of social media in recent years has increased a lot and created new opportunities to study the interactions of different groups of people. Because of this it comes to possible to study the user‟s opinion. Two popular topics in the study of social networks are community detection and finding trends. Trend Analysis is an ongoing field of research in Data mining field, May referred as an “Opinion Mining” by some researchers. Trend Analysis is the computational treatment of opinions, sentiments and subjectivity of text. The related field to Trend Analysis contains finding emotions in a statement which attracted researchers recently are discussed. The main target of this survey is to give nearly full image of Trend Analysis along with the community detection and their related fields with brief details. The main contributions of this paper include the overall analysis to bridge the concept between trend analyses with community detection. To find out the anomaly detection in a community with using the concept of trend analysis is a difficult task.


Emergency department management of vaginal bleeding in the nonpregnant patient.

Abnormal uterine bleeding is the most common reason women seek gynecologic care, and many of these women present to an emergency department for evaluation. It is essential that emergency clinicians have a thorough understanding of the underlying physiology of the menstrual cycle to appropriately manage a nonpregnant woman with abnormal bleeding. Evidence to guide the management of nonpregnant patients with abnormal bleeding is limited, and recommendations are based mostly on expert opinion. This issue reviews common causes of abnormal bleeding, including anovulatory, ovulatory, and structural causes in both stable and unstable patients. The approach to abnormal bleeding in the prepubertal girl is also discussed. Emergency clinicians are encouraged to initiate treatment to temporize an acute bleeding episode until timely follow-up with a gynecologist can be obtained.


Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging.

The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. The availability of a noninvasive technique that can visualize the neuronal projections connecting the functional centers should therefore provide new keys to the understanding of brain function. By using high-resolution three-dimensional diffusion magnetic resonance imaging and a newly designed tracking approach, we show that neuronal pathways in the rat brain can be probed in situ. The results are validated through comparison with known anatomical locations of such fibers.


An aspect representation for object manipulation based on convolutional neural networks

We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning sequences of actions to solve tasks. In previous work, we introduced the aspect transition graph (ATG) which represents how actions lead from one observation to another using a directed multi-graph. In this work, we propose a novel aspect representation based on hierarchical CNN features, learned with convolutional neural networks, that supports manipulation and captures the essential affordances of an object based on RGB-D images. In a traditional planning system, robots are given a pre-defined set of actions that take the robot from one symbolic state to another. However symbolic states often lack the flexibility to generalize across similar situations. Our proposed representation is grounded in the robot's observations and lies in a continuous space that allows the robot to handle similar unseen situations. The hierarchical CNN features within a representation also allow the robot to act precisely with respect to the spatial location of individual features. We evaluate the robustness of this representation using the Washington RGB-D Objects Dataset and show that it achieves state of the art results for instance pose estimation. We then test this representation in conjunction with an ATG on a drill grasping task on Robonaut-2. We show that given grasp, drag, and turn demonstrations on the drill, the robot is capable of planning sequences of learned actions to compensate for reachability constraints.


Aspect Extraction Performance with POS Tag Pattern of Dependency Relation in Aspect-based Sentiment Analysis

The most important task in aspect-based sentiment analysis (ABSA) is the aspect and sentiment word extraction. It is a challenge to identify and extract each aspect and it specific associated sentiment word correctly in the review sentence that consists of multiple aspects with various polarities expressed for multiple sentiments. By exploiting the dependency relation between words in a review, the multiple aspects and its corresponding sentiment can be identified. However, not all types of dependency relation patterns are able to extract candidate aspect and sentiment word pairs. In this paper, a preliminary study was performed on the performance of different type of dependency relation with different POS tag patterns in pre-extracting candidate aspect from customer review. The result contributes to the identification of the specific type dependency relation with it POS tag pattern that lead to high aspect extraction performance. The combination of these dependency relations offers a solution for single aspect single sentiment and multi aspect multi sentiment cases.


Diversifying Web Service Recommendation Results via Exploring Service Usage History

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users' potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.


Exploring Social-Historical Ties on Location-Based Social Networks

Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to “check-in” at geographical locations and share such experiences with their friends. Millions of “check-in” records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user’s social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user’s check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user’s check-in behavior. In particular, our model captures the property of user’s check-in history in forms of power-law distribution and short-term effect, and helps in explaining user’s check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user’s checkins and shows how social and historical ties can help location prediction.


Mobile marketing: the role of permission and acceptance

The escalation and convergence of distributed networks and wireless telecommunications has created a tremendous potential platform for providing business services. In consumer markets, mobile marketing is expected to be a key growth area. The immediacy, interactivity and mobility of wireless devices provide a novel platform for marketing. The personal and ubiquitous nature of devices means that interactivity can be provided anytime and anywhere. However, as experience has shown, it is important to keep the consumer in mind. Mobile marketing permission and acceptance are core issues that marketers have yet to fully explain or resolve. This paper provides direction in this area. After briefly discussing some background on mobile marketing, the paper conceptualises key characteristics for mobile marketing permission and acceptance. The paper concludes with predictions on the future of mobile marketing and some core areas of further research.


Understanding mobility based on GPS data

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.


Compiler-Directed Lightweight Checkpointing for Fine-Grained Guaranteed Soft Error Recovery

This paper presents Bolt, a compiler-directed soft error recovery scheme, that provides fine-grained and guaranteed recovery without excessive performance and hardware overhead. To get rid of expensive hardware support, the compiler protects the architectural inputs during their entire liveness period by safely checkpointing the last updated value in idempotent regions. To minimize the performance overhead, Bolt leverages a novel compiler analysis that eliminates those checkpoints whose value can be reconstructed by other checkpointed values without compromising the recovery guarantee. As a result, Bolt incurs only 4.7% performance overhead on average which is 57% reduction compared to the state-of-the-art scheme that requires expensive hardware support for the same recovery guarantee as Bolt.


A ternary content-addressable memory (TCAM) based on 4T static storage and including a current-race sensing scheme

A 256 144-bit TCAM is designed in 0.18m CMOS. The proposed TCAM cell uses 4T static storage for increased density. The proposed match-line (ML) sense scheme reduces power consumption by minimizing switching activity of search-lines and limiting voltage swing of MLs. The scheme achieves a match-time of 3 ns and operates at a minimum supply voltage of 1.2 V.


The Neural Substrate of Human Empathy: Effects of Perspective-taking and Cognitive Appraisal

Whether observation of distress in others leads to empathic concern and altruistic motivation, or to personal distress and egoistic motivation, seems to depend upon the capacity for self-other differentiation and cognitive appraisal. In this experiment, behavioral measures and event-related functional magnetic resonance imaging were used to investigate the effects of perspective-taking and cognitive appraisal while participants observed the facial expression of pain resulting from medical treatment. Video clips showing the faces of patients were presented either with the instruction to imagine the feelings of the patient (imagine other) or to imagine oneself to be in the patient's situation (imagine self). Cognitive appraisal was manipulated by providing information that the medical treatment had or had not been successful. Behavioral measures demonstrated that perspective-taking and treatment effectiveness instructions affected participants' affective responses to the observed pain. Hemodynamic changes were detected in the insular cortices, anterior medial cingulate cortex (aMCC), amygdala, and in visual areas including the fusiform gyrus. Graded responses related to the perspective-taking instructions were observed in middle insula, aMCC, medial and lateral premotor areas, and selectively in left and right parietal cortices. Treatment effectiveness resulted in signal changes in the perigenual anterior cingulate cortex, in the ventromedial orbito-frontal cortex, in the right lateral middle frontal gyrus, and in the cerebellum. These findings support the view that humans' responses to the pain of others can be modulated by cognitive and motivational processes, which influence whether observing a conspecific in need of help will result in empathic concern, an important instigator for helping behavior.


Tiny ImageNet Visual Recognition Challenge

In this work, we investigate the effect of convolutional network depth, receptive field size, dropout layers, rectified activation unit type and dataset noise on its accuracy in Tiny-ImageNet Challenge settings. In order to make a thorough evaluation of the cause of the peformance improvement, we start with a basic 5 layer model with 5×5 convolutional receptive fields. We keep increasing network depth or reducing receptive field size, and continue applying modern techniques, such as PReLu and dropout, to the model. Our model achieves excellent performance even compared to state-of-the-art results, with 0.444 final error rate on the test set.


Immersive VR for Scientific Visualization: A Progress Report

potential to be a powerful tool for the visualization of burgeoning scientific data sets and models. In this article we sketch a research agenda for the hardware and software technology underlying IVR for scientific visualization. In contrast to Brooks’ excellent survey last year, which reported on the state of IVR and provided concrete examples of its production use, this article is somewhat speculative. We don’t present solutions but rather a progress report, a hope, and a call to action, to help scientists cope with a major crisis that threatens to impede their progress. Brooks’ examples show that the technology has only recently started to mature—in his words, it “barely works.” IVR is used for walkthroughs of buildings and other structures, virtual prototyping (vehicles such as cars, tractors, and airplanes), medical applications (surgical visualization, planning, and training), “experiences” applied as clinical therapy (reliving Vietnam experiences to treat post-traumatic stress disorder, treating agoraphobia), and entertainment. Building on Brooks’ work, here we concentrate on why scientific visualization is also a good application area for IVR. First we’ll briefly review scientific visualization as a means of understanding models and data, then discuss the problem of exploding data set size, both from sensors and from simulation runs, and the consequent demand for new approaches. We see IVR as part of the solution: as a richer visualization and interaction environment, it can potentially enhance the scientist’s ability to manipulate the levels of abstraction necessary for multi-terabyte and petabyte data sets and to formulate hypotheses to guide very long simulation runs. In short, IVR has the potential to facilitate a more balanced human-computer partnership that maximizes bandwidth to the brain by more fully engaging the human sensorium. We argue that IVR remains in a primitive state of development and is, in the case of CAVEs and tiled projection displays, very expensive and therefore not in routine use. (We use the term cave to denote both the original CAVE developed at the University of Illinois’ Electronic Visualization Laboratory and CAVE-style derivatives.) Evolving hardware and software technology may, however, enable IVR to become as ubiquitous as 3D graphics workstations—once exotic and very expensive—are today. Finally, we describe a research agenda, first for the technologies that enable IVR and then for the use of IVR for scientific visualization. Punctuating the discussion are sidebars giving examples of scientific IVR work currently under way at Brown University that addresses some of the research challenges, as well as other sidebars on data set size growth and IVR interaction metaphors.


Establishment of tumor-specific copy number alterations from plasma DNA of patients with cancer

With the increasing number of available predictive biomarkers, clinical management of cancer is becoming increasingly reliant on the accurate serial monitoring of tumor genotypes. We tested whether tumor-specific copy number changes can be inferred from the peripheral blood of patients with cancer. To this end, we determined the plasma DNA size distribution and the fraction of mutated plasma DNA fragments with deep sequencing and an ultrasensitive mutation-detection method, i.e., the Beads, Emulsion, Amplification, and Magnetics (BEAMing) assay. When analyzing the plasma DNA of 32 patients with Stage IV colorectal carcinoma, we found that a subset of the patients (34.4%) had a biphasic size distribution of plasma DNA fragments that was associated with increased circulating tumor cell numbers and elevated concentration of mutated plasma DNA fragments. In these cases, we were able to establish genome-wide tumor-specific copy number alterations directly from plasma DNA. Thus, we could analyze the current copy number status of the tumor genome, which was in some cases many years after diagnosis of the primary tumor. An unexpected finding was that not all patients with progressive metastatic disease appear to release tumor DNA into the circulation in measurable quantities. When we analyzed plasma DNA from 35 patients with metastatic breast cancer, we made similar observations suggesting that our approach may be applicable to a variety of tumor entities. This is the first description of such a biphasic distribution in a surprisingly high proportion of cancer patients which may have important implications for tumor diagnosis and monitoring.


SEISA: set expansion by iterative similarity aggregation

In this paper, we study the problem of expanding a set of given seed entities into a more complete set by discovering other entities that also belong to the same concept set. A typical example is to use "Canon" and "Nikon" as seed entities, and derive other entities (e.g., "Olympus") in the same concept set of camera brands. In order to discover such relevant entities, we exploit several web data sources, including lists extracted from web pages and user queries from a web search engine. While these web data are highly diverse with rich information that usually cover a wide range of the domains of interest, they tend to be very noisy. We observe that previously proposed random walk based approaches do not perform very well on these noisy data sources. Accordingly, we propose a new general framework based on iterative similarity aggregation, and present detailed experimental results to show that, when using general-purpose web data for set expansion, our approach outperforms previous techniques in terms of both precision and recall.


A simple intrusion detection method for controller area network

The Controller Area Network (CAN) is established as the main communication channel inside vehicles. CAN relies on frame broadcast to share data between different microcontrollers managing critical or comfort functions such as cruise control or air conditioning. CAN is distinguished by its simplicity, its real-time application compatibility and its low deployment cost. However, CAN major drawback is its lack of security support. That is, CAN fails to provide protections against attacks such as intrusion, denial of service or impersonation. We propose in this work a simple intrusion detection method for CAN. Our main idea is to make each microcontroller monitor the CAN bus to detect malicious frames.


One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction

Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.


Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals. That men are machines (whatever else they may be) has long been suspected; but not till our generation have men fairly felt in concrete just what wonderful psycho-neuro-physical mechanisms they are. William James (1893; 1842 – 1910) A. Fred, J. Filipe, and H. Gamboa (Eds.): BIOSTEC 2009, CCIS 52, pp. 21–47, 2010. c © Springer-Verlag Berlin Heidelberg 2010 22 E.L. van den Broek et al.


Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts, but no research has put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intraand inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 14.3 percent over the model using only the SKU’s own predictors. But of the improvements achieved, 88.1 percent of it comes from the intra-category information, and only 11.9 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.


Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs

In this paper, we present extensive advances in live-fly field experimentation capabilities of large numbers of fixed-wing aerial robots, and highlight both the enabling technologies as well as the challenges addressed in such largescale flight operations. We showcase results from recent field tests, including the autonomous launch, flight, and landing of 50 UAVs, which illuminate numerous operational lessons learned and generate rich multi-UAV datasets. We detail the design and open architecture of the testbed, which intentionally leverages low-cost and open-source components, aimed at promoting continued advances and alignment of multi-robot systems research and practice.


Model-based dynamic gait generation for a leg-wheel transformable robot

We report on the model-based approach to dynamic trotting and pronking gait generation for a leg-wheel transformable robot. The rolling spring-loaded inverted pendulum (R-SLIP) model served as the template for robot locomotion by programming the robot's motion according to the stable fixed-point trajectories of the model. Two strategies are developed to match the robot leg motions to the virtual model leg: First, using the two active degrees of freedom on each leg to simulate the passive spring effect of the R-SLIP model leg. Second, installing a torsion spring on the leg-wheel module to render the leg-wheel morphology identical to that of the model leg. This model-based approach to dynamic behavior generation for the robot is experimentally evaluated. The robot can successfully generate an R-SLIP-like stable pronking gait with a flight phase.


MobiCloud: A geo-distributed mobile cloud computing platform

In a cloud computing environment, users prefer to migrate their locally processing workloads onto the cloud where more resources with better performance can be expected. ProtoGENI [1] and PlanetLab [17] have further improved the current Internet-based resource outsourcing by allowing end users to construct a virtual network system through virtualization and programmable networking technologies. However, to the best of our knowledge, there is no such general service or resource provisioning platform designated for mobile devices. In this paper, we present a new design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform. The discussions of the system components, infrastructure, management, implementation flow, and service scenarios are followed by an example on how to experience the MobiCloud system.


Toward Enabling Automated Cognition and Decision-Making in Complex Cyber-Physical Systems

This article presents a framework for empowering automated cognition and decision making in complex cyber-physical systems (CPS). The key idea is for each cyber-physical component in the system to be able to construct from observations and actions a reduced model of its behavior and respond to external stimuli in the form of a (timed) probabilistic automaton such as a semi-Markov decision process. The reduced order behavioral models of various cyber-physical components can then be integrated into an optimization and decision-making module to determine the best actions for the components under different operating scenarios and cost / payoff functions by solving a bounded-rationality global decision-making problem.1


Gossip-based Causal Order Delivery Protocol Respecting Deadline Constraints in Publish/Subscribe Systems

Publish/subscribe systems based on gossip protocols are elastically to scale in and out and provides suitable consistency guarantees for data safety and high availability but, does not deal with end-to-end message delay and message order-based consistency. Especially in real-time collaborative applications, it is possible for the messages to take each a different time to arrive at end users. So, these applications should be based on P/S infrastructure including dealing with message deadlines and message ordering consistencies. Gossip communication is becoming one of the promising solutions for addressing P/S scalability problems in providing information propagation functionality by exploiting a mixture of diverse consistency options. In this paper, we present a new causal order protocol based on scalable P/S architecture for real-time collaborative applications in social web platforms to guarantee causally ordered message delivery, respecting deadline-constraints from brokers to subscribers. In the proposed protocol, every broker manages a 2-dimensional vector, representing its knowledge of the last message sent by each broker at a certain time. But, every broker disseminates a multicast message with a 1-dimensional vector, the time-stamped information that represents the maximum number of gossip rounds to subscribers because all messages disseminated by brokers have the same deadline as the maximum number of gossip rounds. Therefore, the proposed protocol for P/S based on gossiping results in very low communication overhead from brokers to subscribers in the context of respecting deadline-constraints.


Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method

This research work exposes the novel approach of analysis at existing works based on machine vision system for the identification of the visual symptoms of Cotton crop diseases, from RGB images. Diseases regions of cotton crops are revealed in digital pictures, Which were amended and segmented. In this work Proposed Enhanced PSO feature selection method adopts Skew divergence method and user features like Edge,Color,Texture variances to extract the features. Set of features was extracted from each of them. The extracted feature was input to the SVM, Back propagation neural network (BPN),Fuzzy with Edge CYMK color feature and GA feature selection. Tests were performed to identify the best classification model. It has been hypothesized that from the given characteristics of the images, there should be a subset of features more informative of the image domain.To test this hypothesis, three classification models were assessed via crossvalidation. To Evaluate its efficiency of six types of diseases have been accurately classified like Bacterial Blight, Fusariumwilt, Leaf Blight, Root rot, Micro Nurient, Verticilium Wilt.The Experimental results obtained show that the robust feature vector set which is an Enhancement of a feature extraction method (EPSO) has been afforded the performance assessment of this system. Keywords— SVM, BPN, Fuzzy, CMYK and Edge Features, Genetic Algorithm, Cotton leaf data sets., Enhance Particle swarm optimization, Skew divergences features. I. Introduct ion Data mining is the process of extracting patterns from the data and to transform raw data into information.. It is commonly utilized in a broad range of profiling practices, such as marketing, surveillance, fraud detection, agriculture environment and scientific discovery. Among other things, the most prominent features of data mining techniques are clustering and predictive. The benefits of data mining are its capability to gain deeper perceptive of the patterns using current available exposure capabilities . In this work categorization of the cotton leaf diseases are done using data mining with image processing techniques. The main target is to identify the disease in the leaf spot of the cotton crops. In this regard, It is discussed that about 80 to 90 percentage disease on the Cotton crops are on its leaf spot [1]. Consequently areas of interest is that identifying the leaf of the cotton tree rather than whole cotton plant the cotton leaf is mostly suffered from diseases like fungus, virus, Foliar leaf spot of cotton, Alternaria leaf spot of cotton. The machine visualization system right now is usually contained on computer, digital camera and application software. Various kinds of algorithms are incorporated in the application software. Image processing analysis is one significant method that helps to segment the image into objects and background. One of the key steps in image analysis is feature detection.Plant disease reduces the production and quality of food, fiber and biofuel crops. Image processing has fascinated many researchers in the area of pattern recognition and technique combined with data mining are applied to trace out the leaf spots from the of plant leaves, which helps in diagnosing the cotton leaf spot diseases accurately The image processing techniques are extensively applied to agricultural science, and it has great perspective, especially in the plant protection field, which ultimately leads to crop management. Image analysis can be applied for the following purposes: 1. To detect diseased leaf, stem, fruit, root 2. To enumerate affected area by disease. 3. To find the boundaries of the affected area. A moreover leaf spot is a major component of the crops [2]. The diseases can be easily recognized with the help of the polluted vicinity of the crop. Usually the leaves will naturally going to focus the impure part in a clear way which can be easily identified. Generally by naked eyes we can easily identify the infected region. So we can say the transform in the crop color is the imperative feature for the notification. When the health of the crop is in good stage then the color of the crop is dissimilar but as soon as the crop is going to affected by some harming pathogens, the color transforms automatically. Crop diseases have turned into a dilemma because it may cause a diminution in productivity [3]. In this research work described that goal of identifying foliar diseases in cotton plantations. The primary goal of the developed system has been to identify the existence of pathogens in cotton fillers. Once a disease is identified it has to be automatically classified through further processing of the corresponding image. A survey conducted in one of south zone particular Tamil Nadu at Andhiyur district. During the investigation congregate from the farmers' side gathered the suggestion about the cotton crop diseases details. In this work organized about the Section 2. Literature survey, Section 3. Motivation, Problem Analysis, Section 4. Material and Methods, Section 5. Comparative studies, Section 6. Result and Discussion, Section 7. Conclusion and Acknowledgment, References. II. Literature Survey The proposed work describes about the diagnosis of cotton leaves using various approaches suggesting that the various implementation ways as illustrated and discussed below. Hui Li et al., in the year 2011 has been implemented the Web-Based Intelligent Diagnosis System for Cotton Diseases Control system the author proposed a BP neural network for his system. International Journal of Scientific Engineering and Technology (ISSN : 2277-1581) Volume No.3, Issue No.1 pp : 22-30 1 Jan 2014 IJSET@2014 Page 23 A research scheme was designed for the system test, in which 80 samples, including 8 main species of diseases, 10 samples in each sort were included. The result showed the rate of correctness that system could identify the symptom was 89.5% in average, and the average running time for a diagnosis was 900ms [4].Yan Cheng Zhang et al., ( 2007 ) proposed fuzzy feature selection approach fuzzy curves (FC) and fuzzy surfaces (FS) – to select features and classification of cotton disease levels. [5]. Syed A. Health et al., In this research work discussed about the automated system that can identify the pest and disease affecting parts such as cotton leaf, boll or flower. In this work proposed a CMYK based image cleaning technique to remove shadows, hands and other impurities from images. The outcomes are tested over a database consisting of 600 images to classify the presented image as a leaf, boll or flower [6]. Bernardes A. A. et al., ( 2011) proposed method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while SVM has been used for classification. The image set of supposedly adulterated leaves was classified within one of the four other sub-classes, namely: MA, RA, AS, and NONE. Finally obtained were: 96.2% accuracy for the SA class, 97.1% accuracy for the MA class, 80% accuracy for the RA class, and 71.4% accuracy for the AS class [7]. Meunkaewjinda. A, et al., (2008). In his work the cotton leaf disease segmentation is performed using modified self organizing feature map with genetic algorithms for optimization and support vector machines for classification. Finally, the resulting segmented image is filtered by Gabor wavelet which allows the system to analyze leaf disease color features more efficiently [8]. Gulhane. V. A et al. (2011) This work described Self organizing feature map together with a back-propagation neural network is used to recognize the color of the image. Finally find out the classification of the cotton diseases [9]. Viraj A. Gulhane. e tal., (2012).This proposed work addresses about the disease analysis is possible for the cotton leaf disease recognition, the analysis of the various diseases present on the cotton leaves can be effectively detected in the early stage before it will injure the whole crops, initially we can be able to detect three types of diseases of the cotton leaves by the methodology of Eigen feature regularization and extraction technique. In this method 90% of detection of Red spot i.e. fungal disease is detected, it is most dangerous disease; it can highly affect the productivity of the cotton crop in more extent. And if it detects in early stage we can say that, we able to make better manufacture [10]. Qinghai He et al., (2013). In this paper the author described RGB color model, HIS color model, and YCbCr color model for extracting the injured image from cotton leaf images were developed.. The ratio of damage (γ) was chosen as feature to measure the degree of damage which caused by diseases or pests. In this work shows the comparison of the results obtained by implementing in different color models, the comparison of outcomes shows good accuracy in both color models and YCbCr color space is considered as the best color model for extracting the damaged image [11]. III. Motivation This research work address on solving various problems like boosting up the precision rates and diminish the error rates using the Proposed Enhanced PSO which make use of Skew divergences to calculate the features correctly from the image.This method increases the accuracy and reduce the information gap to farmers. Creating a complicated agricultural environment to support the farmers to easily identify the diseases and get control of it using the Pest Recommendation module.  Deployed the automatic disease classification processes using advanced Data mining and Image processing techniques.  Augment the profit and safety of the farmer’s life and diminish their burden.  Time consuming (less).  Enhance the economic status of our country. Problem Credentials  Ancient days crop disease identification process through the laboratory condition.  However, this requires continuou


Digital image integrity - a survey of protection and verification techniques

We are currently on a verge of a revolution in digital photography. Developments in computational imaging and adoption of artificial intelligence have spawned new editing techniques that give impressive results in astonishingly short timeframes. The advent of multi-sensor and multi-lens cameras will further challenge many existing integrity verification techniques. As a result, it will be necessary to re-evaluate our notion of image authenticity and look for new techniques that could work efficiently in this new reality. The goal of this paper is to thoroughly review existing techniques for protection and verification of digital image integrity. In contrast to other recent surveys, the discussion covers the most important developments both in active protection and in passive forensic analysis techniques. Existing approaches are analyzed with respect to their capabilities, fundamental limitations, and prospective attack vectors. Whenever possible, the discussion is supplemented with real operation examples and a list of available implementations. Finally, the paper reviews resources available in the research community, including public data-sets and commercial or open-source software. The paper concludes by discussing relevant developments in computational imaging and highlighting future challenges and open research problems.


Robust multivariate autoregression for anomaly detection in dynamic product ratings

User provided rating data about products and services is one key feature of websites such as Amazon, TripAdvisor, or Yelp. Since these ratings are rather static but might change over time, a temporal analysis of rating distributions provides deeper insights into the evolution of a products' quality. Given a time-series of rating distributions, in this work, we answer the following questions: (1) How to detect the base behavior of users regarding a product's evaluation over time? (2) How to detect points in time where the rating distribution differs from this base behavior, e.g., due to attacks or spontaneous changes in the product's quality? To achieve these goals, we model the base behavior of users regarding a product as a latent multivariate autoregressive process. This latent behavior is mixed with a sparse anomaly signal finally leading to the observed data. We propose an efficient algorithm solving our objective and we present interesting findings on various real world datasets.


Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications

This paper summarizes results on a recently introduced family of Datalog-based languages, called Datalog+/-, which is a new framework for tractable ontology querying, and for a variety of other applications. Datalog+/- extends plain Datalog by features such as existentially quantified rule heads and, at the same time, restricts the rule syntax so as to achieve decidability and tractability. In particular, we discuss three paradigms ensuring decidability: chase termination, guardedness, and stickiness.


Estimating 3D Egomotion from Perspective Image Sequence

This paper deals with the computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. First, the effects of 3-D camera rotation and translation upon the observed image are discussed and in particular the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2-D region of possible FOE-locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE-region is an explicit indicator for the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3-D scene structure and motion become possible. This paper concentrates on the aspects of computing the fuzzy FOE and shows the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle. Zndex Tenns-Autonomous mobile robot, dynamic scene analysis, fuzzy focus of expansion, motion analysis, passive navigation, sensor motion estimation.


Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.


Visibility in bad weather from a single image

Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. This requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. This method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather; second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. The method does not require the geometrical information of the input image, and is applicable for both color and gray images.


Gray and color image contrast enhancement by the curvelet transform

We present in this paper a new method for contrast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the Multiscale Retinex. In a range of examples, we use edge detection and segmentation, among other processing applications, to provide for quantitative comparative evaluation. Our findings are that curvelet based enhancement out-performs other enhancement methods on noisy images, but on noiseless or near noiseless images curvelet based enhancement is not remarkably better than wavelet based enhancement.


A novel algorithm for color constancy

Color constancy is the skill by which it is possible to tell the color of an object even under a colored light. I interpret the color of an object as its color under a fixed canonical light, rather than as a surface reflectance function. This leads to an analysis that shows two distinct sets of circumstances under which color constancy is possible. In this framework, color constancy requires estimating the illuminant under which the image was taken. The estimate is then used to choose one of a set of linear maps, which is applied to the image to yield a color descriptor at each point. This set of maps is computed in advance. The illuminant can be estimated using image measurements alone, because, given a number of weak assumptions detailed in the text, the color of the illuminant is constrained by the colors observed in the image. This constraint arises from the fact that surfaces can reflect no more light than is cast on them. For example, if one observes a patch that excites the red receptor strongly, the illuminant cannot have been deep blue. Two algorithms are possible using this constraint, corresponding to different assumptions about the world. The first algorithm, Crule will work for any surface reflectance. Crule corresponds to a form of coefficient rule, but obtains the coefficients by using constraints on illuminant color. The set of illuminants for which Crule will be successful depends strongly on the choice of photoreceptors: for narrowband photoreceptors, Crule will work in an unrestricted world. The second algorithm, Mwext, requires that both surface reflectances and illuminants be chosen from finite dimensional spaces; but under these restrictive conditions it can recover a large number of parameters in the illuminant, and is not an attractive model of human color constancy. Crule has been tested on real images of Mondriaans, and works well. I show results for Crule and for the Retinex algorithm of Land (Land 1971; Land 1983; Land 1985) operating on a number of real images. The experimental work shows that for good constancy, a color constancy system will need to adjust the gain of the receptors it employs in a fashion analagous to adaptation in humans.


Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.


Photographic tone reproduction for digital images

A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who must map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and is shown to produce good results for the wide variety of images that we have tested. Photographic Tone Reproduction for Digital Images Erik Reinhard University of Utah Michael Stark University of Utah Peter Shirley University of Utah Jim Ferwerda Cornell University


Mixture of experts for classification of gender, ethnic origin, and pose of human faces

In this paper we describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The FERET database allows us to demonstrate performance on hundreds or thousands of images. The mixture of experts is implemented using the "divide and conquer" modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the "gating network" components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel ("expert") for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification.


Building Optimal Question Answering System Automatically using Configuration Space Exploration (CSE) for QA4MRE 2013 Tasks

Different software systems for automatic question answering have been developed in recent years. Some systems perform well on specific domains, but may not be appropriate for other domains. As the complexity and scaling of such information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into a system so that one can achieve a desired or optimal level of performance on a given task. In this working notepaper, we present a generic framework that can be used for any machinereading task and automatically find the best configuration of algorithmic components as well as values of their corresponding parameters. Although we have designed the framework for all QA4MRE-2013 tasks (i.e. Main task, Biomedical about Alzheimer’s and Entrance Exam), our analysis will mostly focus on Biomedical about Alzheimer’s task. We introduce the Configuration Space Exploration (CSE) framework, an extension to the Unstructured Information Management Architecture (UIMA) which provides a general distributed solution for building and exploring possible configurations for any intelligent information system. For the Biomedial about Alzheimer’s task, CSE was used to generate more than 1000 different configurations from existing components; we selected the 3 best runs for submission. We achieved an average c@1 of 0.27; our highest score was 0.60 for reading-test-1, and our lowest was 0.0 for reading-test3. We further enhanced the system by introducing point-wise mutual information (PMI) scoring for answer ranking, which produced an average c@1 of 0.4025, with a highest score of 0.77 for reading test-1 and a lowest score of 0.2 for reading test-2.


Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this


First-Order Mixed Integer Linear Programming

Mixed integer linear programming (MILP) is a powerful representation often used to formulate decision-making problems under uncertainty. However, it lacks a natural mechanism to reason about objects, classes of objects, and relations. First-order logic (FOL), on the other hand, excels at reasoning about classes of objects, but lacks a rich representation of uncertainty. While representing propositional logic in MILP has been extensively explored, no theory exists yet for fully combining FOL with MILP. We propose a new representation, called first-order programming or FOP, which subsumes both FOL and MILP. We establish formal methods for reasoning about first order programs, including a sound and complete lifted inference procedure for integer first order programs. Since FOP can offer exponential savings in representation and proof size compared to FOL, and since representations and proofs are never significantly longer in FOP than in FOL, we anticipate that inference in FOP will be more tractable than inference in FOL for corresponding problems.


Speed and power scaling of SRAM's

Simple models for the delay, power, and area of a static random access memory (SRAM) are used to determine the optimal organizations for an SRAM and study the scaling of their speed and power with size and technology. The delay is found to increase by about one gate delay for every doubling of the RAM size up to 1 Mb, beyond which the interconnect delay becomes an increasingly significant fraction of the total delay. With technology scaling, the nonscaling of threshold mismatches in the sense amplifiers is found to significantly impact the total delay in generations of 0.1 /spl mu/m and below.


Prevention of Relay Attack Using NFC

Near Field Communication (NFC) is one of the emerging and promising technological developments for mobile phones and other contactless devices. NFC technologies allow two active devices embedded with chip transmit small pieces of data between each other via short range wireless connection and at low speed depending on the configurations. It offers low friction process because of the close range that the two NFC enabled devices can setup a connection. The combination of NFC with smart devices has led to development and range of NFC that includes data exchange, service discovery, connection, e-payment, and ticketing. With the help of a NFC enabled phone and card reader device, contactless card transaction can be performed. Security problems related to relay attack were analyzed and identified a proper solution to prevent the attack. In the proposed system, a Frame wait integer is used to check and verify data manipulation, by attaching the transacted data with a signed integer.


Completion and Reconstruction with Primitive Shapes

We consider the problem of reconstruction from incomplete point-clouds. To find a closed mesh the reconstruction is guided by a set of primitive shapes which has been detected on the input point-cloud (e.g. planes, cylinders etc.). With this guidance we not only continue the surrounding structure into the holes but also synthesize plausible edges and corners from the primitives’ intersections. To this end we give a surface energy functional that incorporates the primitive shapes in a guiding vector field. The discretized functional can be minimized with an efficient graphcut algorithm. A novel greedy optimization strategy is proposed to minimize the functional under the constraint that surface parts corresponding to a given primitive must be connected. From the primitive shapes our method can also reconstruct an idealized model that is suitable for use in a CAD system.


Improved DV-Hop localization algorithm for wireless sensor networks

Wireless sensor networks (WSN) are usually composed of a great number of randomly deployed nodes that communicate among themselves and gather information about the environment. In many applications, it is required to know geographical location of the sensor which detected an event. The existing localization algorithms can be classified into two categories: range-based and range-free. Range-based algorithms measure the actual distances between nodes, while range-free algorithms approximate the distance based on connectivity information. Range-free localization is cost-effective alternative to a more expensive range based approaches since there is no necessity for additional hardware. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is one of the range-free localization algorithms utilizing hop-distance estimation. In this paper, we propose an improvement of the range-free DV-Hop localization algorithm and present simulations results to evaluate it.


Syntactic methods for topic-independent authorship attribution

The efficacy of syntactic features for topic-independent authorship attribution is evaluated, taking a feature set of frequencies of words and punctuation marks as baseline. The features are ‘deep’ in the sense that they are derived by parsing the subject texts, in contrast to ‘shallow’ syntactic features for which a part-of-speech analysis is enough. The experiments are conducted on a corpus of novels written around the year 1900 by 20 different authors, and cover two tasks. In the first task, text samples are taken from books by one author, and the goal is to pair samples from the same book. In the second task, text samples are taken from several authors, but only one sample from each book, and the goal is to pair samples from the same author. In the first task, the baseline feature set outperformed the syntax-based feature set, but for the second task, the outcome was the opposite. This suggests that, compared to lexical features such as vocabulary and punctuation, syntactic features are more robust to changes in topic.


Object permanence in five-month-old infants

A new method was devised to test object permanence in young infants. Fivemonth-old infants were habituated to a screen that moved back and forth through a 180-degree arc, in the manner of a drawbridge. After infants reached habituation, a box was centered behind the screen. Infants were shown two test events: a possible event and an impossible event. In the possible event, the screen stopped when it reached the occluded box; in the impossible event, the screen moved through the space occupied by the box. The results indicated that infants looked reliably longer at the impossible than at the possible event. This finding suggested that infants (1) understood that the box continued to exist, in its same location, after it was occluded by the screen, and (2) expected the screen to stop against the occluded box and were surprised, or puzzled, when it failed to do so. A control experiment in which the box was placed next to the screen provided support for this interpretation of the results. Together, the results of these experiments indicate that, contrary to Piaget’s (1954) claims, infants as young as 5 months of age understand that objects continue to exist when occluded. The results also indicate that 5-month-old infants realize that solid objects do not move through the space occupied by other solid objects. *This research was supported by a grant from the National Institute of Health (HD-13248) to ESS. The data analysis was supported by a grant from the National Science Foundation (SES84-08626) to SW. While working on this research, RI3 was supported by fellowships from the Natural Sciences and Engineering Research Council of Canada and the Qu6bec Department of Education. We thank Judy Deloache and Bob Reeve, for their careful reading of the manuscript; Marty Banks, Susan Carey, and Paul Harris, for helpful comments on earlier versions of the manuscript; Wendy Smith Born, Sarah Mangelsdorf, and the members of the Infant Lab at the University of Pennsylvania, for their help with the data collection; and Dawn Iacobucci, for her help with the data analysis. **Reprint requests should be sent to RenCe Baillargeon, Psychology Department, University of Illinois at Urbana-Champaign, 603 East Daniel Street, Champaign, IL 61820, U.S.A. OOlO-0277/85/$5.90 0 Elsevier Sequoia/Printed in The Netherlands


Social Software in Higher Education: The Diversity of Applications and Their Contributions to Students' Learning Experiences


Risks and risk mitigation in global software development: A tertiary study

Context There is extensive interest in global software development (GSD) which has led to a large number of papers reporting on GSD. A number of systematic literature reviews (SLRs) have attempted to aggregate information from individual studies. Objective: We wish to investigate GSD SLR research with a focus on discovering what research has been conducted in the area and to determine if the SLRs furnish appropriate risk and risk mitigation advice to provide guidance to organizations involved with GSD. Method: We performed a broad automated search to identify GSD SLRs. Data extracted from each study included: (1) authors, their affiliation and publishing venue, (2) SLR quality, (3) research focus, (4) GSD risks, (5) risk mitigation strategies and, (6) for each SLR the number of primary studies reporting each risk and risk mitigation strategy. Results: We found a total of 37 papers reporting 24 unique GSD SLR studies. Major GSD topics covered include: (1) organizational environment, (2) project execution, (3) project planning and control and (4) project scope and requirements. We extracted 85 risks and 77 risk mitigation advice items and categorized them under four major headings: outsourcing rationale, software development, human resources, and project management. The largest group of risks was related to project management. GSD outsourcing rationale risks ranked highest in terms of primary study support but in many cases these risks were only identified by a single SLR. Conclusions: The focus of the GSD SLRs we identified is mapping the research rather than providing evidence-based guidance to industry. Empirical support for the majority of risks identified is moderate to low, both in terms of the number of SLRs identifying the risk, and in the number of primary studies providing empirical support. Risk mitigation advice is also limited, and empirical support for these items is low. 2013 Elsevier B.V. All rights reserved.


Exploring trajectory-driven local geographic topics in foursquare

The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.


Computational Thinking as a Computer Science Education Framework and the Related Effects on Gender Equity

I have recently completed my third year of study in the Educational Psychology and Educational Technology doctoral program at Michigan State University. I have successfully completed all of my coursework and comprehensive/qualifying exams. I will be proposing my dissertation study in the Fall semester of 2016 and expect to defend my dissertation in the Fall semester of 2017. My prior research has been focused on issues related to computational thinking, creativity, and computer science education. I am currently developing my literature review and honing my core research questions. I hope to subsequently develop my research methods and measures more fully, with plans to begin fieldwork in Fall of 2016.


A genomic perspective on protein families.

In order to extract the maximum amount of information from the rapidly accumulating genome sequences, all conserved genes need to be classified according to their homologous relationships. Comparison of proteins encoded in seven complete genomes from five major phylogenetic lineages and elucidation of consistent patterns of sequence similarities allowed the delineation of 720 clusters of orthologous groups (COGs). Each COG consists of individual orthologous proteins or orthologous sets of paralogs from at least three lineages. Orthologs typically have the same function, allowing transfer of functional information from one member to an entire COG. This relation automatically yields a number of functional predictions for poorly characterized genomes. The COGs comprise a framework for functional and evolutionary genome analysis.


Language Learning in Virtual Reality Environments: Past, Present, and Future

This study investigated the research trends in language learning in a virtual reality environment by conducting a content analysis of findings published in the literature from 2004 to 2013 in four top ranked computer-assisted language learning journals: Language Learning & Technology, CALICO Journal, Computer Assisted Language Learning, and ReCALL. Data from 29 articles were cross-analyzed in terms of research topics, technologies used, language learning settings, sample groups, and methodological approaches. It was found that the three most popular research topics for learners were interactive communication; behaviors, affections, and beliefs; and task-based instruction. However, the analysis results highlight the need for the inclusion of the impact of teacher. The data also revealed that more studies are utilizing triangulation of measurement processes to enable in-depth analysis. A trend of gathering data through informal learning procedures was also observed. This article concludes by highlighting particular fields related to VR in which further research is urgently needed.


Emotion classification using minimal EEG channels and frequency bands

In this research we propose to use EEG signal to classify two emotions (i.e., positive and negative) elicited by pictures. With power spectrum features, the accuracy rate of SVM classifier is about 85.41%. Considering each pair of channels and different frequency bands, it shows that frontal pairs of channels give a better result than the other area and high frequency bands give a better result than low frequency bands. Furthermore, we can reduce number of pairs of channels from 7 to 5 with almost the same accuracy and can cut low frequency bands in order to save computation time. All of these are beneficial to the development of emotion classification system using minimal EEG channels in real-time.


Crowd evacuation planning using Cartesian Genetic Programming and agent-based crowd modeling

This paper proposes a new evolutionary algorithm-based methodology for optimal crowd evacuation planning. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. Then, an evolutionary framework based on the Cartesian Genetic Programming algorithm and an agent-based crowd simulation model is developed to search for the optimal heuristic rule. By considering dynamic environment features to construct the heuristic rule and using multiple scenarios for training, the proposed methodology aims to find generic and efficient heuristic rules that perform well on different scenarios. The proposed methodology is applied to guide people's evacuation behaviors in six different scenarios. The simulation results demonstrate that the heuristic rule offered by the proposed method is effective to reduce the crowd evacuation time on different scenarios.


Learning Robust Visual-Semantic Embeddings

Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning in deep neural networks, we propose an end-to-end learning framework that is able to extract more robust multi-modal representations across domains. The proposed method combines representation learning models (i.e., auto-encoders) together with cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn joint embeddings for semantic and visual features. A novel technique of unsupervised-data adaptation inference is introduced to construct more comprehensive embeddings for both labeled and unlabeled data. We evaluate our method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with a wide range of applications, including zero and few-shot image recognition and retrieval, from inductive to transductive settings. Empirically, we show that our frame-work improves over the current state of the art on many of the considered tasks.


How computer games affect CS (and other) students' school performance

Compulsive game playing, especially of the role-playing variety, risks failing grades and withdrawal of financial support from tuition-paying parents.


eSports: collaborative and synchronous video annotation system in grid computing environment

We designed eSports - a collaborative and synchronous video annotation platform, which is to be used in Internet scale cross-platform grid computing environment to facilitate computer supported cooperative work (CSCW) in education settings such as distance sport coaching, distance classroom etc. Different from traditional multimedia annotation systems, eSports provides the capabilities to collaboratively and synchronously play and archive real time live video, to take snapshots, to annotate video snapshots using whiteboard and to play back the video annotations synchronized with original video streams. eSports is designed based on the grid based collaboration paradigm $the shared event model using NaradaBrokering, which is a publish/subscribe based distributed message passing and event notification system. In addition to elaborate the design and implementation of eSports, we analyze the potential use cases of eSports under different education settings. We believed that eSports is very useful to improve the online collaborative coaching and education.


The Role of Flow Experience in Cyber-Game Addiction

Consumer habit, an important key to repetitive consumption, is an interesting yet puzzling phenomenon. Sometimes this consumption becomes obsessive--consumers will continue to act a certain way even when they feel it is not in their best interests. However, not all consumers develop such addictions. This study uses cyber-game addiction syndrome as an analogue to trace the possible causes of consumer addiction. Results from structure equation modeling show that repetition of favorite activities has a moderate effect upon addiction, which is in line with the assertion of rational addiction theory. However, flow experience--the emotional state embracing perceptional distortion and enjoyment--shows a much stronger impact on addiction. This suggests that consumers who have experienced flow are more likely to be addicted.


Being There: The Subjective Experience of Presence


Analysis of Bayesian Classification based Approaches for Android Malware Detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, in this paper we develop and analyze proactive Machine Learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification based solutions for detecting unknown Android malware.


Problems of Reliability and Validity in Ethnographic Research

Although problems of reliability and validity have been explored thoroughly by experimenters and other quantitative researchers, their treatment by ethnographers has been sporadic and haphazard This article analyzes these constructs as defined and addressed by ethnographers. Issues of reliability and validity in ethnographic design are compared to their counterparts in experimental design. Threats to the credibility of ethnographic research are summarized and categorized from field study methodology. Strategies intended to enhance credibility are incorporated throughout the investigative process: study design, data collection, data analysis, and presentation of findings. Common approaches to resolving various categories of contamination are illustrated from the current literature in educational ethnography.


Dairy products and colorectal cancer risk: a systematic review and meta-analysis of cohort studies.

BACKGROUND
Previous studies of the association between intake of dairy products and colorectal cancer risk have indicated an inverse association with milk, however, the evidence for cheese or other dairy products is inconsistent.

METHODS
We conducted a systematic review and meta-analysis to clarify the shape of the dose-response relationship between dairy products and colorectal cancer risk. We searched the PubMed database for prospective studies published up to May 2010. Summary relative risks (RRs) were estimated using a random effects model.

RESULTS
Nineteen cohort studies were included. The summary RR was 0.83 (95% CI [confidence interval]: 0.78-0.88, I2=25%) per 400 g/day of total dairy products, 0.91 (95% CI: 0.85-0.94, I2=0%) per 200 g/day of milk intake and 0.96 (95% CI: 0.83-1.12, I2=28%) per 50 g/day of cheese. Inverse associations were observed in both men and women but were restricted to colon cancer. There was evidence of a nonlinear association between milk and total dairy products and colorectal cancer risk, P<0.001, and the inverse associations appeared to be the strongest at the higher range of intake.

CONCLUSION
This meta-analysis shows that milk and total dairy products, but not cheese or other dairy products, are associated with a reduction in colorectal cancer risk.


Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.


Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage

Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports. We observe that when the type set spans several domains, detection of entity mention becomes a limitation for supervised learning models. The primary reason being lack of dataset where entity boundaries are properly annotated while covering a large spectrum of entity types. Our work directly addresses this issue. We propose Heuristics Allied with Distant Supervision (HAnDS) framework to automatically construct a quality dataset suitable for the FgER task. HAnDS framework exploits the high interlink among Wikipedia and Freebase in a pipelined manner, reducing annotation errors introduced by naively using distant supervision approach. Using HAnDS framework, we create two datasets, one suitable for building FgER systems recognizing up to 118 entity types based on the FIGER type hierarchy and another for up to 1115 entity types based on the TypeNet hierarchy. Our extensive empirical experimentation warrants the quality of the generated datasets. Along with this, we also provide a manually annotated dataset for benchmarking FgER systems.


Neuronal correlates of a perceptual decision

THE relationship between neuronal activity and psychophysical judgement has long been of interest to students of sensory processing. Previous analyses of this problem have compared the performance of human or animal observers in detection or discrimination tasks with the signals carried by individual neurons, but have been hampered because neuronal and perceptual data were not obtained at the same time and under the same conditions1–4. We have now measured the performance of monkeys and of visual cortical neurons while the animals performed a psychophysical task well matched to the properties of the neurons under study. Here we report that the reliability and sensitivity of most neurons on this task equalled or exceeded that of the monkeys. We therefore suggest that under our conditions, psychophysical judgements could be based on the activity of a relatively small number of neurons.


Clinical and microbiologic characteristics of vulvovaginitis in Korean prepubertal girls, 2009–2014: a single center experience

OBJECTIVE
To update information on the clinical and microbiologic characteristics of pediatric vulvovaginitis in Korean prepubertal girls.

METHODS
A total of 120 girls (aged 0 to 9 years) with culture-confirmed pediatric vulvovaginitis, diagnosed between 2009 and 2014, were enrolled in the study. The epidemiologic and microbiologic characteristics, and clinical outcomes were assessed. Patients with sexual precocity, as well as those who were referred for suspected sexual abuse, were excluded.

RESULTS
Girls aged 4 to 6 years were at the highest risk of pediatric vulvovaginitis. Seasonal distribution indicated obvious peaks in summer and winter. Of the 120 subjects, specific pathogens were identified in the genital specimens in only 20 cases (16.7%). Streptococcus pyogenes (n=12, 60%) was the leading cause of specific vulvovaginitis. Haemophilus influenzae was isolated in one patient. No cases presented with enteric pathogens, such as Shigella or Yersinia. A history of recent upper respiratory tract infection, swimming, and bubble bath use was reported in 37.5%, 15.8%, and 10.0% of patients, respectively. Recent upper respiratory tract infection was not significantly correlated with the detection of respiratory pathogens in genital specimens (P>0.05). Of 104 patients who underwent perineal hygienic care, 80 (76.9%) showed improvement of symptoms without antibiotic treatment. Furthermore, the efficacy of hygienic care was not significantly different between patients with or without specific pathogens (P>0.05).

CONCLUSION
Specific pathogens were only found in 16.7% of pediatric vulvovaginitis cases. Our results indicate an excellent outcome with hygienic care, irrespective of the presence of specific pathogens.


HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots

The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load-data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer, that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the ACID properties of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.


A Ka-band asymmetrical stacked-FET MMIC Doherty power amplifier

We present a stacked-FET monolithic millimeter-wave (mmW) integrated circuit Doherty power amplifier (DPA). The DPA employs a novel asymmetrical stack gate bias to achieve high power and high efficiency at 6-dB power back-off (PBO). The circuit is fabricated in a 0.15-µm enhancement mode (E-mode) Gallium Arsenide (GaAs) process. Experimental results demonstrate output power at 1-dB gain compression (P1dB) of 28.2 dBm, peak power added efficiency (PAE) of 37% and PAE at 6-dB PBO of 27% at 28 GHz. Measured small signal gain is 15 dB while the 3-dB bandwidth covers from 25.5 to 29.5 GHz. Using digital predistortion (DPD) with a 20 MHz 64 QAM modulated signal, an adjacent channel power ratio (ACPR) of −46 dBc has been observed.


Autoregressive Attention for Parallel Sequence Modeling

We introduce an autoregressive attention mechanism for parallelizable characterlevel sequence modeling. We use this method to augment a neural model consisting of blocks of causal convolutional layers connected by highway network skip connections. We denote the models with and without the proposed attention mechanism respectively as Highway Causal Convolution (Causal Conv) and Autoregressive-attention Causal Convolution (ARA-Conv). The autoregressive attention mechanism crucially maintains causality in the decoder, allowing for parallel implementation. We demonstrate that these models, compared to their recurrent counterparts, enable fast and accurate learning in character-level NLP tasks. In particular, these models outperform recurrent neural network models in natural language correction and language modeling tasks, and run in a fraction of the time.


Benchmarks as Limits to Arbitrage : Understanding the Low Volatility Anomaly ∗

Over the past 41 years, high volatility and high beta stocks have substantially underperformed low volatility and low beta stocks in U.S. markets. We propose an explanation that combines the average investor's preference for risk and the typical institutional investor’s mandate to maximize the ratio of excess returns and tracking error relative to a fixed benchmark (the information ratio) without resorting to leverage. Models of delegated asset management show that such mandates discourage arbitrage activity in both high alpha, low beta stocks and low alpha, high beta stocks. This explanation is consistent with several aspects of the low volatility anomaly including why it has strengthened in recent years even as institutional investors have become more dominant.