title: Self-efficacy: Toward a unifying theory of behavioral change

abstract: The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from four principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. The more dependable the experiential sources, the greater are the changes in perceived selfefficacy. A number of factors are identified as influencing the cognitive processing of efficacy information arising from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. Possible directions for further research are discussed.


title: A DLL-Supported, Low Phase Noise Fractional-N PLL With a Wideband VCO and a Highly Linear Frequency Ramp Generator for FMCW Radars

abstract: This paper describes a frequency synthesizer for frequency-modulated continuous wave (FMCW) radars, operating in combination with a frequency multiplier, at 77 GHz. The fractional-N phase-locked loop (PLL)-based synthesizer is equipped with a wideband voltage-controlled oscillator, which is realized with CMOS transistors in a current-reuse technique, and a delay-locked loop (DLL). Utilized as a frequency multiplier for the reference signal, the DLL improves both the phase noise performance of the PLL and the linearity of the frequency sweep generated by the PLL. Two types of voltage-controlled delay lines used in the DLL are also introduced and compared in this paper. Both show a good performance in terms of phase noise and can be easily realized in a standard CMOS technology. The false-lock issue of the DLL is also discussed and a solution is proposed. Furthermore, a frequency multiplier is used to multiply the output frequency of the PLL by a multiplication factor of 18. This architecture enables an increase in the target resolution and an improvement in the accuracy of the measurements of FMCW radars.


title: Effective Communication in Requirements Elicitation: A Comparison of Methodologies

abstract: The elicitation or communication of user requirements comprises an early and critical but highly error-prone stage in system development. Socially oriented methodologies provide more support for user involvement in design than the rigidity of more traditional methods, facilitating the degree of user---designer communication and the `capture' of requirements. A more emergent and collaborative view of requirements elicitation and communication is required to encompass the user, contextual and organisational factors. From this accompanying literature in communication issues in requirements elicitation, a four-dimensional framework is outlined and used to appraise comparatively four different methodologies seeking to promote a closer working relationship between users and designers. The facilitation of communication between users and designers is subject to discussion of the ways in which communicative activities can be `optimised' for successful requirements gathering, by making recommendations based on the four dimensions to provide fruitful considerations for system designers.


title: Grid-connected PV System Using a Quasi-Z-source Inverter

abstract: This paper presents the MPPT (Maximum Power Point Tracking) and PCC (Point of Common Coupling) current control strategy for photovoltaic (PV) grid-connected generating system using the quasi-Z-source inverter (QZSI). At first, in order to explain the above controllers, the characteristic of QZSI is analyzed. And then, the MPPT control technique with a modified P&O method, the PCC current control one for the regulation of dc-link capacitor voltage, and the PWM methods for the proposed system are explained. The feasibility of the propose algorithm is verified through the simulation and experiment with 3kW system.


A Cognitive Model of Social Influence.


title: Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme

abstract: In this paper, a new algorithm for vehicle logo recognition on the basis of an enhanced scale-invariant feature transform (SIFT)-based feature-matching scheme is proposed. This algorithm is assessed on a set of 1200 logo images that belong to ten distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1200 images to a training set and a testing set, respectively. It is shown that the enhanced matching approach proposed in this paper boosts the recognition accuracy compared with the standard SIFT-based feature-matching method. The reported results indicate a high recognition rate in vehicle logos and a fast processing time, making it suitable for real-time applications.


title: A Machine Learning Approach for Fault Detection in Vehicular Cyber-Physical Systems

abstract: A network of vehicular cyber-physical systems (VCPSs) can use wireless communications to interact with each other and the surrounding environment to improve transportation safety, mobility, and sustainability. However, cloud-oriented architectures are vulnerable to cyber attacks, which may endanger passenger and pedestrian safety and privacy, and cause severe property damage. For instance, a hacker can use message falsification attack to affect functionality of a particular application in a platoon of VCPSs. In this paper, a neural network-based fault detection technique is applied to detect and track fault data injection attacks on the cooperative adaptive cruise control layer of a platoon of connected vehicles in real time. A decision support system was developed to reduce the probability and severity of any consequent accident. A case study with its design specifications is demonstrated in detail. The simulation results show that the proposed method can improve system reliability, robustness, and safety.


title: A comparative study of navigation meshes

abstract: A navigation mesh is a representation of a 2D or 3D virtual environment that enables path planning and crowd simulation for walking characters. Various state-of-the-art navigation meshes exist, but there is no standardized way of evaluating or comparing them. Each implementation is in a different state of maturity, has been tested on different hardware, uses different example environments, and may have been designed with a different application in mind. In this paper, we conduct the first comparative study of navigation meshes. First, we give general definitions of 2D and 3D environments and navigation meshes. Second, we propose theoretical properties by which navigation meshes can be classified. Third, we introduce metrics by which the quality of a navigation mesh implementation can be measured objectively. Finally, we use these metrics to compare various state-of-the-art navigation meshes in a range of 2D and 3D environments. We expect that this work will set a new standard for the evaluation of navigation meshes, that it will help developers choose an appropriate navigation mesh for their application, and that it will steer future research on navigation meshes in interesting directions.


Technology , Geography , and Trade Author ( s ) :


title: Serotonergic function, two-mode models of self-regulation, and vulnerability to depression: What depression has in common with impulsive aggression

abstract: Evidence from diverse literatures supports the viewpoint that two modes of self-regulation exist, a lower-order system that responds quickly to associative cues of the moment and a higher-order system that responds more reflectively and planfully; that low serotonergic function is linked to relative dominance of the lower-order system; that how dominance of the lower-order system is manifested depends on additional variables; and that low serotonergic function therefore can promote behavioral patterns as divergent as impulsive aggression and lethargic depression. Literatures reviewed include work on two-mode models; studies of brain function supporting the biological plausibility of the two-mode view and the involvement of serotonergic pathways in functions pertaining to it; and studies relating low serotonergic function to impulsiveness, aggression (including extreme violence), aspects of personality, and depression vulnerability. Substantial differences between depression and other phenomena reviewed are interpreted by proposing that depression reflects both low serotonergic function and low reward sensitivity. The article closes with brief consideration of the idea that low serotonergic function relates to even more diverse phenomena, whose natures depend in part on sensitivities of other systems.


title: Surface EMG pattern recognition for real-time control of a wrist exoskeleton

abstract: BackgroundSurface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.MethodsBoth sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.ResultsThe data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.ConclusionsThe results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.


title: Human Age Estimation Based on Locality and Ordinal Information

abstract: In this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original high-dimensional feature space. Thus, we first measure the energy of each feature in preserving the underlying local structure information and the ordinal information of the facial images, respectively, and then we intend to learn a low-dimensional aging representation that can maximally preserve both kinds of information. To further improve the performance, we try to eliminate the redundant local information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation among features. Finally, we formulate all these issues into a unified optimization problem, which is similar to linear discriminant analysis in format. Since it is expensive to collect the labeled facial aging images in practice, we extend the proposed supervised method to a semi-supervised learning mode including the semi-supervised feature selection method and the semi-supervised age prediction algorithm. Extensive experiments are conducted on the FACES dataset, the Images of Groups dataset, and the FG-NET aging dataset to show the power of the proposed algorithms, compared to the state-of-the-arts.


title: Enhancing Privacy in Remote Data Classification

abstract: Neural networks are a fundamental tool in data classification. A protocol whereby a user may ask a service provider to run a neural network on an input provided in encrypted format is proposed here, in such a way that the neural network owner does not get any knowledge about the processed data. At the same time, the knowledgeb embeddedwithin the network itself is protected.With respect to previousworks in this field, the interaction between the user and the neural network owner is kept to a minimum without resorting to general secure multi-party computation protocols.


title: Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

abstract: An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.


title: Learning Factored Representations in a Deep Mixture of Experts

abstract: Mixtures of Experts combine the outputs of several "expert" networks, each of which specializes in a different part of the input space. This is achieved by training a "gating" network that maps each input to a distribution over the experts. Such models show promise for building larger networks that are still cheap to compute at test time, and more parallelizable at training time. In this this work, we extend the Mixture of Experts to a stacked model, the Deep Mixture of Experts, with multiple sets of gating and experts. This exponentially increases the number of effective experts by associating each input with a combination of experts at each layer, yet maintains a modest model size. On a randomly translated version of the MNIST dataset, we find that the Deep Mixture of Experts automatically learns to develop location-dependent ("where") experts at the first layer, and class-specific ("what") experts at the second layer. In addition, we see that the different combinations are in use when the model is applied to a dataset of speech monophones. These demonstrate effective use of all expert combinations.


Extracting Tactics from Cybersecurity Articles


title: Generic Attacks on Secure Outsourced Databases

abstract: Recently, various protocols have been proposed for securely outsourcing database storage to a third party server, ranging from systems with "full-fledged" security based on strong cryptographic primitives such as fully homomorphic encryption or oblivious RAM, to more practical implementations based on searchable symmetric encryption or even on deterministic and order-preserving encryption. On the flip side, various attacks have emerged that show that for some of these protocols confidentiality of the data can be compromised, usually given certain auxiliary information. We take a step back and identify a need for a formal understanding of the inherent efficiency/privacy trade-off in outsourced database systems, independent of the details of the system. We propose abstract models that capture secure outsourced storage systems in sufficient generality, and identify two basic sources of leakage, namely access pattern and ommunication volume. We use our models to distinguish certain classes of outsourced database systems that have been proposed, and deduce that all of them exhibit at least one of these leakage sources. We then develop generic reconstruction attacks on any system supporting range queries where either access pattern or communication volume is leaked. These attacks are in a rather weak passive adversarial model, where the untrusted server knows only the underlying query distribution. In particular, to perform our attack the server need not have any prior knowledge about the data, and need not know any of the issued queries nor their results. Yet, the server can reconstruct the secret attribute of every record in the database after about $N^4$ queries, where N is the domain size. We provide a matching lower bound showing that our attacks are essentially optimal. Our reconstruction attacks using communication volume apply even to systems based on homomorphic encryption or oblivious RAM in the natural way. Finally, we provide experimental results demonstrating the efficacy of our attacks on real datasets with a variety of different features. On all these datasets, after the required number of queries our attacks successfully recovered the secret attributes of every record in at most a few seconds.


title: Copyright c ○ Geoffrey Hinton 2006. To Recognize Shapes, First Learn to Generate Images

abstract: Abstract The uniformity of the cortical architecture and the ability of functions to move to different areas of cortex following early damage strongly suggest that there is a single basic learning algorithm for extracting underlying structure from richly structured, high-dimensional sensory data. There have been many attempts to design such an algorithm, but until recently they all suffered from serious computational weaknesses. This chapter describes several of the proposed algorithms and shows how they can be combined to produce hybrid methods that work efficiently in networks with many layers and millions of adaptive connections.


title: Utilizing RFID Signaling Scheme for Localization of Stationary Objects and Speed Estimation of Mobile Objects

abstract: Mapping the physical location of nodes within a wireless sensor network (WSN) is critical in many applications such as tracking and environmental sampling. Passive RFID tags pose an interesting solution to localizing nodes because an outside reader, rather than the tag, supplies the power to the tag. Thus, utilizing passive RFID technology allows a localization scheme to not be limited to objects that have wireless communication capability because the technique only requires that the object carries a RFID tag. This paper illustrates a method in which objects can be localized without the need to communicate received signal strength information between the reader and the tagged item. The method matches tag count percentage patterns under different signal attenuation levels to a database of tag count percentages, attenuations and distances from the base station reader.


title: GUI Design for IDE Command Recommendations

abstract: This paper describes a novel design of a graphical user interface (GUI) to recommend useful command within an integrated development environment. The recommendation GUI contains a description of the suggested command, an explanation why the command is recommended, and a command usage example. The proposed design is based on the analysis of relevant guidelines identified in the literature. Its perceived usability and acceptance were evaluated in a live user study with 36 software developers. Our findings, partially contradicting existing literature, indicate that the presentation of the command-the description and the example-is perceived as more useful than the explanation of the rationale for the recommendation.


title: Measuring Stipple Aesthetics in Hand-Drawn and Computer-Generated Images

abstract: When people compare a computer-generated illustration to a hand-drawn illustration of the same object, they usually perceive differences. This seems to indicate that the two kinds of images follow different aesthetic principles. To explore and explain these differences, the authors compare texture stippling in hand-drawn and computer-generated illustrations, using image-processing analysis techniques.


title: Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator

abstract: Recent papers have shown the possibility to implement large scale neural network models that perform complex algorithms in a biologically realistic way. However, such models have been simulated on architectures unable to perform real-time simulations. In previous work we presented the possibility to simulate simple models in real-time on the SpiNNaker neuromimetic architecture. However, such models were “static”: the algorithm performed was defined at design-time. In this paper we present a novel learning rule, that exploits the peculiarities of the SpiNNaker system, enabling models designed with the Neural Engineering Framework (NEF) to learn transfer functions using a supervised framework. We show that the proposed learning rule, belonging to the Prescribed Error Sensitivity (PES) class, is able to learn, effectively, both linear and non-linear functions.


title: A Review of CALL and L2 Reading: Glossing for Comprehension and Acquisition

abstract: This paper provides a comprehensive review of the present state of second language reading research and computer-assisted glossing for reading comprehension and vocabulary acquisition. Although computer-assisted language learning, in general, and computer-assisted glossing, in particular, are often cited as facilitative pedagogical and self-study tools for second language reading development Chun, 2001; Al-Seghayer, 2003; Ko, 2005; Blake, 2007; Stockwell, 2011, the state of computer-assisted glossing research presents a much less compelling and far more nuanced picture of efficacy and facilitation Ariew & Ercetin, 2004; Bowles, 2004; Taylor, 2006, 2009; AbuSeileek, 2008; Sato & Sazuki, 2010. Research on glossing for reading comprehension presents a mixed collection of findings suggesting facilitation Leffa, 1992; Lomicka, 1998; Ko, 2005 and inhibition Hegelheimer, 1997; Plass et al., 2003; Akbulut, 2005; Sakar & Ercetin, 2005, although affective and attentional benefits of glossing for reading comprehension are widely acknowledged. On the other hand, research on incidental vocabulary acquisition as a result of glossing-and particularly multimedia glossing-suggests significant effect and a compelling rationale for ulitization of glossing tools in classroom and self-study contexts Ariew & Ercetin, 2004; AbuSeileek, 2008; Taylor, 2009; Stockwell, 2011; Aljabri, 2011.


title: Simultaneous multithreading: maximizing on-chip parallelism

abstract: This paper examines simultaneous multithreading, a technique permitting several independent threads to issue instructions to a superscalar's multiple functional units in a single cycle. We present several models of simultaneous multithreading and compare them with alternative organizations: a wide superscalar, a fine-grain multithreaded processor, and single-chip, multiple-issue multiprocessing architectures. Our results show that both (single-threaded) superscalar and fine-grain multithreaded architectures are limited in their ability to utilize the resources of a wide-issue processor. Simultaneous multithreading has the potential to achieve 4 times the throughput of a superscalar, and double that of fine-grain multi-threading. We evaluate several cache configurations made possible by this type of organization and evaluate tradeoffs between them. We also show that simultaneous multithreading is an attractive alternative to single-chip multiprocessors; simultaneous multithreaded processors with a variety of organizations outperform corresponding conventional multiprocessors with similar execution resources. While simultaneous multithreading has excellent potential to increase processor utilization, it can add substantial complexity to the design. We examine many of these complexities and evaluate alternative organizations in the design space.


title: A therapeutic confirmatory study to assess the safety and efficacy of Zydena (udenafil) for the treatment of erectile dysfunction in male patients with diabetes mellitus.

abstract: INTRODUCTION ::: Patients with diabetes mellitus (DM) are reported to experience more severe erectile dysfunction (ED) symptoms and respond less to ED treatments compared with patients with ED of other etiologies. ::: ::: ::: AIM ::: This study was undertaken to evaluate the safety and efficacy of udenafil for the treatment of ED in a larger number of patients with DM. ::: ::: ::: METHODS ::: A placebo-controlled, randomized, double-blind, double-dummy, parallel-group design multicenter study, fixed-dose trial was conducted. The trial involved seven study sites in Korea, with 174 ED patients with DM. The subjects, treated with placebo, 100 mg, or 200 mg of udenafil for 12 weeks, were asked to complete the International Index of Erectile Function (IIEF), the Sexual Encounter Profile (SEP) diary, and the Global Assessment Question (GAQ) during the study period. ::: ::: ::: MAIN OUTCOME MEASURES ::: The primary efficacy parameter was the change in the erectile function domain (EFD) score of IIEF from baseline. Secondary parameters were IIEF questions 3 (Q3) and Q4, SEP Q2 and Q3, rate of achieving normal erectile function (EFD ≥ 26), and the response to GAQ. ::: ::: ::: RESULTS ::: Compared with the placebo, patients receiving both doses of udenafil showed statistically significant improvements in the IIEF-EFD score, respectively. However, statistically significant difference was not observed between the udenafil 100 mg and the udenafil 200 mg groups. Similar results were observed in the comparison of Q3 and Q4 of IIEF, SEP diary, and GAQ. The percentages of subjects experiencing at least one adverse event related to the study drugs were 3.6%, 15.8%, and 22.4% for the placebo, udenafil 100 mg, and udenafil 200 mg groups, respectively. However, these events were all mild in severity. Major adverse events were flushing, headache, nausea, and conjunctival hyperemia. ::: ::: ::: CONCLUSION ::: Udenafil was significantly effective for the treatment of ED, demonstrating statistically significant improvement in erectile function in patients with DM. The incidence of adverse events was relatively low and well tolerated in patients with DM.


title: How to Prove Yourself: Practical Solutions to Identification and Signature Problems

abstract: In this paper we describe simple identification and signature schemes which enable any user to prove his identity and the authenticity of his messages to any other user without shared or public keys. The schemes are provably secure against any known or chosen message attack if factoring is difficult, and typical implementations require only 1% to 4% of the number of modular multiplications required by the RSA scheme. Due to their simplicity, security and speed, these schemes are ideally suited for microprocessor-based devices such as smart cards, personal computers, and remote control systems.


title: Cleaning products and air fresheners: exposure to primary and secondary air pollutants

abstract: Abstract Building occupants, including cleaning personnel, are exposed to a wide variety of airborne chemicals when cleaning agents and air fresheners are used in buildings. Certain of these chemicals are listed by the state of California as toxic air contaminants (TACs) and a subset of these are regulated by the US federal government as hazardous air pollutants (HAPs). California's Proposition 65 list of species recognized as carcinogens or reproductive toxicants also includes constituents of certain cleaning products and air fresheners. In addition, many cleaning agents and air fresheners contain chemicals that can react with other air contaminants to yield potentially harmful secondary products. For example, terpenes can react rapidly with ozone in indoor air generating many secondary pollutants, including TACs such as formaldehyde. Furthermore, ozone–terpene reactions produce the hydroxyl radical, which reacts rapidly with organics, leading to the formation of other potentially toxic air pollutants. Indoor reactive chemistry involving the nitrate radical and cleaning-product constituents is also of concern, since it produces organic nitrates as well as some of the same oxidation products generated by ozone and hydroxyl radicals. Few studies have directly addressed the indoor concentrations of TACs that might result from primary emissions or secondary pollutant formation following the use of cleaning agents and air fresheners. In this paper, we combine direct empirical evidence with the basic principles of indoor pollutant behavior and with information from relevant studies, to analyze and critically assess air pollutant exposures resulting from the use of cleaning products and air fresheners. Attention is focused on compounds that are listed as HAPs, TACs or Proposition 65 carcinogens/reproductive toxicants and compounds that can readily react to generate secondary pollutants. The toxicity of many of these secondary pollutants has yet to be evaluated. The inhalation intake of airborne organic compounds from cleaning product use is estimated to be of the order of 10 mg d −1 person −1 in California. More than two dozen research articles present evidence of adverse health effects from inhalation exposure associated with cleaning or cleaning products. Exposure to primary and secondary pollutants depends on the complex interplay of many sets of factors and processes, including cleaning product composition, usage, building occupancy, emission dynamics, transport and mixing, building ventilation, sorptive interactions with building surfaces, and reactive chemistry. Current understanding is sufficient to describe the influence of these variables qualitatively in most cases and quantitatively in a few.


title: Predicting Train Occupancies based on Query Logs and External Data Sources

abstract: On dense railway networks "such as in Belgium" train travelers are frequently confronted with overly occupied trains, especially during peak hours. Crowdedness on trains leads to a deterioration in the quality of service and has a negative impact on the well-being of the passenger. In order to stimulate travelers to consider less crowded trains, the iRail project wants to show an occupancy indicator in their route planning applications by the means of predictive modeling. As there is no official occupancy data available, training data is obtained by crowd-sourcing using the iRail web app and the mobile Railer application for iPhone. Users can indicate their departure & arrival station, at what time they took a train and classify the occupancy of that train into the classes: low, medium or high. While preliminary results on a limited dataset conclude that the models do not yet perform sufficiently well, we are convinced that with further research and a larger amount of data, our predictive model will be able to achieve higher predictive performances. All datasets used in the current research are, for that purpose, made publicly available under an open license on the iRail website and in the form of a Kaggle competition. Moreover, an infrastructure is set up that automatically processes new logs submitted by users in order for our model to continuously learn. Occupancy predictions for future trains are made available through an api.


title: Simulation of direct thrust control for linear induction motor including end-effect

abstract: In this paper, a direct thrust control (DTC) of linear induction motor (LIM) has been developed and a comprehensive study is presented. DTC is a method based on primer flux control in the stationary reference frame using direct control of the inverter switching. The basic concept of DTC is to control both primer flux and electromagnetic thrust of the machine simultaneously. Modeling phase begins with a derivation of suitable mathematical model to describe LIM in the stationary α-β reference frame. Then, the concept of DTC based on switching table (ST-DTC) strategy is illustrated and dealt with in detail. Finally, the performance of this control method has been demonstrated by simulations performed using Matlab/Simulink and the results obtained from the simulations are discussed.


title: Thermal Models and Electrical Machine Performance Improvement Using Encapsulation Material

abstract: This paper presents thermal finite element analysis (FEA) and experiments to demonstrate improvement of electrical machine performance using end-turn encapsulation materials with high thermal conductivity. The thermal performance of electric machines is an extremely important factor for power density and operating life. Forced air and liquid cooling are used to achieve required thermal management and high power density in many modern applications, but addition of high thermal conductivity materials can be adopted and used to reduce temperature rise and increase the machine's output capabilities, too. Thermal finite element models are developed and evaluated to compare different encapsulation materials under various operating points in this paper. Finally, experimental validation was performed for each case to prove the thermal analysis and to demonstrate the improved machine output capabilities when thermally-conductive encapsulants are used.


title: Globally optimal toon tracking

abstract: The ability to identify objects or region correspondences between consecutive frames of a given hand-drawn animation sequence is an indispensable tool for automating animation modification tasks such as sequence-wide recoloring or shape-editing of a specific animated character. Existing correspondence identification methods heavily rely on appearance features, but these features alone are insufficient to reliably identify region correspondences when there exist occlusions or when two or more objects share similar appearances. To resolve the above problems, manual assistance is often required. In this paper, we propose a new correspondence identification method which considers both appearance features and motions of regions in a global manner. We formulate correspondence likelihoods between temporal region pairs as a network flow graph problem which can be solved by a well-established optimization algorithm. We have evaluated our method with various animation sequences and results show that our method consistently outperforms the state-of-the-art methods without any user guidance.


title: Consensus Report for Regionalization of Services for Critically Ill or Injured Children

abstract: The care of critically ill children has become more complex and demanding. This document establishes recommendations for developing regionalized integration of the care of these children into the emergency medical services system. These recommendations were developed by pediatricians with expertise in pediatric critical care, transport, and emergency medicine from the Pediatric Section of the Society of Critical Care Medicine Task Force on Regionalization of Pediatric Critical Care and the Committee on Pediatric Emergency Medicine from the American Academy of Pediatrics. The document was developed from existing guidelines from a number of professional organizations (including the Society of Critical Care Medicine and the American Academy of Pediatrics), a thorough review of the literature, and expert consensus.


title: Crime analysis and prediction using data mining

abstract: Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day.


title: Mapping world scientific collaboration: Authors, institutions, and countries

abstract: International collaboration is being heralded as the hallmark of contemporary scientific production. Yet little quantitative evidence has portrayed the landscape and trends of such collaboration. To this end, 14,000,000 documents indexed in Thomson Reuters's Web of Science (WoS) were studied to provide a state-of-the-art description of scientific collaborations across the world. The results indicate that the number of authors in the largest research teams have not significantly grown during the past decade; however, the number of smaller research teams has seen significant increases in growth. In terms of composition, the largest teams have become more diverse than the latter teams and tend more toward interinstitutional and international collaboration. Investigating the size of teams showed large variation between fields. Mapping scientific cooperation at the country level reveals that Western countries situated at the core of the map are extensively cooperating with each other. High-impact institutions are significantly more collaborative than others. This work should inform policy makers, administrators, and those interested in the progression of scientific collaboration. © 2012 Wiley Periodicals, Inc.


title: Indoor mapping using gmapping on embedded system

abstract: In recent years, the core technology enable the rapid rise of autonomous vehicles like the solving the Simultaneous Localization And Mapping (SLAM) problem on the embedded system. And the gmapping package on the ROS platform providing laser-based SLAM is widely used and studied. However, implementing it on embedded system imposes two challenges to us: running ROS requires large CPU consumption and the algorithm of SLAM is computationally intensive. In this paper, we present a system implementation of the gmapping on an ARM based embedded hardware not installing ROS and we also improve the architecture of the mapping system by parallel execution of mapping and estimating pose. The mapping process is running on the embedded system and it can create a 2-D occupancy map from laser and pose data collected by a mobile robot with low CPU load and time consumption. Experimental results carried out with mobile robots in indoor environments illustrate the accuracy of our implementation and the low consumption of time and CPU load of the mapping system.


title: Inhibition of macrophage nuclear factor-kappaB leads to a dominant anti-inflammatory phenotype that attenuates glomerular inflammation in vivo.

abstract: Infiltrating macrophages (mphi) can cause injury or facilitate repair, depending on how they are activated by the microenvironment. Studies in vitro have defined the roles of individual cytokines and signaling pathways in activation, but little is known about how macrophages integrate the multiple signals they receive in vivo. We inhibited nuclear factor-kappaB in bone marrow-derived macrophages (BMDMs) by using a recombinant adenovirus expressing dominant-negative IkappaB (Ad-IkappaB). This re-orientated macrophage activation so they became profoundly anti-inflammatory in settings where they would normally be classically activated. In vitro, the lipopolysaccharide-induced nitric oxide, interleukin-12, and tumor necrosis factor-alpha synthesis was abrogated while interleukin-10 synthesis increased. In vivo, fluorescently labeled BMDMs transduced with Ad-IkappaB and injected into the renal artery significantly reduced inducible nitric oxide synthase and MHC class II expression when activated naturally in glomeruli of rats with nephrotoxic nephritis. Furthermore, although they only comprised 15% of glomerular macrophages, their presence significantly reduced glomerular infiltration and activation of host macrophages. Injury in nephrotoxic nephritis was also decreased when assessed morphologically and by severity of albuminuria. The results demonstrate the power of Ad-IkappaB-transduced BMDMs to inhibit injury when activated by acute immune-mediated inflammation within the glomerulus.


title: Driver fatigue monitoring system using Support Vector Machines

abstract: Driver fatigue is one of the leading causes of traffic accidents. This paper presents a real-time non-intrusive fatigue monitoring system which exploits the driver's facial expression to detect and alert fatigued drivers. The presented approach adopts the Viola-Jones classifier to detect the driver's facial features. The correlation coefficient template matching method is then applied to derive the state of each feature on a frame by frame basis. A Support Vector Machine (SVM) is finally integrated within the system to classify the facial appearance as either fatigued or otherwise. Using this simple and cheap implementation, the overall system achieved an accuracy of 95.2%, outperforming other developed systems employing expensive hardware to reach the same objective.


ASQ: Asking Significant Questions. Alternative Tools for Information Need and Accountability Assessments by Libraries.


title: Psychological Needs as a Predictor of Cyber Bullying: A Preliminary Report on College Students.

abstract: Abstract Recent surveys show that cyber bullying is a pervasive problem in North America. Many news stories have reported cyber bullying incidents around the world. Reports on the prevalence of cyber bullying and victimization as a result of cyber bullying increase yearly. Although we know what cyber bullying is it is important that we learn more about the psychological effects of it. Therefore, the aim of the current study is to investigate the relationship between psychological needs and cyber bullying. Participants of the study included 666 undergraduate students (231 males and 435 females) from 15 programs in the Faculty of Education at Selcuk University, Turkey. Questions about demographics, engagement in and exposure to cyber bullying, and the Adjective Check List were administered. 22.5% of the students reported engaging in cyber bullying at least one time, and 55.3% of the students reported being victims of cyber bullying at least once in their lifetime. Males reported more cyber bullying behavior than females. Results indicate that aggression and succorance positively predict cyber bullying wheras intraception negatively predict it. In addition, endurance and affiliation negatively predict cyber victimization. Only the need for change was found as a positive, but weak predictor of cyber victimization. In light of these findings, aggression and intraception should be investigated further in future research on cyber bullying. Key Words Cyber bullying, Cyber victims, Psychological Needs, College Students Over the past decade, technology has become increasingly important in the lives of adolescents. Adolescents are heavy users of electronic communication such as instant messaging, e-mail, and text messaging. They are also heavy users of communication-oriented internet sites such as blogs, social networking, and sites for sharing photos and videos (Subrahmanyam, & Greenfield, 2008). The internet offers connectivity to friends and family and access to important information. However, as with other social environments, the potential to meet and interact with others in harmful ways exists (Ybarra, Diener-West, & Leaf, 2007). One such interaction of growing concern is cyber bullying (Hinduja & Patchin, 2008). Cyber bullying has recently emerged as a new form of bullying and harassment. Cyber bullying is defined as "an individual or a group willfully using information and communication involving electronic technologies to facilitate deliberate and repeated harassment or threat to another individual or group by sending or posting cruel text and/or graphics using technological means" (Belsey, 2008; Berson, Berson, & Ferron, 2002; Finkelhor, Mitchell, & Wolak, 2008; Mason, 2008; Patchin, & Hinduja, 2006; Willard, 2007; Ybarra, & Mitchell, 2004a, 2004b). Recent surveys show that cyber bullying is a pervasive problem in North America (Agatston, Kowalski, & Limber, 2007; Beran and Li, 2005; Ybarra, & Mitchell, 2007) and many news stories have reported cyber bullying incidents all over the world (Aricak, Siyahhan, Uzunhasanoglu, Saribeyoglu, Ciplak, Yilmaz & Memmedov, 2008; Li, 2007a; Slonje, & Smith, 2008; Smith et al., 2008). Reports on the prevalence of cyber bullying and victimization have been increasing regularly every year. Finkelhor, Mitchell, and Wolak reported that six percent of young people were exposed to cyber bullying (threats, rumors, or other offensive behavior) during the past year. Six and a half percent of young, regular internet users in Ybarra's study reported at least one form of cyber bullying in the previous year. In Patchin and Hinduja's (2006) study, almost 30% of the adolescent respondents reported that they had been victims of cyberbullying-operationalized as having been ignored, disrespected, called names, threatened, picked on, or made fun of or having had rumors spread by others (Patchin, & Hinduja, 2006). In a study conducted in 2007 with an online panel of youth ages 13 to 17 years old, 43% had experienced cyber bullying in the past year, defined as "use of the internet, cell phones, or other technology to send or post text or images intended to hurt or embarrass another person" (Wolak, Mitchell, & Finkelhor, 2007). …


title: A Microstrip Ultra-Wideband Bandpass Filter With Cascaded Broadband Bandpass and Bandstop Filters

abstract: This paper develops a novel ultra-wideband bandpass filter by cascading a broadband bandpass filter with another broadband bandstop filter. Properly selected impedances of transmission lines achieve broadband bandpass and bandstop filters and make independent designs possible. Detailed design and synthesis procedures are provided; moreover, agreement between measured and theoretically predicted results demonstrates feasibility of the proposed filter. Due to its simple structure, the ultra-wideband bandpass filter newly introduced in this paper is suitable for integration in the single-chipped circuit or implementation on printed circuit boards.


Robust Ellipse Detection by Fitting Randomly Selected Edge Patches


title: DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks

abstract: This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework.


title: Key success factors for implementing Business Intelligence in South African public sector organisations

abstract: Intelligence (BI) has been rated as a key application and technology investment which provides organisations with great value by improving their decision making processes. The public sector provides a case for implementing BI for improved decision making processes as a way of enhancing its service delivery. However, the implementation of BI in these organisations have revealed to be quite a complex task to undertake. This research paper sets out to explore the implementation of BI in the public sector in South Africa. The research was conducted through two case studies and data was collected by conducting semi-structured interviews and document collection with organisations that are implementing BI. A qualitative thematic analysis method was then used to construct the major themes that emerged from the data. The study revealed that BI can be used as an enabler of change and improvement in public sector activities. Consolidating structures, systems and processes was identified as a precursor to implementing it, while the use of the BI initiative to support organisational strategic objectives was seen as ensuring executive buy-in. However, the level of skills needed to use BI tools was highlighted as key factor in hindering its use in these organisations. ICT has furthermore more been identified as being an important factor for the promotion of development and equitable access to public services.


title: Locally Linear Regression for Pose-Invariant Face Recognition

abstract: The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.


Entropy-based Subspace Clustering for Mining Numerical


title: Circuits and techniques for high-resolution measurement of on-chip power supply noise

abstract: This paper presents a technique for characterizing the statistical properties and spectrum of power supply noise using only two on-chip low-throughput samplers. The samplers utilize a voltage-controlled oscillator to perform high-resolution analog-to-digital conversion with minimal hardware. The measurement system is implemented in a 0.13-/spl mu/m process along with a high-speed link transceiver. Measured results from this chip validate the accuracy of the measurement system and elucidate several aspects of power supply noise, including its cyclostationary nature.


title: A fuzzy classification system for prediction of the results of the basketball games

abstract: Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of the KEEL system. Feature selection algorithms are applied and a majority voting is used in order to select the most representative features.


title: Formal semantics and interpreters in a principles of programming languages course

abstract: Most junior-senior level programming languages courses approach the subject either from the point-of-view of principles (concepts) of programming languages or from the perspective of understanding languages through writing progressively more complex interpreters. In this paper we show how to use formal semantics in a series of interpreter assignments in a principles or concepts-based course. The interpreter assignments make the semantics more concrete for students while providing a deeper understanding of concepts.


title: Beyond flat surface computing: challenges of depth-aware and curved interfaces

abstract: In the past decade, multi-touch-sensitive interactive surfaces have transitioned from pure research prototypes in the lab, to commercial products with wide-spread adoption. One of the longer term visions of this research follows the idea of ubiquitous computing, where everyday surfaces in our environment are made interactive. However, most of current interfaces remain firmly tied to the traditional flat rectangular displays of the today's computers and while they benefit from the directness and the ease of use, they are often not much more than touch-enabled standard desktop interfaces. In this paper, we argue for explorations that transcend the traditional notion of the flat display, and envision interfaces that are curved, three-dimensional, or that cross the boundary between the digital and physical world. In particular, we present two research directions that explore this idea: (a) exploring the three-dimensional interaction space above the display and (b) enabling gestural and touch interactions on curved devices for novel interaction possibilities. To illustrate both of these, we draw examples from our own work and the work of others, and guide the reader through several case studies that highlight the challenges and benefits of such novel interfaces. The implications on media requirements and collaboration aspects are discussed in detail, and, whenever possible, we highlight promising directions of future research. We believe that the compelling application design for future non-flat user interfaces will greatly depend on exploiting the unique characteristics of the given form factor.


title: Analysis and Application of an On-Package Planar Inverted-F Antenna

abstract: An on-package planar inverted-F antenna (PIFA) is described. The on-package PIFA consists of a single folded metal plate and has several advantages, including small size, light weight, low cost, and ease of fabrication. The antenna radiation pattern is omnidirectional in the H-plane. The coupling effect between the on-package PIFA and the radio-frequency (RF) components in the shielding package was studied. The antenna performance rarely changes and the isolation between the antenna and the RF components can be maximized when the locations of the components are appropriately arranged in the package. Finally, a wireless local-area network (WLAN) front-end module (FEM) including the switch, low-pass filter (LPF), bandpass filter (BPF), and power amplifier (PA), is embedded into the shielding package of the antenna. The error vector magnitude (EVM) of the resultant antenna integrated FEM, together with a WLAN card containing the baseband/medium-access control (MAC) circuitry, is tested. Good performance is obtained, showing the usability of the proposed antenna configuration.


title: City-Level Traffic Flow Prediction via LSTM Networks

abstract: Along with the rapid development of modern urban cities and the increasing number of vehicles, they also cause severe traffic problems. They trouble citizens a lot in wasting time and gas energy, leading to their bad temper during work, and even causing traffic accidents. Thus, monitoring the traffic flow is a critical portion in building intelligent transportation systems for urban cities. In this paper, we aim to predict traffic flow with big traffic data in urban cities. Our intuition is that although the traffic information is influenced by many factors and precise traffic modeling is difficult, we use the machine learning techniques to analyze the historical traffic data and train the prediction model. Particularly, we build a LSTM model with different dimensions of historical data, and then make prediction almost in real time for current traffic flows. We have built a prototype and tested it with two benchmark dataset. Experiment results show that our system obtains high prediction accuracy compared with the ground truth.


title: Polymer Microwave Fibers: A blend of RF, copper and optical communication

abstract: This paper discusses some of the recent advances and challenges that lie ahead for Polymer Microwave Fibers (PMF). PMF is a communication concept that combines mm-wave chips, metal couplers and cheap plastic fibers. It has some unique benefits over copper wireline and optical which can give it a preferred solution for low-cost, low-weight robust high-speed data-communication such as automotive and industrial Ethernet, consumer-oriented connectivity, signal distribution and more.


title: Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation

abstract: Convolutional networks optimized for accuracy on challenging, dense prediction tasks are prohibitively slow to run on each frame in a video. The spatial similarity of nearby video frames, however, suggests opportunity to reuse computation. Existing work has explored basic feature reuse and feature warping based on optical flow, but has encountered limits to the speedup attainable with these techniques. In this paper, we present a new, two part approach to accelerating inference on video. First, we propose a fast feature propagation technique that utilizes the block motion vectors present in compressed video (e.g. H.264 codecs) to cheaply propagate features from frame to frame. Second, we develop a novel feature estimation scheme, termed feature interpolation, that fuses features propagated from enclosing keyframes to render accurate feature estimates, even at sparse keyframe frequencies. We evaluate our system on the Cityscapes and CamVid datasets, comparing to both a frame-by-frame baseline and related work. We find that we are able to substantially accelerate segmentation on video, achieving near real-time frame rates (20.1 frames per second) on large images (960 x 720 pixels), while maintaining competitive accuracy. This represents an improvement of almost 6x over the single-frame baseline and 2.5x over the fastest prior work.


title: A System for Automated Detection of Ampoule Injection Impurities

abstract: Ampoule injection is a routinely used treatment in hospitals due to its rapid effect after intravenous injection. During manufacturing, tiny foreign particles can be present in the ampoule injection. Therefore, strict inspection must be performed before ampoule injections can be sold for hospital use. In the quality control inspection process, most ampoule enterprises still rely on manual inspection which suffers from inherent inconsistency and unreliability. This paper reports an automated system for inspecting foreign particles within ampoule injections. A custom-designed hardware platform is applied for ampoule transportation, particle agitation, and image capturing and analysis. Constructed trajectories of moving objects within liquid are proposed for use to differentiate foreign particles from air bubbles and random noise. To accurately classify foreign particles, multiple features including particle area, mean gray value, geometric invariant moments, and wavelet packet energy spectrum are used in supervised learning to generate feature vectors. The results show that the proposed algorithm is effective in classifying foreign particles and reducing false positive rates. The automated inspection system inspects over 150 ampoule injections per minute (versus $\sim {12}~{\rm ampoule~ injections ~per~ minute}$ by technologist) with higher accuracy and repeatability. In addition, the automated system is capable of diagnosing impurity types while existing inspection systems are not able to classify detected particles.


title: Performance analysis of DCO OFDM and DCO WOFDM systems in MMF optical links

abstract: This study compares the transmission performances of DC biased Optical Orthogonal Frequency Division Multiplexing (DCO OFDM) and DC biased Optical Wavelet based Orthogonal Frequency Division Multiplexing (DCO WOFDM) signals over Multi Mode Fiber (MMF) optical links. Mach Zehnder Modulator (MZM) was utilized as the optical modulator. The simulations were carried out using Optisystem software. The simulation results prove that the PSD of DCO WOFDM signal is more compatible with the channel frequency response compared to the PSD of DCO OFDM signal. This consequently results in considerable improvement in BER performance of DCO WOFDM system compared to the BER performance of DCO OFDM system as well as slight improvement in OSNR performance of DCO WOFDM signal in comparison with the OSNR performance of DCO OFDM signal. It should be considered that the occupied bandwidth by DCO WOFDM signal is twice the occupied bandwidth by DCO OFDM signal.


title: Clustering techniques: The user's dilemma

abstract: Abstract Numerous papers on clustering techniques and their applications in engineering, medical, and biological areas have appeared in pattern recognition literature during the past decade. This paper attempts to set some guidelines for a potential user of a clustering technique. We examine eight clustering programs which are representative of the various available techniques and compare their performances from several points of view. A formal comparative analysis is also performed with a portion of Munson's handprinted character data set. We believe that an understanding of the intrinsic characteristics of a clustering technique is essential to the intelligent application of the technique. Further, the output of a clustering program, along with whatever information a user may have about the data set, should be used together to form hypotheses about the structure of the data set.


title: Client-Side Defense Against Web-Based Identity Theft

abstract: Web spoofing is a significant problem involving fraudulent email and web sites that trick unsuspecting users into revealing private information. We discuss some aspects of common attacks and propose a framework for client-side defense: a browser plug-in that examines web pages and warns the user when requests for data may be part of a spoof attack. While the plugin, SpoofGuard, has been tested using actual sites obtained through government agencies concerned about the problem, we expect that web spoofing and other forms of identity theft will be continuing problems in


title: An Examination of Nonspeech Oral Motor Exercises for Children with Velopharyngeal Inadequacy

abstract: The velopharyngeal closure mechanism is the articulator that separates the oral and nasal cavities during speech and swallowing. Articulation and resonance may be adversely affected if velopharyngeal inadequacy (VPI) is present. VPI is generally corrected through surgery or speech prosthetics. There is, however, a small subset of clients who may improve with treatment using muscle rehabilitation procedures that are task specific to speech. Nonspeech oral motor exercise treatment has been used but found ineffective.


Detecting Plagiarism in Text Documents through Grammar-Analysis of Authors


Movie Recommendation with DBpedia


title: Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs

abstract: The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or highly generalize it (low precision). Striking a tradeoff between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method that produces simple process models with low branching complexity and consistently high and balanced fitness, precision and generalization, while achieving execution times 2-6 times faster than state-of-the-art methods on a set of 12 real-life logs. Further, our approach guarantees deadlock-freedom for cyclic process models and soundness for acyclic. Our proposal combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the directly-follows graph.


title: Efficient Trapdoor Hash Functions for Digital Signatures

abstract: By appending some bits to the original signature, a trapdoor hash function converts any signature scheme into secure signature scheme with very efficient online computation. Many of them have been proposed. The paper presents three new trapdoor hash functions with promotion in offline computation, online computation, appended bits, and the size of trapdoor key (but with longer public key). While the appended bits is shortened half (or more), the efficiency of online computation is doubled or more. These features make it suitable for mobile environment and real-time application.


title: Neural Generative Question Answering

abstract: This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.


title: Software Engineering Using Artificial Intelligence Techniques: Current State and Open Problems

abstract: Abstract : This paper surveys the application of artificial intelligence approaches to the software engineering processes. These approaches can have a major impact on reducing the time to market and improving the quality of software systems in general. Existing survey papers are driven by the AI techniques used, or are focused on specific software engineering processes. This paper relates AI techniques to software engineering processes specified by the IEEE 12207 standard of software engineering. The paper is driven by the activities and tasks specified in the standard for each software engineering process. The paper brings the state of the art of AI techniques closer to the software engineer, and highlights the open research problems for the research community. Keywords: Automated Software Engineering, Artificial Intelligence Techniques. 1. Introduction 2. The software intensive systems we develop these days are becoming much more complex in terms of the number of functional and nonfunctional requirements they need to support. The impact of low quality can also have a catastrophic impact on the mission of these systems in many critical applications. Moreover, the cost of software development dominates the total cost of such systems. Research in applying artificial intelligence techniques to software Engineering have grown tremendously in the last two decades producing a large number of projects and publications. A number of conferences and journals are dedicated to publish the research in this field. The AI techniques are proposed in order to reduce the time to market and enhance the quality of software systems. Yet many of these AI techniques remain largely used by the research community and with little impact on the processes and tools used by the practicing software engineer. The recent survey papers published in this field are mainly targeted to the research community. They are driven by the specific AI techniques used rather than the software engineering activities supported. They are also focused on a specific software engineering process such as software design [28] This survey paper attempts to close the gap between the research and practice of applying AI techniques to the software engineering processes. It also highlights open practical problems to the research community in applying such techniques by surveying the recently proposed work in this area. We use the terminology and the processes defined by the IEEE 12207 standard of software engineering. We then map the current state art of AI art techniques proposed in the literature to specific tasks and activities of some of the software processes define by the 12207 standard. These AI techniques attempt to automate or semi-automate these tasks and produce optimal or semi-optimal solutions in much less time. . The paper is organized as follows. In section 2, we give an overview of the IEEE 12207 standard of software engineering, and describe the most important AI techniques. We survey the current AI techniques proposed for the primary processes of development in sections 3. We highlight the open problems in section 4.


title: Design and implementation of a PI-MPPT based Buck-Boost converter

abstract: A Proportional-Integral (PI) based Maximum Power Point Tracking (MPPT) control algorithm is proposed in this study where it is applied to a Buck-Boost converter. It is aimed to combine regular PI control and MPPT technique to enhance the generated power from photovoltaic PV) panels. The perturb and observe (P&O) technique is used as the MPPT control algorithm. The study proposes to reduce converter output oscillation owing to implemented MPPT control technique with additional PI observer. Furthermore aims to optimize output power using PI voltage mode closed-loop structure.


title: Towards a Reference Model for After-Sales Service Processes

abstract: In the last years, given the high market pressure and the increased competition in several industries, the search for new business opportunity is focusing on service activities. After-Sales (AS) service has become increasingly important as a source of differentiation and market share for manufacturers and resellers, as well as a strategic driver for customer’s retention. These changes often call for a new conceptual definition of the product-service bundle marketed to the final customer and for a thorough revision of the logistical and organizational configuration of the whole service chain. It comes out the necessity to design appropriate processes and to have a general and shared definition of their structure. Aim of the paper is to suggest a model which provides a common configuration of the assistance processes according to a framework that links the different typologies of assistance with the product service strategies offered by companies. Some case studies have been considered in order to validate the proposed framework and the model.


title: Iterative channel estimation using soft decision feedback

abstract: We present an iterative channel estimation scheme with soft decision feedback. In a block-by-block transmission system with a soft-output equalizer and/or decoder it is possible to feed back the decisions to the channel estimator to improve its performance. We propose to feed back soft decisions which will reduce error propagation. We also derive a suboptimal estimator with substantially lower complexity and present simulations for a GSM-like system. The improvement of the bit-error rate over non-iterative schemes with this low complexity estimator is about 0.8 dB (equalizer feedback) and 1.5 dB (decoder feedback).


title: An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation

abstract: In this paper, we propose a novel domain adaptation method named "mixed fine tuning" for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.


title: Airport Security Screener Competency: A Cross-Sectional and Longitudinal Analysis

abstract: The performance of 5,717 aviation security screeners in detecting prohibited items in x-ray images of passenger bags was analyzed over a period of 4 years. The measure used in this study was detection performance on the X-Ray Competency Assessment Test (X-Ray CAT) and performance on this measure was obtained on an annual basis. Between tests, screeners performed varying amounts of training in the task of prohibited item detection using an adaptive computer-based training system called X-Ray Tutor (XRT). For both XRT and X-Ray CAT, prohibited items are categorized into guns, knives, improvised explosive devices (IEDs), and other prohibited items. Cross-sectional and longitudinal analyses of the training and test data were performed. Both types of analysis revealed a strong improvement of X-Ray CAT performance as a result of XRT training for all 4 item categories. The results of the study indicate that screener competency in detecting prohibited items in x-ray images of passenger bags can be greatly improve...


Making business sense of the Internet


title: Scaling up fast evolutionary programming with cooperative coevolution

abstract: Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. However, most analytical and experimental results on EP have been obtained using low-dimensional problems. It is interesting to know whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. It was discovered that neither classical EP (CEP) nor fast EP (FEP) performed satisfactorily for some large-scale problems. The paper shows empirically that FEP with cooperative coevolution (FEPCC) can speed up convergence rates on the large-scale problems whose dimension ranges from 100 to 1000. Cooperative coevolution adopts the divide-and-conquer strategy. It divides the system into many modules, and evolves each module separately and cooperatively. The results of FEPCC on the problems investigated here are something of a surprise. The time used by FEPCC to find a near optimal solution appears to scale linearly; that is, the time used seems to go up linearly as the dimensionality of the problems studied increases.


Mobile Robot Localization and Navigation in Artificial Intelligence: Survey


title: A Ka-band substrate-integrated waveguide diplexer with wide frequency spread

abstract: A compact 20 GHz/30 GHz diplexer for satellite communication systems is presented. It consists of a T-junction and two bandpass filters implemented in substrate-integrated waveguide (SIW) technology. The 30 GHz filter relies on a standard design, whereas a wide stopband approach is used for the 20 GHz filter to suppress a parasitic passband at the upper frequency. The design procedure is explained and simulation as well as measurement results are presented. The complete diplexer is fabricated in a standard PCB process. The measurement results validate the approach.


Clustering through decision tree construction


title: Recent advances in Doppler radar sensors for pervasive healthcare monitoring

abstract: This paper reviews recent advances in technologies using Doppler radar to detect heartbeat and respiration of a human subject. With contributions from many researchers in this field, new detection methods and system architectures have been proposed to improve the detection accuracy and robustness. The advantage of noncontact/covert detection has drawn interests on various applications. While many of the reported systems are bench-top prototypes for concept demonstration, several portable systems and integrated radar chips have been demonstrated. This paper reviews different architectures and discusses their potentials for integrated circuit implementation. Integrating the radar sensor on a chip allows the function of noncontact vital sign and vibration detection to be embedded in portable electronic equipment, like many other radio frequency (RF) devices. A radar sensor network is then feasible for pervasive monitoring in healthcare applications.


title: Mobility Aware Server Selection for Mobile Streaming Multimedia Content Distribution Networks

abstract: We propose a Content Delivery Network (CDN) with servers arranged hierarchically in multiple tiers. Lower-tier servers are topologically closer to the clients, and hence can deliver better QoS in terms of end-to-end delay and jitter. On the other hand, higher-tier servers have a larger coverage area and hence their clients incur fewer server handoffs. We present a server selection scheme that reduces the number of server handoffs while meeting differentiated QoS requirement for each client. The scheme dynamically estimates the client's residence time and uses a simple algorithm to assign clients to the appropriate tier. The scheme also caters for traditional server selection criteria, such as the expected QoS from the servers, bandwidth consumption, and the server load. We show through simulations that the scheme can achieve up to 15% reduction in handoffs, at the cost of minimal increases in delay and jitter while ensuring that clients of different QoS classes experience different delays.


title: WebMate: Generating Test Cases for Web 2.0

abstract: Web applications are everywhere—well tested web applications however are in short supply. The mixture of JavaScript, HTML and CSS in a variety of different browsers makes it virtually impossible to apply static analysis techniques. In this setting, systematic testing becomes a real challenge. We present a technique to automatically generate tests for Web 2.0 applications. Our approach systematically explores and tests all distinct functions of a web application. Our prototype implementation WEBMATE handles interfaces as complex as Facebook and is able to cover up to 7 times as much code as existing tools. The only requirements to use WEBMATE are the address of the application and, if necessary, user name and password.


title: Weakly supervised detection with decoupled attention-based deep representation

abstract: Training object detectors with only image-level annotations is an important problem with a variety of applications. However, due to the deformable nature of objects, a target object delineated by a bounding box always includes irrelevant context and occlusions, which causes large intra-class object variations and ambiguity in object-background distinction. For this reason, identifying the object of interest from a substantial amount of cluttered backgrounds is very challenging. In this paper, we propose a decoupled attention-based deep model to optimize region-based object representation. Different from existing approaches posing object representation in a single-tower model, our proposed network decouples object representation into two separate modules, i.e., image representation and attention localization. The image representation module captures content-based semantic representation, while the attention localization module regresses an attention map which simultaneously highlights the locations of the discriminative object parts and down weights the irrelevant backgrounds presented in the image. The combined representation alleviates the impact from the noisy context and occlusions inside an object bounding box. As a result, object-background ambiguity can be largely reduced and background regions can be suppressed effectively. In addition, the proposed object representation model can be seamlessly integrated into a state-of-the-art weakly supervised detection framework, and the entire model can be trained end-to-end. We extensively evaluate the detection performance on the PASCAL VOC 2007, VOC 2010 and VOC2012 datasets. Experimental results demonstrate that our approach effectively improves weakly supervised object detection.


title: Penile Dysmorphic Disorder: Development of a Screening Scale

abstract: Penile dysmorphic disorder (PDD) is shorthand for men diagnosed with body dysmorphic disorder, in whom the size or shape of the penis is their main, if not their exclusive, preoccupation causing significant shame or handicap. There are no specific measures for identifying men with PDD compared to men who are anxious about the size of their penis but do not have PDD. Such a measure might be helpful for treatment planning, reducing unrealistic expectations, and measuring outcome after any psychological or physical intervention. Our aim was, therefore, to validate a specific measure, termed the Cosmetic Procedure Screening Scale for PDD (COPS-P). Eighty-one male participants were divided into three groups: a PDD group (n = 21), a small penis anxiety group (n = 37), and a control group (n = 23). All participants completed the COPS-P as well as standardized measures of depression, anxiety, social phobia, body image, quality of life, and erectile function. Penis size was also measured. The final COPS-P was based on nine items. The scale had good internal reliability and significant convergent validity with measures of related constructs. It discriminated between the PDD group, the small penis anxiety group, and the control group. This is the first study to develop a scale able to discriminate between those with PDD and men anxious about their size who did not have PDD. Clinicians and researchers may use the scale as part of an assessment for men presenting with anxiety about penis size and as an audit or outcome measure after any intervention for this population.


title: Comparison of five different rat models of peripheral nerve injury

abstract: Described here is a comparison of five peripheral sciatic nerve injury models in rats which all result in various degrees of neuropathic pain symptoms. They are the chronic constriction injury (CCI), the spinal nerve ligation (SNL), the partial sciatic ligation (PSL), the tibial and sural transection (TST), and the complete sciatic transection (CST) model. Behavioral testing was performed on these models over a 56-day period under strict experimental conditions to minimise variability between the surgical models and to allow for an accurate evaluation of the sensory deficits produced by each model. Overall, all five models of neuropathic pain produced signs of allodynia and hyperalgesia to various stimuli. However, the duration and magnitude of the evoked responses varied considerably between the different models.


title: SemRank: ranking complex relationship search results on the semantic web

abstract: While the idea that querying mechanisms for complex relationships (otherwise known as Semantic Associations) should be integral to Semantic Web search technologies has recently gained some ground, the issue of how search results will be ranked remains largely unaddressed. Since it is expected that the number of relationships between entities in a knowledge base will be much larger than the number of entities themselves, the likelihood that Semantic Association searches would result in an overwhelming number of results for users is increased, therefore elevating the need for appropriate ranking schemes. Furthermore, it is unlikely that ranking schemes for ranking entities (documents, resources, etc.) may be applied to complex structures such as Semantic Associations.In this paper, we present an approach that ranks results based on how predictable a result might be for users. It is based on a relevance model SemRank, which is a rich blend of semantic and information-theoretic techniques with heuristics that supports the novel idea of modulative searches, where users may vary their search modes to effect changes in the ordering of results depending on their need. We also present the infrastructure used in the SSARK system to support the computation of SemRank values for resulting Semantic Associations and their ordering.


title: Cinematic Visual Discourse: Representation, Generation, and Evaluation

abstract: In this paper, we present the design, implementation, and evaluation of an end-to-end camera planning system called Darshak. Darshak automatically constructs cinematic narrative discourse of a given story in a 3-D virtual environment. It utilizes a hierarchical partial-order causal link (POCL) planning algorithm to generate narrative plans that contain story events and camera directives for filming them. Dramatic situation patterns, commonly used by writers of fictional narratives, are formalized as communicative plan operators that provide a basis for structuring the cinematic content of the story's visualization. The dramatic patterns are realized through abstract communicative operators that represent operations on a viewer's beliefs about the story and its telling. Camera shot compositions and transitions are defined in this plan-based framework as execution primitives. Darshak's performance is evaluated through a novel user study based on techniques used to evaluate existing cognitive models of narrative comprehension. Initial study reveals significant effect of the choice of visualization strategies on measured viewer comprehension. It further shows significant effect of Darshak's choice of visualization strategy on comprehension.


title: Statistical Parametric Speech Synthesis Using Generalized Distillation Framework

abstract: This letter proposes an improved statistical parametric speech synthesis (SPSS) method which utilizes auxiliary information for acoustic modeling under generalized distillation framework. In conventional SPSS, acoustic models are trained using context features as input and acoustic features as output. In our proposed method, two acoustic models, so-called teacher and student, are involved. Both of them are recurrent neural networks (RNN) with bidirectional long short-term memory (BLSTM) units. The teacher, which aims to provide the student with additional knowledge, employs auxiliary features (e.g., articulatory features or spectra of short-time Fourier transform) in addition to the conventional input and output of acoustic models. The student, which serves as the final acoustic model for synthesis, adopts a multitask learning architecture which uses the outcome of the teacher as the target of its secondary task. Experimental results show that this method can achieve better accuracy of acoustic feature prediction and produce more natural synthetic speech than conventional BLSTM-RNN-based acoustic modeling with single-task or multitask learning.


title: A substrate integrated matched load with embedded resistive thick film

abstract: A matched load in substrate integrated waveguide (SIW) technology is presented at K-band. An embedded resistive thick film is used to absorb the input signal. The proposed structure is fully compatible with standard multilayer printed circuit board processes. The realized SIW termination exhibits a bandwidth of 2.75 GHz with a return loss of more than 20 dB around 18.4 GHz.


title: User-transparent Distributed TensorFlow

abstract: Deep Learning (DL) algorithms have become the {\em de facto} choice for data analysis. Several DL implementations -- primarily limited to a single compute node -- such as Caffe, TensorFlow, Theano and Torch have become readily available. Distributed DL implementations capable of execution on large scale systems are becoming important to address the computational needs of large data produced by scientific simulations and experiments. Yet, the adoption of distributed DL implementations faces significant impediments: 1) most implementations require DL analysts to modify their code significantly -- which is a show-stopper, 2) several distributed DL implementations are geared towards cloud computing systems -- which is inadequate for execution on massively parallel systems such as supercomputers. ::: This work addresses each of these problems. We provide a distributed memory DL implementation by incorporating required changes in the TensorFlow runtime itself. This dramatically reduces the entry barrier for using a distributed TensorFlow implementation. We use Message Passing Interface (MPI) -- which provides performance portability, especially since MPI specific changes are abstracted from users. Lastly -- and arguably most importantly -- we make our implementation available for broader use, under the umbrella of Machine Learning Toolkit for Extreme Scale (MaTEx) at {\texttt this http URL}. We refer to our implementation as MaTEx-TensorFlow.


title: Single-accelerometer-based daily physical activity classification

abstract: In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers’ intervention. For the purpose of assessing customers’ daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of ~80%, which was comparable with that obtained by a Decision Tree classifier.


Efficient Hierarchical Embedding for Learning Coherent Visual Styles


title: Business Process Outsourcing: an event study on the nature of processes and firm valuation

abstract: Business Process Outsourcing (BPO) is a phenomenon that is rapidly increasing in both incidence and importance. This study empirically examines the value proposition of BPO with respect to the nature of the processes being outsourced. Using the event study methodology, we employ the value chain (VC) position and existing ownership of a business process as our primary independent variables, and the stock abnormal return in response to the BPO announcement as the dependent performance variable in our research model. The study was conducted on 298 BPO announcements from 1998 to 2005. Results support the argument that outsourcing is valuable for both primary and supportive business processes. However, we found that BPO announcements on primary processes yield higher abnormal returns than supportive processes. Although existing process ownership was not found to be a powerful differentiator for BPO performance, its interaction with VC position provides important insights into the timing of outsourcing. The evidence suggests that internal cultivation of processes is important for BPO success, particularly when BPO is applied to primary processes.


title: The mental prosthesis: Assessing the speed of a p300-based brain-computer interface

abstract: Describes a study designed to assess a brain-computer interface (BCI), originally described by Farwell and Donchin in 1988. The system utilizes the fact that the rare events in the oddball paradigm elicit the P300 component of the event-related potential (ERP). The BCI presents the user with a matrix of 6 by 6 cells, each containing one letter of the alphabet. The user focuses attention on the cell containing the letter to be communicated while the rows and the columns of the matrix are intensified. Each intensification is an event in the oddball sequence, the row and the column containing the attended cell are "rare" items and, therefore, only these events elicit a P300. The computer thus detects the transmitted character by determining which row and which column elicited the P300. The authors report an assessment, using a bootstrapping approach, which indicates that an off line version of the system can communicate at the rate of 7.8 characters a minute and achieve 80% accuracy. The system's performance in real time was also assessed. The authors' data indicate that a P300-based BCI is feasible and practical. However, these conclusions are based on tests using healthy individuals.


title: The use of phrases and structured queries in information retrieval

abstract: Both phrases and Boolean queries have a long history in information retrieval, particularly in commercial systems. In previous work, Boolean queries have been used as a source of phrases for a statistical retrieval model, This work, like the majority of research on phrases, resulted in little improvement in retrieval effectiveness, In this paper, we describe an approach where phrases identified in natural language queries are used to build structured queries for a probabilistic retrieval model. Our results show that using phrases in this way can improve performance, and that phrases that are automatically extracted from a natural language query perform nearly as well as manually selected phrases.


title: Wideband Beam-Scanning Circularly Polarized Inclined Slots Using Ridge Gap Waveguide

abstract: A wideband circularly polarized (CP) frequency-dependent slot antenna array is designed for beam scanning using the ridge gap waveguide (RGW) technology. The merge between both the printed and the RGW technologies for providing a wideband CP antenna array is newly implemented here. The designed CP antenna has been fabricated, and measurement results are provided and compared to the full-wave analysis. A measured antenna axial ratio (AR) and impedance bandwidths of ~ 21% are achieved from 12.5 to 15.5 GHz. The proposed antenna preserves its CP characteristics of AR below 3 dB while achieving continuous frequency beam scanning from backward to forward directions including broadside radiation that covers +15° to -15° with an average gain of 12.5 dBi.


title: Electrical Energy Storage for the Grid: A Battery of Choices

abstract: The increasing interest in energy storage for the grid can be attributed to multiple factors, including the capital costs of managing peak demands, the investments needed for grid reliability, and the integration of renewable energy sources. Although existing energy storage is dominated by pumped hydroelectric, there is the recognition that battery systems can offer a number of high-value opportunities, provided that lower costs can be obtained. The battery systems reviewed here include sodium-sulfur batteries that are commercially available for grid applications, redox-flow batteries that offer low cost, and lithium-ion batteries whose development for commercial electronics and electric vehicles is being applied to grid storage.