NAIDS design using ChiMIC-KGS

The tasks of IDS is to detect activities in the form of intrusion by unauthorized parties which attempt to gain access to information unlawfully in a network environment. Changes in intrusion strategies affect the behavior of networks where the representation of network traffic data changes over time. The nature of the network data has high volume and dimension and contains redundancy. This study proposes the use of ChiMIC as a method to reduce redundancy features and select the relevant features to be used in detecting anomalies on the network, where KGS is used as an anomaly detection engine. Both approach are used to build NAIDS that has capabilities to detect the emergence of new attacks over time.


Supply Chain Management ( SCM ) and Organizational Key Factors for Successful Implementation of Enterprise Resource Planning ( ERP ) Systems

This paper reports some findings of an on-going research into ERP implementation issues. A part of the research, which this paper reports, consists of reviewing several cases of successful implementations of ERP systems. The analysis of all these cases revealed that the critical factors for successful implementation of ERP systems fall into two main categories: technological and organizational factors. However, the organizational factors seem to be more important than the technological ones as far as the successful implementation of ERP systems under SCM is concerned.


Semantically Smooth Knowledge Graph Embedding

This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into lowdimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further discovering the intrinsic geometric structure of the embedding space, we propose Semantically Smooth Embedding (SSE). The key idea of SSE is to take full advantage of additional semantic information and enforce the embedding space to be semantically smooth, i.e., entities belonging to the same semantic category will lie close to each other in the embedding space. Two manifold learning algorithms Laplacian Eigenmaps and Locally Linear Embedding are used to model the smoothness assumption. Both are formulated as geometrically based regularization terms to constrain the embedding task. We empirically evaluate SSE in two benchmark tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods. Furthermore, SSE is a general framework. The smoothness assumption can be imposed to a wide variety of embedding models, and it can also be constructed using other information besides entities’ semantic categories.


Automatic Chinese food identification and quantity estimation

Computer-aided food identification and quantity estimation have caught more attention in recent years because of the growing concern of our health. The identification problem is usually defined as an image categorization or classification problem and several researches have been proposed. In this paper, we address the issues of feature descriptors in the food identification problem and introduce a preliminary approach for the quantity estimation using depth information. Sparse coding is utilized in the SIFT and Local binary pattern feature descriptors, and these features combined with gabor and color features are used to represent food items. A multi-label SVM classifier is trained for each feature, and these classifiers are combined with multi-class Adaboost algorithm. For evaluation, 50 categories of worldwide food are used, and each category contains 100 photographs from different sources, such as manually taken or from Internet web albums. An overall accuracy of 68.3% is achieved, and success at top-N candidates achieved 80.6%, 84.8%, and 90.9% accuracy accordingly when N equals 2, 3, and 5, thus making mobile application practical. The experimental results show that the proposed methods greatly improve the performance of original SIFT and LBP feature descriptors. On the other hand, for quantity estimation using depth information, a straight forward method is proposed for certain food, while transparent food ingredients such as pure water and cooked rice are temporarily excluded.


Implementation and Performance Analysis of Firewall on Open vSwitch

Software Defined Networking (SDN) is a current research trend that follows the ideology of physical separation of the control and data plane of the forwarding devices. SDN mainly advocates with two types of devices: (1) Controllers, that implement the control plane and (2) Switches, that perform the data plane operations. OpenFlow protocol (OFP) is the current standard through which controllers and switches can communicate with each other. Using OpenFlow, SDN controllers can manage forwarding behaviors of SDN switches by managing Flow Table entries. Switches use these low-level Flow Table entries to forward packets to appropriate hosts. Firewalls are integral part of today’s networks. We can’t imagine our network without a Firewall which protects our network from potential threats. As SDN is getting pace in replacing traditional architecture, it would be very interesting to see how much security features can be provided by OpenFlow-enabled switches. Hence, it will be very important to see if SDN, on the top of OpenFlow, can efficiently implement Firewalls and provides support for an advanced feature like connection tracking. The task is straightforward: Controller will add Flow Table entries on switches based upon Firewall rules. Such way, we can enhance packet-processing by providing security. In this Document, one strategy for implementing Firewall on SDN is presented. We can write some controller applications that work as Firewall and inspect incoming packets against the Firewall rules. These applications are also able to implement connection tracking mechanism. As SDN devices for the experiments, we selected Ryu controller and Open vSwitch. Initially, such applications are tested on local machine with small Firewall ruleset. Later, they are tested with real-world traffic and comparatively large Firewall ruleset. The testing results present that such strategy can be used as a first step in implementing security features (including connection tracking) in SDN environment.


Predicting and analyzing secondary education placement-test scores: A data mining approach

Understanding the factors that lead to success (or failure) of students at placement tests is an interesting and challenging problem. Since the centralized placement tests and future academic achievements are considered to be related concepts, analysis of the success factors behind placement tests may help understand and potentially improve academic achievement. In this study using a large and feature rich dataset from Secondary Education Transition System in Turkey we developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models we identified the most important predictors. The results showed that C5 decision tree algorithm is the best predictor with 95% accuracy on hold-out sample, followed by support vector machines (with an accuracy of 91%) and artificial neural networks (with an accuracy of 89%). Logistic regression models came out to be the least accurate of the four with and overall accuracy of 82%. The sensitivity analysis revealed that previous test experience, whether a student has a scholarship, student’s number of siblings, previous years’ grade point average are among the most important predictors of the placement test scores. 2012 Elsevier Ltd. All rights reserved.


Introduction to Data Mining

an introduction to data mining san jose state university introduction to data mining university of florida introduction to data mining exinfm introduction to data mining introduction to data mining and knowledge discovery lecture notes for chapter 3 introduction to data mining introduction to data mining introduction to data mining pennsylvania state university introduction to data mining tu/e introduction to data mining sas support session


A Data Mining view on Class Room Teaching Language

From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.


Gray level image processing using contrast enhancement and watershed segmentation with quantitative evaluation

Both image enhancement and image segmentation are most practical approaches among virtually all automated image recognition systems. Feature extraction and recognition have numerous applications on telecommunication, weather forecasting, environment exploration and medical diagnosis. The adaptive image contrast stretching is a typical image enhancement approach and watershed segmentation is a typical image segmentation approach. Under conditions of an improper or disturbed illumination, the adaptive contrast stretching should be conducted, which adapts to intensity distributions. Watershed segmentation is a feasible approach to separate different objects automatically, where watershed lines separate the catchment basins. The erosion and dilation operations are essential procedures involved in watershed segmentation. To avoid over-segmentation, the markers for foreground and background can be selected accordingly. Quantitative measures (gray level energy, discrete entropy, relative entropy and mutual information) are proposed to evaluate the actual improvement via two techniques. These methodologies can be easily expanded to many other image processing approaches.


Grassmannian beamforming for multiple-input multiple-output wireless systems

Transmit and receive beamforming is an attractive, low-complexity technique for exploiting the significant diversity that is available in multiple-input and multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector which is difficult to realize in practice. In this paper, we propose a quantized maximum signal-to-noise ratio beamforming technique where the receiver only sends the label of the best weight vector in a predetermined codebook to the transmitter. We develop optimal codebooks for i.i.d. Rayleigh fading matrix channels. We derive the distribution of the optimal transmit weight vector and use this result to bound the SNR degradation as a function of the quantization. A codebook design criterion is proposed by exploiting the connections between the quantization problem and Grassmannian line packing. The design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the maximum distortion with the resulting Grassmannian codebooks follow naturally from the nature of the code. A proof is given that any system using an overcomplete codebook for transmit diversity and maximum ratio combining obtains a diversity on the order of the product of the number of transmit and receive antennas. Bounds on the loss in capacity due to quantization are derived. Monte Carlo simulations are presented that compare the symbol error probability for different quantization strategies.


Efficient Memory Disaggregation with Infiniswap

Memory-intensive applications suffer large performance loss when their working sets do not fully fit in memory. Yet, they cannot leverage otherwise unused remote memory when paging out to disks even in the presence of large imbalance in memory utilizations across a cluster. Existing proposals for memory disaggregation call for new architectures, new hardware designs, and/or new programming models, making them infeasible. This paper describes the design and implementation of INFINISWAP, a remote memory paging system designed specifically for an RDMA network. INFINISWAP opportunistically harvests and transparently exposes unused memory to unmodified applications by dividing the swap space of each machine into many slabs and distributing them across many machines’ remote memory. Because one-sided RDMA operations bypass remote CPUs, INFINISWAP leverages the power of many choices to perform decentralized slab placements and evictions. We have implemented and deployed INFINISWAP on an RDMA cluster without any modifications to user applications or the OS and evaluated its effectiveness using multiple workloads running on unmodified VoltDB, Memcached, PowerGraph, GraphX, and Apache Spark. Using INFINISWAP, throughputs of these applications improve between 4× (0.94×) to 15.4× (7.8×) over disk (Mellanox nbdX), and median and tail latencies between 5.4× (2×) and 61× (2.3×). INFINISWAP achieves these with negligible remote CPU usage, whereas nbdX becomes CPU-bound. INFINISWAP increases the overall memory utilization of a cluster and works well at scale.


Hate Source : White Supremacist Hate Groups and Hate Crime

The relationship between hate group activity and hate crime is theoretically ambiguous. Hate groups may incite criminal behavior in support of their beliefs. On the other hand, hate groups may reduce hate crime by serving as a forum for members to verbally vent their frustrations or as protection from future biased violence. I nd that the presence of an active white supremacist hate group chapter is associated with an 18.7 percent higher hate crime rate. White supremacist groups are not associated with the level of anti-white hate crimes committed by non-whites, nor do they form in expectation of future hate crimes by non-whites. JEL Codes: K14, J15, D71


Relating Newcomer Personality to Survival and Activity in Recommender Systems

In this work, we explore the degree to which personality information can be used to model newcomer retention, investment, intensity of engagement, and distribution of activity in a recommender community. Prior work shows that Big-Five Personality traits can explain variation in user behavior in other contexts. Building on this, we carry out and report on an analysis of 1008 MovieLens users with identified personality profiles. We find that Introverts and low Agreeableness users are more likely to survive into the second and subsequent sessions compared to their respective counterparts; Introverts and low Conscientiousness users are a significantly more active population compared to their respective counterparts; High Openness and High Neuroticism users contribute (tag) significantly more compared to their counterparts, but their counterparts consume (browse and bookmark) more; and low Agreeableness users are more likely to rate whereas high Agreeableness users are more likely to tag. These results show how modeling newcomer behavior from user personality can be useful for recommender systems designers as they customize the system to guide people towards tasks that need to be done or tasks the users will find rewarding and also decide which users to invest retention efforts in.


Stealthy Denial of Service Strategy in Cloud Computing

The success of the cloud computing paradigm is due to its on-demand, self-service, and pay-by-use nature. According to this paradigm, the effects of Denial of Service (DoS) attacks involve not only the quality of the delivered service, but also the service maintenance costs in terms of resource consumption. Specifically, the longer the detection delay is, the higher the costs to be incurred. Therefore, a particular attention has to be paid for stealthy DoS attacks. They aim at minimizing their visibility, and at the same time, they can be as harmful as the brute-force attacks. They are sophisticated attacks tailored to leverage the worst-case performance of the target system through specific periodic, pulsing, and low-rate traffic patterns. In this paper, we propose a strategy to orchestrate stealthy attack patterns, which exhibit a slowly-increasing-intensity trend designed to inflict the maximum financial cost to the cloud customer, while respecting the job size and the service arrival rate imposed by the detection mechanisms. We describe both how to apply the proposed strategy, and its effects on the target system deployed in the cloud.


Multibiometric Cryptosystems Based on Feature-Level Fusion

Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.


A virtual translation technique to improve current regulator for salient-pole AC machines

This paper presents a synchronous frame current regulator for salient-pole AC machines, which is insensitive to both parameters and synchronous frequencies. The transformation from stationary to synchronous frame is known to produce a frame-dependent cross-coupling term, which results in degradation in current response at high speeds and is represented as an asymmetric pole in the complex vector form of such systems. The asymmetric pole close to right half plane (RHP) in the complex vector s-domain cannot be perfectly canceled by parameter-based design of PI controllers due to parameter variation. Complex vector design synchronous frame PI current regulator with a virtually translated asymmetric pole is shown in this paper to reduce the parameter sensitivity and to improve disturbance rejection. The current regulator with the virtually translated pole have been analyzed in complex vector form for nonsalient-pole AC machine and in scalar form for salient-pole AC machines. It has been shown to yield comparable performances in both cases. This paper includes experimental verification of the performance of the proposed current regulator for an IPMSM.


News-based trading strategies

The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decisionmaking based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system.


Handcrafted features with convolutional neural networks for detection of tumor cells in histology images

Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neural networks (CNNs) to detect cells in H&E stained images. However, all such methods use some form of raw pixel intensities as input and rely on the CNN to learn the deep features. In this work, we extend a recently proposed spatially constrained CNN (SC-CNN) by proposing features that capture texture characteristics and show that although CNN produces good results on automatically learned features, it can perform better if the input consists of a combination of handcrafted features and the raw data. The handcrafted features are computed through the scattering transform which gives non-linear invariant texture features. The combination of handcrafted features with raw data produces sharp proximity maps and better detection results than the results of raw intensities with a similar kind of CNN architecture.


Inverse procedural modeling of 3D models for virtual worlds

This course presents a collection of state-of-the-art approaches for modeling and editing of 3D models for virtual worlds, simulations, and entertainment, in addition to real-world applications. The first contribution of this course is a coherent review of inverse procedural modeling (IPM) (i.e., proceduralization of provided 3D content). We describe different formulations of the problem as well as solutions based on those formulations. We show that although the IPM framework seems under-constrained, the state-of-the-art solutions actually use simple analogies to convert the problem into a set of fundamental computer science problems, which are then solved by corresponding algorithms or optimizations. The second contribution includes a description and categorization of results and applications of the IPM frameworks. Moreover, a substantial part of the course is devoted to summarizing different domain IPM frameworks for practical content generation in modeling and animation.


Lack of sleep affects the evaluation of emotional stimuli

Sleep deprivation (SD) negatively affects various cognitive performances, but surprisingly evidence about a specific impact of sleep loss on subjective evaluation of emotional stimuli remains sparse. In the present study, we assessed the effect of SD on the emotional rating of standardized visual stimuli selected from the International Affective Picture System. Forty university students were assigned to the sleep group (n=20), tested before and after one night of undisturbed sleep at home, or to the deprivation group, tested before and after one night of total SD. One-hundred and eighty pictures (90 test, 90 retest) were selected and categorized as pleasant, neutral and unpleasant. Participants were asked to judge their emotional reactions while viewing pictures by means of the Self-Assessment Manikin. Subjective mood ratings were also obtained by means of Visual Analog Scales. No significant effect of SD was observed on the evaluation of pleasant and unpleasant stimuli. On the contrary, SD subjects perceived the neutral pictures more negatively and showed an increase of negative mood and a decrease of subjective alertness compared to non-deprived subjects. Finally, an analysis of covariance on mean valence ratings of neutral pictures using negative mood as covariate confirmed the effect of SD. Our results indicate that sleep is involved in regulating emotional evaluation. The emotional labeling of neutral stimuli biased toward negative responses was not mediated by the increase of negative mood. This effect can be interpreted as an adaptive reaction supporting the "better safe than sorry" principle. It may also have applied implications for healthcare workers, military and law-enforcement personnel.


A MODERN TAKE

We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model complexity grows, variance increases. Classical bounds in statistical learning theory point to the number of parameters in a model as a measure of model complexity, which means the tradeoff would indicate that variance increases with the size of neural networks. However, we empirically find that variance due to training set sampling is roughly constant (with both width and depth) in practice. Variance caused by the non-convexity of the loss landscape is different. We find that it decreases with width and increases with depth, in our setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings for width. We view bias-variance as a useful lens to study generalization through and encourage further theoretical explanation from this perspective.


A Foundation for the Study of IT Effects: A New Look at DeSanctis and Poole's Concepts of Structural Features and Spirit

Special Issue


Visibility estimation and joint inpainting of lidar depth maps

This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.


Review of Electromagnetic Techniques for Breast Cancer Detection

Breast cancer is anticipated to be responsible for almost 40,000 deaths in the USA in 2011. The current clinical detection techniques suffer from limitations which motivated researchers to investigate alternative modalities for the early detection of breast cancer. This paper focuses on reviewing the main electromagnetic techniques for breast cancer detection. More specifically, this work reviews the cutting edge research in microwave imaging, electrical impedance tomography, diffuse optical tomography, microwave radiometry, biomagnetic detection, biopotential detection, and magnetic resonance imaging (MRI). The goal of this paper is to provide biomedical researchers with an in-depth review that includes all main electromagnetic techniques in the literature and the latest progress in each of these techniques.


Mapping CMMI Project Management Process Areas to SCRUM Practices

Over the past years, the capability maturity model (CMM) and capability maturity model integration (CMMI) have been broadly used for assessing organizational maturity and process capability throughout the world. However, the rapid pace of change in information technology has caused increasing frustration to the heavyweight plans, specifications, and other documentation imposed by contractual inertia and maturity model compliance criteria. In light of that, agile methodologies have been adopted to tackle this challenge. The aim of our paper is to present mapping between CMMI and one of these methodologies, Scrum. It shows how Scrum addresses the Project Management Process Areas of CMMI. This is useful for organizations that have their plan-driven process based on the CMMI model and are planning to improve its processes toward agility or to help organizations to define a new project management framework based on both CMMI and Scrum practices.


DepSky: Dependable and Secure Storage in a Cloud-of-Clouds

The increasing popularity of cloud storage services has lead companies that handle critical data to think about using these services for their storage needs. Medical record databases, large biomedical datasets, historical information about power systems and financial data are some examples of critical data that could be moved to the cloud. However, the reliability and security of data stored in the cloud still remain major concerns. In this work we present DepSky, a system that improves the availability, integrity, and confidentiality of information stored in the cloud through the encryption, encoding, and replication of the data on diverse clouds that form a cloud-of-clouds. We deployed our system using four commercial clouds and used PlanetLab to run clients accessing the service from different countries. We observed that our protocols improved the perceived availability, and in most cases, the access latency, when compared with cloud providers individually. Moreover, the monetary costs of using DepSky in this scenario is at most twice the cost of using a single cloud, which is optimal and seems to be a reasonable cost, given the benefits.


A descent modified

In this paper, we propose a modified Polak–Ribière–Polyak (PRP) conjugate gradient method. An attractive property of the proposed method is that the direction generated by the method is always a descent direction for the objective function. This property is independent of the line search used. Moreover, if exact line search is used, the method reduces to the ordinary PRP method. Under appropriate conditions, we show that the modified PRP method with Armijo-type line search is globally convergent. We also present extensive preliminary numerical experiments to show the efficiency of the proposed method.


Secure Border Gateway Protocol (Secure-BGP)

The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet’s routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This document describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture with other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.


Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases

In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating “smart data” which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.


Series No . 16 Profiting from Innovation in the Digital Economy : Standards , Complementary Assets , and Business Models In the Wireless World


10 user interface elements for mobile learning application development

Mobile learning application is a new fashionable trend. More than a thousand learning applications have been designed and developed. Therefore, the focus on mobile learning applications needs to be refined and improved in order to facilitate users' interaction and response towards the learning process. To develop an effective mobile learning application, one should start from the User Interface (UI) because an effective UI can play a role in constantly making the user focus on an object and subject. The purpose of this study is to investigate and identify the best UI elements to use in mobile learning application development. Four existing UI guidelines and 12 selected learning applications were analysed, and we identified 10 UI Elements in order to develop the next mobile learning applications. All the 10 elements are described accordingly and they have implications for those designing and implementing UI in mobile learning applications.


Automated Accessibility Testing of Mobile Apps

It is important to make mobile apps accessible, so as not to exclude users with common disabilities such as blindness, low vision, or color blindness. Even when developers are aware of these accessibility needs, the lack of tool support makes the development and assessment of accessible apps challenging. Some accessibility properties can be checked statically, but user interface widgets are often created dynamically and are not amenable to static checking. Some accessibility checking frameworks analyze accessibility properties at runtime, but have to rely on existing thorough test suites. In this paper, we introduce the idea of using automated test generation to explore the accessibility of mobile apps. We present the MATE tool (Mobile Accessibility Testing), which automatically explores apps while applying different checks for accessibility issues related to visual impairment. For each issue, MATE generates a detailed report that supports the developer in fixing the issue. Experiments on a sample of 73 apps demonstrate that MATE detects more basic accessibility problems than static analysis, and many additional types of accessibility problems that cannot be detected statically at all. Comparison with existing accessibility testing frameworks demonstrates that the independence of an existing test suite leads to the identification of many more accessibility problems. Even when enabling Android's assistive features like contrast enhancement, MATE can still find many accessibility issues.


Examining the Testing Effect with Open-and Closed-Book Tests

Two experiments examined the testing effect with open-book tests, in which students view notes and textbooks while taking the test, and closed-book tests, in which students take the test without viewing notes or textbooks. Subjects studied prose passages and then restudied or took an openor closed-book test. Taking either kind of test, with feedback, enhanced long-term retention relative to conditions in which subjects restudied material or took a test without feedback. Open-book testing led to better initial performance than closed-book testing, but this benefit did not persist and both types of testing produced equivalent retention on a delayed test. Subjects predicted they would recall more after repeated studying, even though testing enhanced long-term retention more than restudying. These experiments demonstrate that the testing effect occurs with both openand closedbook tests, and that subjects fail to predict the effectiveness of testing relative to studying in enhancing later recall. Copyright # 2007 John Wiley & Sons, Ltd.


The Power of Testing Memory: Basic Research and Implications for Educational Practice.

A powerful way of improving one's memory for material is to be tested on that material. Tests enhance later retention more than additional study of the material, even when tests are given without feedback. This surprising phenomenon is called the testing effect, and although it has been studied by cognitive psychologists sporadically over the years, today there is a renewed effort to learn why testing is effective and to apply testing in educational settings. In this article, we selectively review laboratory studies that reveal the power of testing in improving retention and then turn to studies that demonstrate the basic effects in educational settings. We also consider the related concepts of dynamic testing and formative assessment as other means of using tests to improve learning. Finally, we consider some negative consequences of testing that may occur in certain circumstances, though these negative effects are often small and do not cancel out the large positive effects of testing. Frequent testing in the classroom may boost educational achievement at all levels of education.


METAMEMORY : A THEORETICAL FRAMEWORK AND NEW FINDINGS

Although there has been excellent research by many investigators on the topic of metamemory, here we will focus on our own research program. This article will begin with a description of a theoretical framework that has evolved out of metamemory research, followed by a few remarks about our methodology, and will end with a review of our previously unpublished findings. (Our published findings will not be systematically reviewed here; instead, they will be mentioned only when necessary for continuity .)


Remembering can cause forgetting: retrieval dynamics in long-term memory.

Three studies show that the retrieval process itself causes long-lasting forgetting. Ss studied 8 categories (e.g., Fruit). Half the members of half the categories were then repeatedly practiced through retrieval tests (e.g., Fruit Or_____). Category-cued recall of unpracticed members of practiced categories was impaired on a delayed test. Experiments 2 and 3 identified 2 significant features of this retrieval-induced forgetting: The impairment remains when output interference is controlled, suggesting a retrieval-based suppression that endures for 20 min or more, and the impairment appears restricted to high-frequency members. Low-frequency members show little impairment, even in the presence of strong, practiced competitors that might be expected to block access to those items. These findings suggest a critical role for suppression in models of retrieval inhibition and implicate the retrieval process itself in everyday forgetting.


Distributed practice in verbal recall tasks: A review and quantitative synthesis.

The authors performed a meta-analysis of the distributed practice effect to illuminate the effects of temporal variables that have been neglected in previous reviews. This review found 839 assessments of distributed practice in 317 experiments located in 184 articles. Effects of spacing (consecutive massed presentations vs. spaced learning episodes) and lag (less spaced vs. more spaced learning episodes) were examined, as were expanding interstudy interval (ISI) effects. Analyses suggest that ISI and retention interval operate jointly to affect final-test retention; specifically, the ISI producing maximal retention increased as retention interval increased. Areas needing future research and theoretical implications are discussed.


An ontology for clinical questions about the contents of patient notes

OBJECTIVE
Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a novel question processing and classification model for answering clinical questions applied to electronic patient notes.

METHODS
There are four main steps in the work. Firstly, a relatively large set of clinical questions was collected from staff in an Intensive Care Unit. Then, a clinical question taxonomy was designed for question and answering purposes. Subsequently an annotation guideline was created and used to annotate the question set. Finally, a multilayer classification model was built to classify the clinical questions.

RESULTS
Through the initial classification experiments, we realized that the general features cannot contribute to high performance of a minimum classifier (a small data set with multiple classes). Thus, an automatic knowledge discovery and knowledge reuse process was designed to boost the performance by extracting and expanding the specific features of the questions. In the evaluation, the results show around 90% accuracy can be achieved in the answerable subclass classification and generic question templates classification. On the other hand, the machine learning method does not perform well at identifying the category of unanswerable questions, due to the asymmetric distribution.

CONCLUSIONS
In this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by the research community to improve the efficiency of their own question and answering systems.


Deep Learning for Intelligent Video Analysis

Analyzing videos is one of the fundamental problems of computer vision and multimedia content analysis for decades. The task is very challenging as video is an information-intensive media with large variations and complexities. Thanks to the recent development of deep learning techniques, researchers in both computer vision and multimedia communities are now able to boost the performance of video analysis significantly and initiate new research directions to analyze video content. This tutorial will present recent advances under the umbrella of video understanding, which start from a unified deep learning toolkit--Microsoft Cognitive Toolkit (CNTK) that supports popular model types such as convolutional nets and recurrent networks, to fundamental challenges of video representation learning and video classification, recognition, and finally to an emerging area of video and language.


Glance: rapidly coding behavioral video with the crowd

Behavioral researchers spend considerable amount of time coding video data to systematically extract meaning from subtle human actions and emotions. In this paper, we present Glance, a tool that allows researchers to rapidly query, sample, and analyze large video datasets for behavioral events that are hard to detect automatically. Glance takes advantage of the parallelism available in paid online crowds to interpret natural language queries and then aggregates responses in a summary view of the video data. Glance provides analysts with rapid responses when initially exploring a dataset, and reliable codings when refining an analysis. Our experiments show that Glance can code nearly 50 minutes of video in 5 minutes by recruiting over 60 workers simultaneously, and can get initial feedback to analysts in under 10 seconds for most clips. We present and compare new methods for accurately aggregating the input of multiple workers marking the spans of events in video data, and for measuring the quality of their coding in real-time before a baseline is established by measuring the variance between workers. Glance's rapid responses to natural language queries, feedback regarding question ambiguity and anomalies in the data, and ability to build on prior context in followup queries allow users to have a conversation-like interaction with their data - opening up new possibilities for naturally exploring video data.


An Unsupervised User Behavior Prediction Algorithm Based on Machine Learning and Neural Network For Smart Home

The user operates the smart home devices year in year out, have produced mass operation data, but these data do not be utilized well in past. Nowadays, these data can be used to predict user’s behavior custom with the development of big data and machine learning technologies, and then the prediction results can be employed to enhance the intelligence of a smart home system. In view of this, this paper proposes a novel unsupervised user behavior prediction (UUBP) algorithm, which employs an artificial neural network and proposes a forgetting factor to overcome the shortcomings of the previous prediction algorithm. This algorithm has a high-level of autonomous and self-organizing learning ability while does not require too much human intervention. Furthermore, the algorithm can better avoid the influence of user’s infrequent and out-of-date operation records, because of the forgetting factor. Finally, the use of real end user’s operation records to demonstrate that UUBP algorithm has a better level of performance than other algorithms from effectiveness.


Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types

We present a neural model for question generation from knowledge base triples in a “ZeroShot” setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in an encoderdecoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model sets a new state-ofthe-art for zero-shot QG.


Bulk current injection test modeling using an equivalent circuit for 1.8V mobile ICs

This paper shows a novel simulation method for bulk current injection (BCI) tests of I/O buffer circuits of mobile system memory. The simulation model consists of BCI probe, directional coupler, PCB, PKG, and IC. The proposed method is based on a behavioural I/O buffer model using a pulse generator as an input. A detailed simulation flow is introduced and validated through simulations performed on several injection probe loading conditions using a power decoupling capacitor and an on-chip decoupling capacitor.


HyperUAS - Imaging Spectroscopy from a Multirotor Unmanned Aircraft System

One of the key advantages of a low-flying unmanned aircraft system (UAS) is its ability to acquire digital images at an ultrahigh spatial resolution of a few centimeters. Remote sensing of quantitative biochemical and biophysical characteristics of small-sized spatially fragmented vegetation canopies requires, however, not only high spatial, but also high spectral (i.e., hyperspectral) resolution. In this paper, we describe the design, development, airborne operations, calibration, processing, and interpretation of image data collected with a new hyperspectral unmanned aircraft system (HyperUAS). HyperUAS is a remotely controlled multirotor prototype carrying onboard a lightweight pushbroom spectroradiometer coupled with a dual frequency GPS and an inertial movement unit. The prototype was built to remotely acquire imaging spectroscopy data of 324 spectral bands (162 bands in a spectrally binned mode) with bandwidths between 4 and 5 nm at an ultrahigh spatial resolution of 2–5 cm. Three field airborne experiments, conducted over agricultural crops and over natural ecosystems of Antarctic mosses, proved operability of the system in standard field conditions, but also in a remote and harsh, low-temperature environment of East Antarctica. Experimental results demonstrate that HyperUAS is capable of delivering georeferenced maps of quantitative biochemical and biophysical variables of vegetation and of actual vegetation health state at an unprecedented spatial resolution of 5 cm. C © 2014 Wiley Periodicals, Inc.


Sparse Dueling Bandits

The dueling bandit problem is a variation of the classical multi-armed bandit in which the allowable actions are noisy comparisons between pairs of arms. This paper focuses on a new approach for finding the “best” arm according to the Borda criterion using noisy comparisons. We prove that in the absence of structural assumptions, the sample complexity of this problem is proportional to the sum of the inverse squared gaps between the Borda scores of each suboptimal arm and the best arm. We explore this dependence further and consider structural constraints on the pairwise comparison matrix (a particular form of sparsity natural to this problem) that can significantly reduce the sample complexity. This motivates a new algorithm called Successive Elimination with Comparison Sparsity (SECS) that exploits sparsity to find the Borda winner using fewer samples than standard algorithms. We also evaluate the new algorithm experimentally with synthetic and real data. The results show that the sparsity model and the new algorithm can provide significant improvements over standard approaches.


Focus plus context screens: combining display technology with visualization techniques

Computer users working with large visual documents, such as large layouts, blueprints, or maps perform tasks that require them to simultaneously access overview information while working on details. To avoid the need for zooming, users currently have to choose between using a sufficiently large screen or applying appropriate visualization techniques. Currently available hi-res "wall-size" screens, however, are cost-intensive, space-intensive, or both. Visualization techniques allow the user to more efficiently use the given screen space, but in exchange they either require the user to switch between multiple views or they introduce distortion.In this paper, we present a novel approach to simultaneously display focus and context information. Focus plus context screens consist of a hi-res display and a larger low-res display. Image content is displayed such that the scaling of the display content is preserved, while its resolution may vary according to which display region it is displayed in. Focus plus context screens are applicable to practically all tasks that currently use overviews or fisheye views, but unlike these visualization techniques, focus plus context screens provide a single, non-distorted view. We present a prototype that seamlessly integrates an LCD with a projection screen and demonstrate four applications that we have adapted so far.


Treatment of posterolateral tibial plateau fractures with modified Frosch approach : a cadaveric study and case series

This study aimed to investigate the surgical techniques and the clinical efficacy of a modified Frosch approach in the treatment of posterolateral tibial plateau fractures. The standard Frosch approach was performed on 5 fresh-frozen cadavers. The mean bony surface area was measured upon adequate exposure of the proximal tibial cortex. Lateral proximal tibial plate and posterolateral T-plates were placed and the ease of the procedure was noted. In the study, 12 clinical cases of posterolateral tibial plateau fractures were treated via modified or standard Frosch approaches. The outcome was assessed over short to medium follow-up period. Cadaver studies allowed the inspection of the posterolateral joint surface of all specimens from the lateral side. The mean bony surface areas of the exposed lateral and posterolateral tibial plateau were (6.78 ± 1.13) cm2 and (3.59 ± 0.65) cm2, respectively. Lateral and posterolateral plates were implanted successfully. Lateral proximal tibial plates were fixed in 10 patients via a modified Frosch approach while posterolateral plates were fixed in 2 patients via a standard Frosch approach. Patients were followed up for 10 to 24 months (average: 15.7 months) and no complications were observed during this period. Based on the Rasmussen knee function score system, the results were recorded as excellent, good, and fair in 6, 4, and 2 patients, respectively. In conclusion, the modified Frosch approach has offers advantages of clear clarity in exposure, convenient for reduction and internal fixation of a fracture, and good positive clinical results over the normal approach.


Superficial dorsal penile vein thrombosis (penile Mondor's disease)

In our center between 1992 and 1994 penile Mondor's disease (superficial dorsal penile vein thrombosis) was diagnosed in 5 patients aged 20–39 years. In all patients the thromboses were noted 24–48 hours after a prolonged sexual act with or without an intercourse. The main symptom was a cord-like thickening of the superficial veins, which were painless or slightly painful. Doppler examination of the superficial dorsal vein revealed obstruction of the vessels. In 2 patients the retroglandular plexus was also involved. Patients were treated with anti-inflammatory medications (Tenoxicam or Ibuprofen). The resolution of the thrombosis occurred uneventfully within 4–6 weeks. No recurrence or erectile dysfunction was noted in any of the patients. Penile Mondor's disease is a benign pathology of the superficial dorsal penile vein and should be taken into account in the differential diagnosis of penile pathologies.


Opinion-aware Knowledge Graph for Political Ideology Detection

Identifying individual’s political ideology from their speeches and written texts is important for analyzing political opinions and user behavior on social media. Traditional opinion mining methods rely on bag-of-words representations to classify texts into different ideology categories. Such methods are too coarse for understanding political ideologies. The key to identify different ideologies is to recognize different opinions expressed toward a specific topic. To model this insight, we classify ideologies based on the distribution of opinions expressed towards real-world entities or topics. Specifically, we propose a novel approach to political ideology detection that makes predictions based on an opinion-aware knowledge graph. We show how to construct such graph by integrating the opinions and targeted entities extracted from text into an existing structured knowledge base, and show how to perform ideology inference by information propagation on the graph. Experimental results demonstrate that our method achieves high accuracy in detecting ideologies compared to baselines including LR, SVM and RNN.


Deep Convolution Neural Networks for Twitter Sentiment Analysis

Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment features by analyzing lexical and syntactic features. These features are expressed explicitly through sentiment words, emoticons, exclamation marks, and so on. In this paper, we introduce a word embeddings method obtained by unsupervised learning based on large twitter corpora, this method using latent contextual semantic relationships and co-occurrence statistical characteristics between words in tweets. These word embeddings are combined with n-grams features and word sentiment polarity score features to form a sentiment feature set of tweets. The feature set is integrated into a deep convolution neural network for training and predicting sentiment classification labels. We experimentally compare the performance of our model with the baseline model that is a word n-grams model on five Twitter data sets, the results indicate that our model performs better on the accuracy and F1-measure for twitter sentiment classification.


Arm pose copying for humanoid robots

Learning by imitation is becoming increasingly important for teaching humanoid robots new skills. The simplest form of imitation is behavior copying in which the robot is minimizing the difference between its perceived motion and that of the imitated agent. One problem that must be solved even in this simplest of all imitation tasks is calculating the learner's pose corresponding to the perceived pose of the agent it is imitating. This paper presents a general framework for solving this problem in closed form for the arms of a generalized humanoid robot of which most available humanoids are special cases. The paper also reports the evaluation of the proposed system for real and simulated robots.


In-vehicle information system as a driver's secondary activity: Case study

A robust methodology for detecting and evaluating driver distraction induced by in-vehicle information system using artificial neural network and fuzzy logic is introduced in this paper. An artificial neural network is used to predict driver's performance on a specific road segment. The predicted performance-based measures are compared to the driving with secondary task accomplishment. Fuzzy logic is applied to fuse the variables into a single output, which constitutes a level of driver distraction in percentage. The technique was tested on a vehicle simulator by ten drivers that exploited in-vehicle information system as a secondary activity. The driver-in-the-loop experiment outcomes are discussed.


Short-Interval Detailed Production Scheduling in 300mm Semiconductor Manufacturing using Mixed Integer and Constraint Programming

Fully automated 300mm manufacturing requires the adoption of a real-time lot dispatching paradigm. Automated dispatching has provided significant improvements over manual dispatching by removing variability from the thousands of dispatching decisions made every day in a fab. Real-time resolution of tool queues, with consideration of changing equipment states, process restrictions, physical and logical location of WIP, supply chain objectives and a myriad of other parameters, is required to ensure successful dispatching in the dynamic fab environment. However, the real-time dispatching decision in semiconductor manufacturing generally remains a reactive, heuristic response in existing applications, limited to the current queue of each tool. The shortcomings of this method of assigning WIP to tools, aptly named "opportunistic scavenging" as stated in G. Sullivan (1987), have become more apparent in lean manufacturing environments where lower WIP levels present fewer obvious opportunities for beneficial lot sequencing or batching. Recent advancements in mixed integer programming (MIP) and constraint programming (CP) have raised the possibility of integrating optimization software, commonly used outside of the fab environment to compute optimal solutions for scheduling scenarios ranging from order fulfillment systems to crew-shift-equipment assignments, with a real-time dispatcher to create a short-interval scheduler. The goal of such a scheduler is to optimize WIP flow through various sectors of the fab by expanding the analysis beyond the current WIP queue to consider upstream and downstream flow across the entire tool group or sector. This article describes the production implementation of a short-interval local area scheduler in IBM's leading-edge 300mm fab located in East Fishkill, New York, including motivation, approach, and initial results


AODVSEC: A Novel Approach to Secure Ad Hoc on-Demand Distance Vector (AODV) Routing Protocol from Insider Attacks in MANETs

Mobile Ad hoc Network (MANET) is a collection of mobile nodes that can communicate with each other using multihop wireless links without requiring any fixed based-station infrastructure and centralized management. Each node in the network acts as both a host and a router. In such scenario, designing of an efficient, reliable and secure routing protocol has been a major challenging issue over the last many years. Numerous schemes have been proposed for secure routing protocols and most of the research work has so far focused on providing security for routing using cryptography. In this paper, we propose a novel approach to secure Ad hoc On-demand Distance Vector (AODV) routing protocol from the insider attacks launched through active forging of its Route Reply (RREP) control message. AODV routing protocol does not have any security provision that makes it less reliable in publicly open ad hoc network. To deal with the concerned security attacks, we have proposed AODV Security Extension (AODVSEC) which enhances the scope of AODV for the security provision. We have compared AODVSEC with AODV and Secure AODV (SAODV) in normal situation as well as in presence of the three concerned attacks viz. Resource Consumption (RC) attack, Route Disturb (RD) attack, Route Invasion (RI) attack and Blackhole (BH) attack. To evaluate the performances, we have considered Packet Delivery Fraction (PDF), Average End-to-End Delay (AED), Average Throughput (AT), Normalized Routing Load (NRL) and Average Jitter and Accumulated Average Processing Time.


What's basic about basic emotions?

A widespread assumption in theories of emotion is that there exists a small set of basic emotions. From a biological perspective, this idea is manifested in the belief that there might be neurophysiological and anatomical substrates corresponding to the basic emotions. From a psychological perspective, basic emotions are often held to be the primitive building blocks of other, nonbasic emotions. The content of such claims is examined, and the results suggest that there is no coherent nontrivial notion of basic emotions as the elementary psychological primitives in terms of which other emotions can be explained. Thus, the view that there exist basic emotions out of which all other emotions are built, and in terms of which they can be explained, is questioned, raising the possibility that this position is an article of faith rather than an empirically or theoretically defensible basis for the conduct of emotion research. This suggests that perhaps the notion of basic emotions will not lead to significant progress in the field. An alternative approach to explaining the phenomena that appear to motivate the postulation of basic emotions is presented.


Domestic violence and psychological well-being of survivor women in Punjab , Pakistan

Violence against women is becoming a very critical issue, especially domestic violence. Public health practitioners of developing countries over the years had been deeply concern with trying to study and mitigate the impact of this on female survivors. It is defined as a gender based violence that results in, or is likely to result in, physical, sexual or mental harm /suffering to women, including threat of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life.1,2 Domestic violence is a global issue and commonly used to describe the “women abuse” those who suffer at the hands of their male partner, because wife abused by husband is the most common form of violence against women (e.g. wife beating, burning of women, acid throwing)3−6 and according to the American Medical Association domestic violence is perceived as a pattern of physical, sexual and/or psychological abuse by a person with whom the survivor had an intimate relationship.7−10 Domestic violence is also considered as an important cause of mortality and morbidity of women in every country where these associations have been studied.2 It is considered as an important cause of intentional injuries in women, which consequently reduce women’s confidence by decreasing their desire to participate fully in life.11−14 It occurs in almost all social and economic classes, races and religions in multiple patterns/trends however, not a single society could be declared free of this notion.15


Constraint-based Round Robin Tournament Planning

Sport tournament planning becomes a complex task in the presence of heterogeneous requirements from teams, media, fans and other parties. Existing approaches to sport tournament planning often rely on precomputed tournament schemes which may be too rigid to cater for these requirements. Existing work on sport tournaments suggests a separation of the planning process into three phases. In this work, it is shown that all three phases can be solved using nite-domain constraint programming. The design of Friar Tuck, a generic constraint-based round robin planning tool, is outlined. New numerical results on round robin tournaments obtained with Friar Tuck underline the potential of constraints over nite domains in this area.


Authentication of FPGA Bitstreams: Why and How

Encryption of volatile FPGA bitstreams provides confidentiality to the design but does not ensure its authenticity. This paper motivates the need for adding authentication to the configuration process by providing application examples where this functionality is useful. An examination of possible solutions is followed by suggesting a practical one in consideration of the FPGA’s configuration environment constraints. The solution presented here involves two symmetric-key encryption cores running in parallel to provide both authentication and confidentiality while sharing resources for efficient implementation.


Iterative Procedures for Nonlinear Integral Equations

The numerical solution of nonlinear integral equations involves the iterative soIutioon of finite systems of nonlinear algebraic or transcendental equations. Certain corwent i o n a l techniqucs for treating such systems are reviewed in the context of a particular class of n o n l i n e a r equations. A procedure is synthesized to offset some of the disadvantages of these t e c h n i q u e s in this context; however, the procedure is not restricted to this pt~rticular class of s y s t e m s of nonlinear equations.


Beam selection for performance-complexity optimization in high-dimensional MIMO systems

Millimeter-wave (mm-wave) communications systems offer a promising solution to meeting the increasing data demands on wireless networks. Not only do mm-wave systems allow orders of magnitude larger bandwidths, they also create a high-dimensional spatial signal space due to the small wavelengths, which can be exploited for beamforming and multiplexing gains. However, the complexity of digitally processing the entire high-dimensional signal is prohibitive. By exploiting the inherent channel sparsity in beamspace due to highly directional propagation at mm-wave, it is possible to design near-optimal transceivers with dramatically lower complexity. In such beamspace MIMO systems, it is first necessary to determine the set of beams which define the low-dimensional communication subspace. In this paper, we address this beam selection problem and introduce a simple power-based classifier for determining the beamspace sparsity pattern that characterizes the communication subspace. We first introduce a physical model for a small cell which will serve as the setting for our analysis. We then develop a classifier for the physical model, and show its optimality for a class of ideal signals. Finally, we present illustrative numerical results and show the feasibility of the classifier in mobile settings.


Authoritative Sources in a Hyperlinked Environment

The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of context on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authorative” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristrics for link-based analysis.


Efficient Computation of PageRank

This paper discusses efficient techniques for computing PageRank, a ranking metric for hypertext documents. We show that PageRank can be computed for very large subgraphs of the web (up to hundreds of millions of nodes) on machines with limited main memory. Running-time measurements on various memory configurations are presented for PageRank computation over the 24-million-page Stanford WebBase archive. We discuss several methods for analyzing the convergence of PageRank based on the induced ordering of the pages. We present convergence results helpful for determining the number of iterations necessary to achieve a useful PageRank assignment, both in the absence and presence of search queries.


The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity

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


The PageRank Citation Ranking: Bringing Order to the Web.

The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a method for rating Web pages objectively and mechanically, e ectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to e ciently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.


The Anatomy of a Large-Scale Hypertextual Web Search Engine

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.


Threat , Authoritarianism , and Selective Exposure to Information

We examined the hypothesis that threat alters the cognitive strategies used by high authoritarians in seeking out new political information from the environment. In a laboratory experiment, threat was manipulated through a “mortality salience” manipulation used in research on terror management theory (Pyszczynski, Solomon, & Greenberg, 2003). Subjects (N = 92) were then invited to read one of three editorial articles on the topic of capital punishment. We found that in the absence of threat, both low and high authoritarians were responsive to salient norms of evenhandedness in information selection, preferring exposure to a two-sided article that presents the merits of both sides of an issue to an article that selectively touts the benefits of the pro or con side of the issue. However, in the presence of threat, high but not low authoritarians became significantly more interested in exposure to an article containing uniformly pro-attitudinal arguments, and significantly less interested in a balanced, two-sided article. Finally, a path analysis indicated that selective exposure to attitude-congruent information led to more internally consistent policy attitudes and inhibited attitude change. Discussion focuses on the role of threat in conditioning the cognitive and attitudinal effects of authoritarianism.


Adaptive Learning Hybrid Model for Solar Intensity Forecasting

Energy management is indispensable in the smart grid, which integrates more renewable energy resources, such as solar and wind. Because of the intermittent power generation from these resources, precise power forecasting has become crucial to achieve efficient energy management. In this paper, we propose a novel adaptive learning hybrid model (ALHM) for precise solar intensity forecasting based on meteorological data. We first present a time-varying multiple linear model (TMLM) to capture the linear and dynamic property of the data. We then construct simultaneous confidence bands for variable selection. Next, we apply the genetic algorithm back propagation neural network (GABP) to learn the nonlinear relationships in the data. We further propose ALHM by integrating TMLM, GABP, and the adaptive learning online hybrid algorithm. The proposed ALHM captures the linear, temporal, and nonlinear relationships in the data, and keeps improving the predicting performance adaptively online as more data are collected. Simulation results show that ALHM outperforms several benchmarks in both short-term and long-term solar intensity forecasting.


A Survey and Typology of Human Computation Games

Human computation games (HCGs) can be seen as a paradigm that makes use of human brain power to solve computational problems as a byproduct of game play. In recent years, HCGs are increasing in popularity within various application areas which aim to harvest human problem solving skills by seamlessly integrating computational tasks into games. Thus, a proper system of describing such games is necessary in order to obtain a better understanding of the current body of work and identify new opportunities for future research directions. As a starting point, in this paper, we review existing literature on HCGs and present a typology consisting of 12 dimensions based on the key features of HCGs. With this typology, all HCGs could be classified and compared in a systematic manner.


Gossip along the way : Order-Optimal Consensus through Randomized Path Averaging

Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information processing over networks. However for many topologies that are realistic for wireless ad-hoc and sensor networks (like grids and random geometric graphs), the standard nearest-neighbor gossip converges very slowly. A recently proposed algorithm called geographic gossip improves gossip efficiency by a p n/ log n factor for random geometric graphs, by exploiting geographic information of node locations. In this paper we prove that a variation of geographic gossip that averages along routed paths, improves efficiency by an additional p n/ log n factor and is order optimal for grids and random geometric graphs. Our analysis provides some general techniques and can be used to provide bounds on the performance of randomized message passing algorithms operating over various graph topologies.


Evaluation of games for teaching computer science

Well-designed educational games (both online and offline) can be a valuable way to engage students with a topic. However, games can also be a time-consuming distraction, so it is useful to be able to identify which are suitable. This paper provides guidelines for evaluating games, and applies them to identify promising games. We begin by defining what is meant by a game, ruling out simple animations or quizzes, and focusing on a more playful approach to education. Based on this definition, we have identified games that are available for teaching topics in computer science, and classified them. We have developed criteria for evaluating games based on existing research on educational games, combined with educational theory and considering them in the context of CS. The criteria developed cover the topics taught, how easy it is to install and run, how engaging the game is, and how much time students might invest to use the game. We find that although there are a number of worth-while games in the field of CS and most are free, the coverage of topics is very patchy, with most focused on programming concepts and binary numbers.


Detecting and Tracking The Real-time Hot Topics: A Study on Computational Neuroscience

In this study, following the idea of our previous paper (Wang, et al., 2013a), we improve the method to detect and track hot topics in a specific field by using the real-time article usage data. With the “usage count” data provided by Web of Science, we take the field of computational neuroscience as an example to make analysis. About 10 thousand articles in the field of Computational Neuroscience are queried in Web of Science, when the records, including the usage count data of each paper, have been harvested and updated weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by the most frequently used keywords aggregated from the articles. The analysis reveals that hot topics in Computational Neuroscience are related to the key technologies, like “fmri”, “eeg”, “erp”, etc. Furthermore, using the weekly updated data, we track the dynamical changes of the topics. The characteristic of immediacy of usage data makes it possible to track the “heat” of hot topics timely and dynamically.


A probabilistic rating inference framework for mining user preferences from reviews

We propose a novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. Pref applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. Pref takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of Pref against several related algorithms, and suggest that Pref can produce reasonably good results using a small training corpus. We also describe a useful application of Pref as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated Pref with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.


Building Information Modeling and real world knowledge: A methodological approach to accurate semantic documentation for the built environment

Building Information Modeling is considered by the scientific literature as an emerging trend in the architectural documentation scenario, as it is basically a digital representation of physical and functional features of facilities, serving as a shared knowledge resource during their whole life cycle. BIM is actually a process (not a software, as someone indicated), in which different players act sharing data through digital models in a coordinated, consistent and always up to date workflow, in order to reach reliability and higher quality all over the construction process. This way BIM tools were originally meant to ease the design of new architectures, generated by parametric geometries connected through hierarchical relationships of “smart objects” (components self-aware of their identity and conscious of their interactions with each other). However, this approach can also be successfully applied to what already exists: TLS (Terrestrial Laser Scanning) or digital photogrammetry are supposed to be the first abstraction step in a methodology proposal intended as a scientific strategy in which BIM, relying on its own semantic splitting attitude and its topological structure, is explicitly used in representation of existing buildings belonging to the Cultural Heritage. Presenting some progresses in the development of a specific free Autodesk Revit plug-in, nicknamed GreenSpider after its capability to layout points in the digital domain as if they were nodes of an ideal cobweb, this paper examines how point clouds collected during high definition surveys can be processed with accuracy in a BIM environment, highlighting critical aspects and advantages deriving from the application of parametric techniques to the real world domain representation.


Graph Convolutional Networks With Argument-Aware Pooling for Event Detection

The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the benefits of the dependencybased convolutional neural networks and the entity mentionbased pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions.


Exploring the comparative salience of restaurant attributes: A conjoint analysis approach

This study explores how travelers select a restaurant for dining out, given that restaurant customers consider diverse attributes in making a selection. By applying a conjoint analysis, an exploratory multiplecase study is conducted for three restaurants in New York City. Findings from Study 1 (an overall travelers group) and Study 2 (two different country-of-residence groups: foreign and domestic travelers) show that food, value, atmosphere, and service are considered as substantially important criteria in selecting restaurants, in that order. However, results from Study 3 examining different restaurant types (lowpriced food stand, low-priced indoor, and high-priced indoor) reveal that the food attribute is the most important factor, regardless of restaurant types, whereas the other attributes’ rankings vary. Results from ypes of restaurants and travelers


Integrality and Separability of Input Devices

Current input device taxonomies and other frameworks typically emphasize the mechanical structure of input devices. We suggest that selecting an appropriate input device for an interactive task requires looking beyond the physical structure of devices to the deeper perceptual structure of the task, the device, and the interrelationship between the perceptual structure of the task and the control properties of the device. We affirm that perception is key to understanding performance of multidimensional input devices on multidimensional tasks. We have therefore extended the theory of processing of percetual structure to graphical interactive tasks and to the control structure of input devices. This allows us to predict task and device combinations that lead to better performance and hypothesize that performance is improved when the perceptual structure of the task matches the control structure of the device. We conducted an experiment in which subjects performed two tasks with different perceptual structures, using two input devices with correspondingly different control structures, a three-dimensional tracker and a mouse. We analyzed both speed and accuracy, as well as the trajectories generated by subjects as they used the unconstrained three-dimensional tracker to perform each task. The result support our hypothesis and confirm the importance of matching the perceptual structure of the task and the control structure of the input device.


7 Toward a Universal Underspecified Semantic Representation

We define Canonical Form Minimal Recursion Semantics (CF-MRS) and prove that all the well-formed MRS structures generated by the MRS semantic composition algorithm are in this form. We prove that the qeq relationships are equivalent to outscoping relations when MRS structures are in this form. This result fills the gap between some underspecification formalisms and motivates defining a Canonical Form Underspecified Representation (CF-UR) which brings those underspecification formalisms together.


MIMO radar: an idea whose time has come

It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radar's performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy.


Implementation of multiport dc-dc converter-based Solid State Transformer in smart grid system

A solid-state transformer (SST) would be at least as efficient as a conventional version but would provide other benefits as well, particularly as renewable power sources become more widely used. Among its more notable strong points are on-demand reactive power support for the grid, better power quality, current limiting, management of distributed storage devices and a dc bus. Most of the nation's power grid currently operates one way - power flows from the utility to the consumer - and traditional transformers simply change voltage from one level to another. But smart transformers, based on power semiconductor switches, are more versatile. Not only can they change voltage levels, but also can effectively control the power flow in both directions. The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this paper. The DC-DC stage is based on a quad active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A novel technique that complements the SISO controller by taking into account the cross coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models.


Compact Printed Wide-Slot UWB Antenna With 3.5/5.5-GHz Dual Band-Notched Characteristics

A novel compact wide-slot ultrawideband (UWB) antenna with dual band-notched characteristics is presented. The antenna consists of an inverted U-shaped slot on the ground plane and a radiation patch similar to the slot that is fed by a 50-Ω microstrip line. By etching a C-shaped slot on the radiation patch and extruding an L-shaped stub from the ground plane, dual band-notched properties in the WiMAX (3.4-3.69 GHz) and WLAN (5.15-5.825 GHz) are achieved. The proposed antenna has a compact size of 20×27 mm2 and operates from 2.89 to 11.52 GHz. Furthermore, nearly omnidirectional radiation patterns and constant gain are obtained in the working band.


Connecting and separating mind-sets: culture as situated cognition.

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People perceive meaningful wholes and later separate out constituent parts (D. Navon, 1977). Yet there are cross-national differences in whether a focal target or integrated whole is first perceived. Rather than construe these differences as fixed, the proposed culture-as-situated-cognition model explains these differences as due to whether a collective or individual mind-set is cued at the moment of observation. Eight studies demonstrated that when cultural mind-set and task demands are congruent, easier tasks are accomplished more quickly and more difficult or time-constrained tasks are accomplished more accurately (

STUDY 1
Koreans, Korean Americans; STUDY 2: Hong Kong Chinese; STUDY 3: European- and Asian-heritage Americans; STUDY 4: Americans;

STUDY
5 Hong Kong Chinese; STUDY 6: Americans; STUDY 7: Norwegians; STUDY 8: African-, European-, and Asian-heritage Americans). Meta-analyses (d = .34) demonstrated homogeneous effects across geographic place (East-West), racial-ethnic group, task, and sensory mode-differences are cued in the moment. Contrast and separation are salient individual mind-set procedures, resulting in focus on a single target or main point. Assimilation and connection are salient collective mind-set procedures, resulting in focus on multiplicity and integration.


Multiview Image Completion with Space Structure Propagation

We present a multiview image completion method that provides geometric consistency among different views by propagating space structures. Since a user specifies the region to be completed in one of multiview photographs casually taken in a scene, the proposed method enables us to complete the set of photographs with geometric consistency by creating or removing structures on the specified region. The proposed method incorporates photographs to estimate dense depth maps. We initially complete color as well as depth from a view, and then facilitate two stages of structure propagation and structure-guided completion. Structure propagation optimizes space topology in the scene across photographs, while structure-guide completion enhances, and completes local image structure of both depth and color in multiple photographs with structural coherence by searching nearest neighbor fields in relevant views. We demonstrate the effectiveness of the proposed method in completing multiview images.


Effectiveness and Adoption of a Drawing-to-Learn Study Tool for Recall and Problem Solving: Minute Sketches with Folded Lists

Drawing by learners can be an effective way to develop memory and generate visual models for higher-order skills in biology, but students are often reluctant to adopt drawing as a study method. We designed a nonclassroom intervention that instructed introductory biology college students in a drawing method, minute sketches in folded lists (MSFL), and allowed them to self-assess their recall and problem solving, first in a simple recall task involving non-European alphabets and later using unfamiliar biology content. In two preliminary ex situ experiments, students had greater recall on the simple learning task, non-European alphabets with associated phonetic sounds, using MSFL in comparison with a preferred method, visual review (VR). In the intervention, students studying using MSFL and VR had ∼50-80% greater recall of content studied with MSFL and, in a subset of trials, better performance on problem-solving tasks on biology content. Eight months after beginning the intervention, participants had shifted self-reported use of drawing from 2% to 20% of study time. For a small subset of participants, MSFL had become a preferred study method, and 70% of participants reported continued use of MSFL. This brief, low-cost intervention resulted in enduring changes in study behavior.


A Temporal Sequence Learning for Action Recognition and Prediction

In this work1, we present a method to represent a video with a sequence of words, and learn the temporal sequencing of such words as the key information for predicting and recognizing human actions. We leverage core concepts from the Natural Language Processing (NLP) literature used in sentence classification to solve the problems of action prediction and action recognition. Each frame is converted into a word that is represented as a vector using the Bag of Visual Words (BoW) encoding method. The words are then combined into a sentence to represent the video, as a sentence. The sequence of words in different actions are learned with a simple but effective Temporal Convolutional Neural Network (T-CNN) that captures the temporal sequencing of information in a video sentence. We demonstrate that a key characteristic of the proposed method is its low-latency, i.e. its ability to predict an action accurately with a partial sequence (sentence). Experiments on two datasets, UCF101 and HMDB51 show that the method on average reaches 95% of its accuracy within half the video frames. Results, also demonstrate that our method achieves compatible state-of-the-art performance in action recognition (i.e. at the completion of the sentence) in addition to action prediction.


Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral.
 Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.


Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.


Good features to track

No feature-based vision system can work unless good features can be identi ed and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under a ne image transformations. We test performance with several simulations and experiments.


Planning and Acting in Partially Observable Stochastic Domains

In this paper we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains We begin by introducing the theory of Markov decision processes mdps and partially observable mdps pomdps We then outline a novel algorithm for solving pomdps o line and show how in some cases a nite memory controller can be extracted from the solution to a pomdp We conclude with a discussion of how our approach relates to previous work the complexity of nding exact solutions to pomdps and of some possibilities for nding approximate solutions