title: Design and Implementation of Three-Phase Two-Stage Grid-Connected Module Integrated Converter

abstract: Module integrated converters (MICs) in single phase have witnessed recent market success due to unique features such as improved energy harvest, improved system efficiency, lower installation costs, plug-and-play operation, and enhanced flexibility and modularity. The MIC sector has grown from a niche market to mainstream, especially in the United States. Assuming further expansion of the MIC market, this paper presents the microinverter concept incorporated in large size photovoltaic (PV) installations such as megawatts (MW)-class solar farms where a three-phase ac connection is employed. A high-efficiency three-phase MIC with two-stage zero voltage switching (ZVS) operation for the grid-tied PV system is proposed which will reduce cost per watt, improve reliability, and increase scalability of MW-class solar farms through the development of new solar farm system architectures. The first stage consists of a high-efficiency full-bridge LLC resonant dc-dc converter which interfaces to the PV panel and produces a dc-link voltage. A center points iteration algorithm developed specifically for LLC resonant topologies is used to track the maximum power point of the PV panel. The second stage is comprised of a three-phase dc-ac inverter circuit which employs a simple soft-switching scheme without adding auxiliary components. The modeling and control strategy of this three-phase dc-ac inverter is described. Because the dc-link capacitor plays such an important role for dual-stage MIC, the capacitance calculation is given under type D voltage dip conditions. A 400-W prototype was built and tested. The overall peak efficiency of the prototype was measured and found to be 96% with 98.2% in the first stage and 98.3% in the second stage.


title: Symbolic Decision Procedures for QBF

abstract: Much recent work has gone into adapting techniques that were originally developed for SAT solving to QBF solving. In particular, QBF solvers are often based on SAT solvers. Most competitive QBF solvers are search-based. In this work we explore an alternative approach to QBF solving, based on symbolic quantifier elimination. We extend some recent symbolic approaches for SAT solving to symbolic QBF solving, using various decision-diagram formalisms such as OBDDs and ZDDs. In both approaches, QBF formulas are solved by eliminating all their quantifiers. Our first solver, QMRES, maintains a set of clauses represented by a ZDD and eliminates quantifiers via multi-resolution. Our second solver, QBDD, maintains a set of OBDDs, and eliminate quantifier by applying them to the underlying OBDDs. We compare our symbolic solvers to several competitive search-based solvers. We show that QBDD is not competitive, but QMRES compares favorably with search-based solvers on various benchmarks consisting of non-random formulas.


Depth-Gated Recurrent Neural Networks


Building an Ontology of Boardgame Mechanics based on the BoardGameGeek Database and the MDA Framework


title: Solving system of non-linear equations using Genetic Algorithm

abstract: Solving system of non-linear equation is a general purpose problem that can be applied to real-time applications. Although there are several methods like Newton-Raphson method, secant method, bisection method, fixed-point iteration etc. evolutionary and soft computing methods optimize the space and time complexity to solve nonlinear systems. In this article, we present the Genetic Algorithm approach to solve nonlinear system of equations for travelling salesman problem, tank reactor system and neurophysiology application. The roots of nonlinear system using GA is estimated using population size, degree of mutation, crossover rate and coefficient size. Nonlinear parameters are analyzed and simulated for multi-dimensional NP hard travelling salesman problem, two dimensional tank reactor and neurophysiology application.


title: Dynamic window based approach to mobile robot motion control in the presence of moving obstacles

abstract: This paper presents a motion control method for mobile robots in partially unknown environments populated with moving obstacles. The proposed method is based on the integration of focused D* search algorithm and dynamic window local obstacle avoidance algorithm with some adaptations that provide efficient avoidance of moving obstacles. The moving obstacles are modelled as moving cells in the occupancy grid map and their motion is predicted by applying a procedure similar to the dynamic window approach. The collision points of the robot predicted trajectory and moving cells predicted trajectories form the new active obstacles in the environment, which should be avoided. The algorithms are implemented and verified using a Pioneer 3DX mobile robot equipped with laser range finder.


title: Optimization and architecture effects on GPU computing workload performance

abstract: It is unquestionable that successive hardware generations have significantly improved GPU computing workload performance over the last several years. Moore's law and DRAM scaling have respectively increased single-chip peak instruction throughput by 3X and off-chip bandwidth by 2.2X from NVIDIA's GeForce 8800 GTX in November 2006 to its GeForce GTX 580 in November 2010. However, raw capability numbers typically underestimate the improvements in real application performance over the same time period, due to significant architectural feature improvements. To demonstrate the effects of architecture features and optimizations over time, we conducted experiments on a set of benchmarks from diverse application domains for multiple GPU architecture generations to understand how much performance has truly been improving for those workloads. First, we demonstrate that certain architectural features make a huge difference in the performance of unoptimized code, such as the inclusion of a general cache which can improve performance by 2–4× in some situations. Second, we describe what optimization patterns have been most essential and widely applicable for improving performance for GPU computing workloads across all architecture generations. Some important optimization patterns included data layout transformation, converting scatter accesses to gather accesses, GPU workload regularization, and granularity coarsening, each of which improved performance on some benchmark by over 20%, sometimes by a factor of more than 5×. While hardware improvements to baseline unoptimized code can reduce the speedup magnitude, these patterns remain important for even the most recent GPUs. Finally, we identify which added architectural features created significant new optimization opportunities, such as increased register file capacity or reduced bandwidth penalties for misaligned accesses, which increase performance by 2× or more in the optimized versions of relevant benchmarks.


title: A Two-Stage Framework for Computing Entity Relatedness in Wikipedia

abstract: Introducing a new dataset with human judgments of entity relatedness, we present a thorough study of all entity relatedness measures in recent literature based on Wikipedia as the knowledge graph. No clear dominance is seen between measures based on textual similarity and graph proximity. Some of the better measures involve expensive global graph computations. We then propose a new, space-efficient, computationally lightweight, two-stage framework for relatedness computation. In the first stage, a small weighted subgraph is dynamically grown around the two query entities; in the second stage, relatedness is derived based on computations on this subgraph. Our system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. We also plug our relatedness algorithm into a state-of-the-art entity linker and observe an increase in its accuracy and robustness.


title: Dynamic noise aware training for speech enhancement based on deep neural networks.

abstract: We propose three algorithms to address the mismatch problem in deep neural network (DNN) based speech enhancement. First, we investigate noise aware training by incorporating noise informationin the testutterance with anideal binary maskbased dynamic noise estimation approach to improve DNN’s speech separation ability from the noisy signal. Next, a set of more than 100 noise types is adopted to enrich the generalization capabilities of the DNN to unseen and non-stationary noise conditions. Finally, the quality of the enhanced speech can further be improved by global variance equalization. Empirical results show that each of the three proposed techniques contributes to the performance improvement. Compared to the conventional logarithmic minimum mean squared error speech enhancement method, our DNN system achieves 0.32 PESQ (perceptual evaluation of speech quality) improvement across six signal-tonoise ratio levels ranging from -5dB to 20dB on a test set with unknown noise types. We also observe that the combined strategies can well suppress highly non-stationary noise better than all the competing state-of-the-art techniques we have evaluated. Index Terms: Speech enhancement, deep neural networks, noise aware training, ideal binary mask, non-stationary noise


title: Technology-based Financial Frauds in Taiwan: Issues and Approaches

abstract: Financial fraud or deception for personal gain had become one of the major crimes in society. The situation worsened by the advance and widely application of information technology (IT). In recent years, the number of financial frauds in Taiwan increased dramatically, from 2,817 in 1997 to 43,342 in 2005, which means a 15 fold increase in 8 years. Besides the fact that the transcendence and the anonymity of network allow fraudsters to acquire their victims effectively and to avoid the risk of apprehension, the IT-enabled platform also makes it much more efficient and effective for fraudsters to exchange information with their victims and to transfer financial data. Major types of financial frauds in Taiwan include: computer fraud, credit card fraud, auction fraud, e-commerce fraud, and telemarketing fraud. Modus operandi of financial frauds are depicted and current remedies are summarized. A framework for developing countermeasures is also proposed for future research.


title: A model based engineering tool for ROS component compositioning, configuration and generation of deployment information

abstract: Programming industrial robots requires experts -not only to create the robot applications, but also for changing them later due to different product requirements. Part of the reason for this is that all robot vendors provide their own robot programming language. Due to the language differences, robot applications cannot be reused for different robot types. Often, additional experts are required since one expert is trained only for a special robot type. This makes the use of robots uneconomical for small and medium sized enterprises. The ReApp project addresses this problem by providing a workbench based on ROS (Robot Operating System). A central part of this workbench is the skill and solution modeling tool, which allows the model-based design of robot applications composed of reusable components, and is described in this paper.


title: Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users

abstract: Privacy has been an enduring concern associated with commercial information technology (IT) applications, in particular regarding the issue of personalization. IT-enabled personalization, while potentially making the user computing experience more gratifying, often relies heavily on the user's personal information to deliver individualized services, which raises the user's privacy concerns. We term the tension between personalization and privacy, which follows from marketers exploiting consumers' data to offer personalized product information, the personalization--privacy paradox. To better understand this paradox, we build on the theoretical lenses of uses and gratification theory and information boundary theory to conceptualize the extent to which privacy impacts the process and content gratifications derived from personalization, and how an IT solution can be designed to alleviate privacy concerns. ::: ::: Set in the context of personalized advertising applications for smartphones, we propose and prototype an IT solution, referred to as a personalized, privacy-safe application, that retains users' information locally on their smartphones while still providing them with personalized product messages. We validated this solution through a field experiment by benchmarking it against two more conventional applications: a base nonpersonalized application that broadcasts non-personalized product information to users, and a personalized, nonprivacy safe application that transmits user information to a central marketer's server. The results show that (compared to the non-personalized application), while personalized, privacy-safe or not increased application usage (reflecting process gratification), it was only when it was privacy-safe that users saved product messages (reflecting content gratification) more frequently. Follow-up surveys corroborated these nuanced findings and further revealed the users' psychological states, which explained our field experiment results. We found that saving advertisements for content gratification led to a perceived intrusion of information boundary that made users reluctant to do so. Overall our proposed IT solution, which delivers a personalized service but avoids transmitting users' personal information to third parties, reduces users' perceptions that their information boundaries are being intruded upon, thus mitigating the personalization--privacy paradox and increasing both process and content gratification.


title: Deep Packet Inspection as a Service

abstract: Middleboxes play a major role in contemporary networks, as forwarding packets is often not enough to meet operator demands, and other functionalities (such as security, QoS/QoE provisioning, and load balancing) are required. Traffic is usually routed through a sequence of such middleboxes, which either reside across the network or in a single, consolidated location. Although middleboxes provide a vast range of different capabilities, there are components that are shared among many of them. A task common to almost all middleboxes that deal with L7 protocols is Deep Packet Inspection (DPI). Today, traffic is inspected from scratch by all the middleboxes on its route. In this paper, we propose to treat DPI as a service to the middleboxes, implying that traffic should be scanned only once, but against the data of all middleboxes that use the service. The DPI service then passes the scan results to the appropriate middleboxes. Having DPI as a service has significant advantages in performance, scalability, robustness, and as a catalyst for innovation in the middlebox domain. Moreover, technologies and solutions for current Software Defined Networks (SDN) (e.g., SIMPLE [41]) make it feasible to implement such a service and route traffic to and from its instances.


title: Enterprise to Computer: Star Trek chatbot

abstract: Human interactions and human-computer interactions are strongly influenced by style as well as content. Adding a persona to a chatbot makes it more human-like and contributes to a better and more engaging user experience. In this work, we propose a design for a chatbot that captures the "style" of Star Trek by incorporating references from the show along with peculiar tones of the fictional characters therein. Our Enterprise to Computer bot (E2Cbot) treats Star Trek dialog style and general dialog style differently, using two recurrent neural network Encoder-Decoder models. The Star Trek dialog style uses sequence to sequence (SEQ2SEQ) models (Sutskever et al., 2014; Bahdanau et al., 2014) trained on Star Trek dialogs. The general dialog style uses Word Graph to shift the response of the SEQ2SEQ model into the Star Trek domain. We evaluate the bot both in terms of perplexity and word overlap with Star Trek vocabulary and subjectively using human evaluators.


title: Systemic-attachment formulation for families of children with autism

abstract: Publisher policy: author can archive post-print on institutional repository after 12 months embargo. Must link to publisher version with DOI. Publisher's version/PDF cannot be used. Published source must be acknowledged.


title: BigDog , the Rough-Terrain Quadruped Robot

abstract: Abstract Less than half the Earth's landmass is accessible to existing wheeled and tracked vehicles. But people and animals using their legs can go almost anywhere. Our mission at Boston Dynamics is to develop a new breed of rough-terrain robots that capture the mobility, autonomy and speed of living creatures. Such robots will travel in outdoor terrain that is too steep, rutted, rocky, wet, muddy, and snowy for conventional vehicles. They will travel in cities and in our homes, doing chores and providing care, where steps, stairways and household clutter limit the utility of wheeled vehicles. Robots meeting these goals will have terrain sensors, sophisticated computing and power systems, advanced actuators and dynamic controls. We will give a status report on BigDog, an example of such rough-terrain robots.


title: Kolmogorov amplification from Bell correlation

abstract: It was first observed by John Bell that quantum theory predicts correlations between measurement outcomes that lie beyond the explanatory power of local hidden variable theories. These correlations have traditionally been studied extensively in the probabilistic framework. A drawback of this perspective is that one is then forced to use in a single argument the outcomes of mutually-exclusive measurements. One of us has initiated an alternative approach, invoking only data at hand, in order to circumvent this issue. In this factual view, which is based on Kol-mogorov complexity, we introduce mechanisms such as complexity amplification. We establish that this functionality is realizable, just as its probabilistic counterpart, hereby underlining that Bell correlations are a precious information-processing resource.


title: Rats and Humans Can Optimally Accumulate Evidence for Decision-Making

abstract: The gradual and noisy accumulation of evidence is a fundamental component of decision-making, with noise playing a key role as the source of variability and errors. However, the origins of this noise have never been determined. We developed decision-making tasks in which sensory evidence is delivered in randomly timed pulses, and analyzed the resulting data with models that use the richly detailed information of each trial’s pulse timing to distinguish between different decision-making mechanisms. This analysis allowed measurement of the magnitude of noise in the accumulator’s memory, separately from noise associated with incoming sensory evidence. In our tasks, the accumulator’s memory was noiseless, for both rats and humans. In contrast, the addition of new sensory evidence was the primary source of variability. We suggest our task and modeling approach as a powerful method for revealing internal properties of decision-making processes.


title: Neonatal seizure detection using convolutional neural networks

abstract: This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous unedited multi-channel neonatal EEG totaling 835 hours and comprising of 1389 seizures. The proposed deep architecture, with sample-level filters, achieves an accuracy that is comparable to the state-of-the-art SVM-based neonatal seizure detector, which operates on a set of carefully designed hand-crafted features. The fully convolutional architecture allows for the localization of EEG waveforms and patterns that result in high seizure probabilities for further clinical examination.


Diagnostic imaging of the liver, biliary tract and pancreas. Data analysis and diagnostic procedures


title: REVIEW: EVOLUTION Humans as the World’s Greatest Evolutionary Force

abstract: In addition to altering global ecology, technology and human population growth also affect evolutionary trajectories, dramatically accelerating evolutionary change in other species, especially in commercially important, pest, and disease organisms. Such changes are apparent in antibiotic and human immunodeficiency virus (HIV) resistance to drugs, plant and insect resistance to pesticides, rapid changes in invasive species, life-history change in commercial fisheries, and pest adaptation to biological engineering products. This accelerated evolution costs at least $33 billion to $50 billion a year in the United States. Slowing and controlling arms races in disease and pest management have been successful in diverse ecological and economic systems, illustrating how applied evolutionary principles can help reduce the impact of humankind on evolution.


title: Comparison of PCA, LDA and GDA for palmprint verification

abstract: In this paper, we have compared use of PCA (Principal components analysis) with two powerful feature extraction techniques LDA (Linear discriminant analysis) GDA (Generalized discriminant analysis) which have already been used in palmprint verification. For testing purpose 10 colorful whole-hand images of each hand of 43 persons are collected by a digital camera, namely, a small dataset of 860 images is built. The experimental results show that the best verification result is obtained with the GDA based method, whose average minimal total error rate is only 0.11% on the dataset.


title: Seeing, Acting, Understanding: Motor Resonance in Language Comprehension

abstract: Observing actions and understanding sentences about actions activates corresponding motor processes in the observer-comprehender. In 5 experiments, the authors addressed 2 novel questions regarding language-based motor resonance. The 1st question asks whether visual motion that is associated with an action produces motor resonance in sentence comprehension. The 2nd question asks whether motor resonance is modulated during sentence comprehension. The authors' experiments provide an affirmative response to both questions. A rotating visual stimulus affects both actual manual rotation and the comprehension of manual rotation sentences. Motor resonance is modulated by the linguistic input and is a rather immediate and localized phenomenon. The results are discussed in the context of theories of action observation and mental simulation. What kind of perturbation in people's minds and brains does hearing or reading a sentence like "Eric turned down the volume" bring about? Classical cognitive science assumes that this will lead to the activation of abstract representations in long-term memory that will be integrated into a network representing the meaning of the sentence. The physical action of manual rotation is not part of such an abstract representation. A completely different answer to the question is inspired by current research on action observation and understanding. In contrast to the classical cognitive view, this new view predicts that the example sentence will activate a motor program for (counterclockwise) manual rotation in the listener or reader. The rationale for this prediction lies in the phenomenon of motor resonance. When people observe someone else perform an action, the neural substrates are recruited that are active when they are performing that action themselves. Motor resonance has been observed in a wide range of studies and has been the focus of ideomotor theories (Greenwald, 1970; James, 1890; Jeannerod, 1994; Prinz, 1997). Many studies of motor resonance have been inspired by the recent discovery of so-called mirror neurons in the ventral premotor cortex of the macaque monkey (e.g., Gallese, Fadiga, Foggassi, & Rizzolatti, 1996). Single-cell recordings of neurons in the macaque monkey ventral premotor cortex fire when the monkey observes an action being performed that it also has in its own action repertoire (e.g., grasping a food item). These neu- rons have been termed mirror neurons. Mirror neurons have also been shown to fire when the monkey hears a sound associated with an action in its repertoire, for instance, cracking a nut (Kohler et al., 2002). It is important to note that mirror neurons have been shown to be responsive to an understanding of the goal of an action. When the monkey knew there was food behind a screen, its mirror neurons responded when the experimenter's hand moved toward the food, even though it disappeared behind a screen. The activa- tion pattern was similar to a condition without the screen; some mirror neurons responded equally strongly in both conditions, whereas others responded more strongly in the full vision condi- tion. In contrast, this pattern of activation was not shown, with or without screen, if there was no food, but the experimenter made the same grasping movement (Umiltaet al., 2001). Thus, having a mental representation of the goal of a grasping action seems both necessary and sufficient for mirror neuron activation.


title: Prevention of violence against women and girls: lessons from practice

abstract: This Series paper describes programming to prevent violence against women and girls, and emphasises the importance of systematic, sustained programming across the social ecology (ie, the delicate equilibrium of interacting social, institutional, cultural, and political contexts of people’s lives) to transform gender-power inequalities. Effective prevention policy and programming is founded on five core principles: first, analysis and actions to prevent violence across the social ecology (individual, interpersonal, community, and societal); second, intervention designs based on an intersectional gender-power analysis; third, theory-informed models developed on the basis of evidence; fourth, sustained investment in multisector interventions; and finally, aspirational programming that promotes personal and collective thought, and enables activism on women’s and girls’ rights to violence-free lives. Prevention programming of the future will depend on all of us having a vision of, and a commitment to, gender equality to make violence-free lives for women and girls a reality.


title: Synthesis of Some Mono-Mannich Bases and Corresponding Azine Derivatives and Evaluation of their Anticonvulsant Activity

abstract: Mono-Mannich bases, 3-amino-1-aryl-1-propanone hydrochlorides (Ig1-Ig4), and their corresponding azine derivatives, N,N'-bis(3- amino-1-aryl-propylidene) hydrazine dihydrochlorides (D1-D4), were synthesized and their anticonvulsant activities were evaluated. Alterations in biological activity depending on modifications in chemical structure were also followed. The aryl part was phenyl in Ig1, D1, Ig2, D2, Ig3, D3, or p-hydroxyphenyl in Ig4, and D4. The amine part was dimethylamine in Ig1, D1, Ig4, and D4, piperidine in Ig2, D2 or morpholine in Ig3, D3. Compounds D2, D3, and D4 are new. The anticonvulsant activity was determined by maximal electroshock (MES) and subcutaneous metrazole (pentetrazol; scMet) tests. The rotorod toxicity test was used for determining neurological deficits. While the compounds were not effective in scMet, they were found to exert protective effect in MES. The results of MES are as follows: Compound [dose level (mg/kg), time (h)]: Ig1 [30 (0.5 h), 100 (0.5 h)]; Ig2 [30 (0.5 h, 4 h)]; Ig3 [30 (0.5 h), 100 (0.5 h), 300 (0.5 h, 4 h)]; Ig4 [300 (0.5 h, 4 h), 100 (4 h)]; D1 [30 (0.5 h)]; D3 [100 (0.5 h,4 h), 300 (0.5 h), 30 (4 h)]]; D4 [300 (0.5 h, 4 h)]. D2 did not show any anticonvulsant activity in both tests. Ig1, Ig2, D1, D2, and D3 exhibited neurotoxicity. Compounds Ig2, D1, and D2 were neurotoxic at 100 mg/kg dose level at 0.5 h. Ig1 was neurotoxic at 300 mg at 0.5 h, D3 was neurotoxic at 300 mg at 4 h. Conversion of mono-Mannich bases to their corresponding azine derivatives generally decreased the anticonvulsant activity. Ig3, Ig4 and D4 seem to be promising candidates to develop new anticonvulsant compounds for grand mal epilepsy for further synthesis and in vivo studies, since they were effective in MES screening and no neurotoxicity was observed with them.


title: ON THE EMERGENCE OF SHADOW IT - A TRANSACTION COST-BASED APPROACH

abstract: Information Technology (IT) used for business processes is not only provided by the organization’s IT department. Business departments and users autonomously implement IT solutions, which are not embedded in the organizational IT service management. This increasingly occurring phenomenon is called Shadow IT. The various opportunities and risks of Shadow IT challenge organizations and call for approaches to manage the phenomenon. An initial point to achieve measurable indications for the management is to explain why Shadow IT emerges. Therefore, this paper explores the business decision to implement Shadow IT. Based on existing research we derive that Shadow IT is created after a make-or-buy decision, which is substantiated in the Transaction Cost Theory. We deploy a triangulation approach using the methods expert interviews and multiple-case study to investigate Shadow IT emergence. Our findings identify prohibitive transaction costs in the exchange relation between business and IT departments, influenced by misalignment, as the main explanation. We conclude that the principles of Transaction Cost Theory may be applied to develop governance structures for managing Shadow IT. This strengthens the link between IT Governance and Business IT Alignment and expands the understanding of business integration within the IT domains of an organization.


title: Kin2. A Kinect 2 toolbox for MATLAB

abstract: This paper introduces Kin2, a Kinect 2 toolbox for MATLAB. This toolbox encapsulates most of the Kinect for Windows SDK 2.0 functionality in a single class with high-level methods. The toolbox is written mostly in C++ with MATLAB Mex functions providing access to color, depth, infrared, and body index frames; coordinate mapping capabilities; real-time six-body tracking with 25 joints and hands states; face and high-definition face processing; and real-time 3D reconstruction. We showed that the performance of a Kin2 application decreases 30% on average with respect to a native C++ application, however, Kin reduces in one order of magnitude the average code length yielding a significant reduction in development time for prototyping and research. Kin2 is the first publicly available toolbox that provides Kinect V2 capabilities to MATLAB users.Kin2 provides most of the Kinect functionality including color, depth, infrared, coordinate mapping, body tracking, face, HD face, and 3D reconstruction.Kin2 adds additional functionality not present in the Kinect for Windows SDK 2.0 such as colored point cloud acquisition and color camera calibration.Kin2 reduces the average code length yielding a significant reduction in development time.


title: Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues

abstract: We propose detecting and responding to humor in spoken dialogue by extracting language and audio cues and subsequently feeding these features into a combined recurrent neural network (RNN) and logistic regression model. In this paper, we parse Switchboard phone conversations to build a corpus of punchlines and unfunny lines where punchlines precede laughter tokens in Switchboard transcripts. We create a combined RNN and logistic regression model that uses both acoustic and language cues to predict whether a conversational agent should respond to an utterance with laughter. Our model achieves an F1-score of 63.2 and accuracy of 73.9. This model outperforms our logistic language model (F1-score 56.6) and RNN acoustic model (59.4) as well as the final RNN model of D. Bertero, 2016 (52.9). Using our final model, we create a “laughbot” that audibly responds to a user with laughter when their utterance is classified as a punchline. A conversational agent outfitted with a humor-recognition system such as the one we present in this paper would be valuable as these agents gain utility in everyday life.


title: Diversity-Sensitive Conditional Generative Adversarial Networks

abstract: We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code. To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes. The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives. Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity. We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction. We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.


abstract: This paper investigates the adaptive distributed formation control problem for multiple nonholonomic wheeled mobile robots. First, the formation control problem is converted into a state consensus problem by the aid of a variable transformation. Then, distributed kinematic controllers and adaptive dynamic controllers are developed for each robot such that a group of nonholonomic mobile robots asymptotically converge to a desired geometric pattern with its centroid moving along the specified reference trajectory. The specified reference trajectory is assumed to be the trajectory of a virtual leader whose information is available to only a subset of the followers. Also the followers are assumed to have only local interaction. Some sufficient conditions are derived for accomplishing the asymptotically stability of the systems based on algebraic graph theory, matrix theory, and Lyapunov control approach. Finally, simulation examples illustrate the effectiveness of the proposed controllers. HighlightsAdaptive distributed formation control for multiple nonholonomic wheeled mobile robots is investigated.Formation control problem is converted to a state consensus problem via a variable transformation.The specified reference trajectory is assumed to be the trajectory of a virtual leader whose information is available to only a subset of the followers.Distributed kinematic controllers are designed for guaranteeing to reach desired formation.Adaptive dynamic controllers are proposed for guaranteeing mobile robots to track theirs desired kinematic inputs.


title: Memory functions in chronic pain: examining contributions of attention and age to test performance

abstract: OBJECTIVES: Previous studies have revealed that memory performance is diminished in chronic pain patients. Few studies, however, have assessed multiple components of memory in a single sample. It is currently also unknown whether attentional problems, which are commonly observed in chronic pain, mediate the decline in memory. Finally, previous studies have focused on middle-aged adults, and a possible detrimental effect of aging on memory performance in chronic pain patients has been commonly disregarded. This study, therefore, aimed at describing the pattern of semantic, working, and visual and verbal episodic memory performance in participants with chronic pain, while testing for possible contributions of attention and age to task performance. METHODS: Thirty-four participants with chronic pain and 32 pain-free participants completed tests of episodic, semantic, and working memory to assess memory performance and a test of attention. RESULTS: Participants with chronic pain performed worse on tests of working memory and verbal episodic memory. A decline in attention explained some, but not all, group differences in memory performance. Finally, no additional effect of age on the diminished task performance in participants with chronic pain was observed. DISCUSSION: Taken together, the results indicate that chronic pain significantly affects memory performance. Part of this effect may be caused by underlying attentional dysfunction, although this could not fully explain the observed memory decline. An increase in age in combination with the presence of chronic pain did not additionally affect memory performance.


title: Localized region context and object feature fusion for people head detection

abstract: People head detection in crowded scenes is challenging due to the large variability in clothing and appearance, small scales of people, and strong partial occlusions. Traditional bottom-up proposal methods and existing region proposal network approaches suffer from either poor recall or low precision. In this paper, we propose to improve both the recall and precision of head detection of region proposal models by integrating the local head information. In specific, we first use a region proposal network to predict the bounding boxes and corresponding scores of multiple instances in the region. A local head classifier network is then trained to score the bounding box generated from the region proposal model. After that, we propose an adaptive fusion method by optimally combining both the region and local scores to obtain the final score of each candidate bounding box. Furthermore, our fusion models can automatically learn the optimal hyper-parameters from data. Our algorithm achieves superior people head detection performance on the crowded scenes data set, which significantly outperforms several recent state-of-the-art baselines in the literature.


A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification


title: Mobile guide technologies (smartphone apps): Collaborative Heuristic Evaluation (CHE) with expert and novice users

abstract: Providing quality and meaningful experiences to the users are vital particularly with the devices that users are familiar with. Usability issues should be discarded before the systems/designs are deployed to the users. In this study, we compared the usability issues found by expert and novice users using Collaborative Heuristic Evaluation (CHE). This study was carried out with 6 experts and 9 novice users. There are 3 different mobile guides used in this study, 2 were designed to be used in museum and another mobile guide was designed to be used at a tourist attraction. Results showed that expert users found significantly more heuristic problems than novice users during the CHE procedure across the 3 different mobile guides (smartphone apps). On the other hand, there were heuristic problems found by the novices that were not noticeable to the expert.


title: Customer Lifetime Value and Defection Possibility Prediction Model Using Machine Learning: An Application to a Cloud-Based Software Company

abstract: This paper proposes an estimation of Customer Lifetime Value (CLV) for a cloud-based software company by using machine learning techniques. The purpose of this study is twofold. We classify the customers of one cloud-based software company by using two classifications methods: C4.5 and a support vector machine (SVM). We use machine learning primarily to estimate the frequency distribution of the customer defection possibility. The result shows that both the C4.5 and SVM classifications perform well, and by obtaining frequency distributions of the defection possibility, we can predict the number of customers defecting and the number of customers retained.


title: Right heterotaxy with Hirschsprung's disease-A new association

abstract: A baby girl with prenatal diagnosis of complex cardiac anomalies and diaphragmatic hernia was born at 36 weeks of gestation. At 4 hr of life, the baby developed respiratory distress and was intubated. She was found to have right hetetrotaxy with total anomalous pulmonary venous drainage into the portal vein, five hepatic veins draining the liver and intrathoracic herniation of the stomach. The child also developed abdominal distension on the second day of life with passage of scanty meconium. The diagnosis of Hirschsprung's disease (HD) was confirmed by histology. HD in association with right heterotaxy has not been reported earlier. The association of heterotaxy with HD in our patient raises a possible genetic link between the two anomalies that needs further research.


title: Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition

abstract: In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR–HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition.


title: Bayesian reinforcement learning in continuous POMDPs with application to robot navigation

abstract: We consider the problem of optimal control in continuous and partially observable environments when the parameters of the model are not known exactly. Partially observable Markov decision processes (POMDPs) provide a rich mathematical model to handle such environments but require a known model to be solved by most approaches. This is a limitation in practice as the exact model parameters are often difficult to specify exactly. We adopt a Bayesian approach where a posterior distribution over the model parameters is maintained and updated through experience with the environment. We propose a particle filter algorithm to maintain the posterior distribution and an online planning algorithm, based on trajectory sampling, to plan the best action to perform under the current posterior. The resulting approach selects control actions which optimally trade-off between 1) exploring the environment to learn the model, 2) identifying the system's state, and 3) exploiting its knowledge in order to maximize long-term rewards. Our preliminary results on a simulated robot navigation problem show that our approach is able to learn good models of the sensors and actuators, and performs as well as if it had the true model.


Steam turbine model


title: An empirical study on developer-related factors characterizing fix-inducing commits

abstract: This paper analyzes developer-related factors that could influence the likelihood for a commit to induce a fix. Specifically, we focus on factors that could potentially hinder developers' ability to correctly understand the code components involved in the change to be committed as follows: (i) the coherence of the commit (i.e., how much it is focused on a specific topic); (ii) the experience level of the developer on the files involved in the commit; and (iii) the interfering changes performed by other developers on the files involved in past commits. The results of our study indicate that ‘fix-inducing’ commits (i.e., commits that induced a fix) are significantly less coherent than ‘clean’ commits (i.e., commits that did not induce a fix). Surprisingly, ‘fix-inducing’ commits are performed by more experienced developers; yet, those are the developers performing more complex changes in the system. Finally, ‘fix-inducing’ commits have a higher number of past interfering changes as compared with ‘clean’ commits. Our empirical study sheds light on previously unexplored factors and presents significant results that can be used to improve approaches for defect prediction. Copyright © 2016 John Wiley & Sons, Ltd.


title: Uronic acid-containing glycopeptides from Fusarium oxysporum: Possible significance as chemotypes

abstract: Abstract Hot aqueous extraction of mycelia of Fusarium oxysporum , followed by fractionation on an anionic resin column gave glycopeptides FL-2 and FL-3. Methylation analysis and 1D and 2D NMR data demonstrated β- d -Man p units and partial hydrolysis gave α- d -Glc p A(1→2)- d -Gal, arising from β- d -Gal f -containing groups. Both are chemotaxonomic markers of Fusarium spp. FL-3 contained 2,6-di- O -substituted Man p , as well as 2,6-di- O -substituted Gal f units, raising the possibility that the former are main-chain constituents, as well as the expected latter structure. The carbohydrate structures of FL-2 and FL-3 differ from those of previously examined polysaccharides of Fusarium spp., which are in turn all different from each other, so that they can serve as fingerprints. Possible variations in their main-chain structures can occur as well as those of their side-chains.


title: A topic-enhanced word embedding for Twitter sentiment classification

abstract: Word representation is crucial to lexical features used in Twitter sentiment analysis models. Recent work has demonstrated that dense, low-dimensional and real-valued word embedding gives competitive performance for Twitter sentiment classification. We follow this line of work, and propose a topic-enhanced word embedding for the task, which is generally neglected in previous work. Firstly, we exploit a recursive autoencoder framework to learn topic-enhanced word embedding, where topic information is generated through topic modeling based on an effective implementation of Latent Dirichlet Allocation (LDA). Then we use a uniform framework by adopting Support Vector Machine (SVM) classifier, to compare existing word representation methods with our method. Experimental results on the dataset show that topic-enhanced word embedding is very effective for Twitter sentiment classification.


title: Sentioscope: A Soccer Player Tracking System Using Model Field Particles

abstract: Tracking multiple players is crucial to analyze soccer videos in real time. Yet, rapid illumination changes and occlusions among players who look similar from a distance make tracking in soccer very difficult. Particle-filter-based approaches have been utilized for their ability in tracking under occlusion and rapid motions. Unlike the common practice of choosing particles on targets, we introduce the notion of shared particles densely sampled at fixed positions on the model field. We globally evaluate targets’ likelihood of being on the model field particles using our combined appearance and motion model. This allows us to encapsulate the interactions among the targets in the state-space model and track players through challenging occlusions. The proposed tracking algorithm is embedded into a real-life soccer player tracking system called Sentioscope. We describe the complete steps of the system and evaluate our approach on large-scale video data gathered from professional soccer league matches. The experimental results show that the proposed algorithm is more successful, compared with the previous methods, in multiple-object tracking with similar appearances and unpredictable motion patterns such as in team sports.


title: Automatic medical disease treatment system using datamining

abstract: In our proposed system is identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-to-date with the latest discoveries. By using the tools such as NLP, ML techniques. In this research, focus on diseases and treatment information, and the relation that exists between these two entities. The main goal of this research is to identify the disease name with the symptoms specified and extract the sentence from the article and get the Relation that exists between Disease-Treatment and classify the information into cure, prevent, side effect to the user.This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.


title: Ant supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, applied to Traveling Salesman Problem

abstract: AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.


title: QEMU-based framework for non-intrusive virtual machine instrumentation and introspection

abstract: This paper presents the framework based on the emulator QEMU. Our framework provides set of multi-platform analysis tools for the virtual machines and mechanism for creating instrumentation and analysis tools. Our framework is based on a lightweight approach to dynamic analysis of binary code executed in virtual machines. This approach is non-intrusive and provides system-wide analysis capabilities. It does not require loading any guest agents and source code of the OS. Therefore it may be applied to ROM-based guest systems and enables using of record/replay of the system execution. We use application binary interface (ABI) of the platform to be analyzed for creating introspection tools. These tools recover the part of kernel-level information related to the system calls executed on the guest machine.


title: Measuring network security using dynamic bayesian network

abstract: Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics have generally focused on measuring individual vulnerabilities without considering their combined effects. Our previous work tackle this issue by exploring the causal relationships between vulnerabilities encoded in an attack graph. However, the evolving nature of vulnerabilities and networks has largely been ignored. In this paper, we propose a Dynamic Bayesian Networks (DBNs)-based model to incorporate temporal factors, such as the availability of exploit codes or patches. Starting from the model, we study two concrete cases to demonstrate the potential applications. This novel model provides a theoretical foundation and a practical framework for continuously measuring network security in a dynamic environment.


title: Graphene-based nano-antennas for electromagnetic nanocommunications in the terahertz band

abstract: Nanotechnology is enabling the development of devices in a scale ranging from one to a few hundred nanometers. Coordination and information sharing among these nano-devices will lead towards the development of future nanonetworks, boosting the range of applications of nanotechnology in the biomedical, environmental and military fields. Despite the major progress in nano-device design and fabrication, it is still not clear how these atomically precise machines will communicate. Recently, the advancements in graphene-based electronics have opened the door to electromagnetic communications in the nano-scale. In this paper, a new quantum mechanical framework is used to analyze the properties of Carbon Nanotubes (CNTs) as nano-dipole antennas. For this, first the transmission line properties of CNTs are obtained using the tight-binding model as functions of the CNT length, diameter, and edge geometry. Then, relevant antenna parameters such as the fundamental resonant frequency and the input impedance are calculated and compared to those of a nano-patch antenna based on a Graphene Nanoribbon (GNR) with similar dimensions. The results show that for a maximum antenna size in the order of several hundred nanometers (the expected maximum size for a nano-device), both a nano-dipole and a nano-patch antenna will be able to radiate electromagnetic waves in the terahertz band (0.1–10.0 THz).


title: A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge

abstract: Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation.


title: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

abstract: Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.


title: Cloud computing security considerations

abstract: Cloud computing is a new way of delivering computing resources and is not a new technology. It is an internet based service delivery model which provides internet based services, computing and storage for users in all markets including financial health care and government. This new economic model for computing has found fertile ground and is attracting massive global investment. Although the benefits of cloud computing are clear, so is the need to develop proper security for cloud implementations. Cloud security is becoming a key differentiator and competitive edge between cloud providers. This paper discusses the security issues that arise in a cloud computing frame work. It focuses on technical security issues arising from the usage of cloud services and also provides an overview of key security issues related to cloud computing with the view of a secure cloud architecture environment.


title: Open-Schema Event Profiling for Massive News Corpora

abstract: With the rapid growth of online information services, a sheer volume of news data becomes available. To help people quickly digest the explosive information, we define a new problem - schema-based news event profiling - profiling events reported in open-domain news corpora, with a set of slots and slot-value pairs for each event, where the set of slots forms the schema of an event type. Such profiling not only provides readers with concise views of events, but also facilitates various applications such as information retrieval, knowledge graph construction and question answering. It is however a quite challenging task. The first challenge is to find out events and event types because they are both initially unknown. The second difficulty is the lack of pre-defined event-type schemas. Lastly, even with the schemas extracted, to generate event profiles from them is still essential yet demanding. To address these challenges, we propose a fully automatic, unsupervised, three-step framework to obtain event profiles. First, we develop a Bayesian non-parametric model to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles. Second, we propose an unsupervised embedding model for schema induction that encodes the insight: an entity may serve as the values of multiple slots in an event, but if it appears in more sentences along with the same set of more entities in the event, its slots in these sentences tend to be similar. Finally, we build event profiles by extracting slot values for each event based on the slots' expression patterns. To the best of our knowledge, this is the first work on schema-based profiling for news events. Experimental results on a large news corpus demonstrate the superior performance of our method against the state-of-the-art baselines on event detection, schema induction and event profiling.


title: On Constraint Sampling in the Linear Programming Approach To Approximate . . .

abstract: In the linear programming approach to approximate dynamic programming, one tries to solve a certain linear program--the ALP--that has a relatively small numberK of variables but an intractable numberM of constraints. In this paper, we study a scheme that samples and imposes a subset ofm <


title: Contribution of Prosthetic Knee and Ankle Mechanisms to Swing-Phase Foot Clearance

abstract: Individuals who ambulate with transfemoral prostheses fall more frequently than users of transtibial prostheses or able-bodied persons. One factor contributing to these falls may be reduced toe clearance during swing phase of the prosthesis. Quantitative gait data were acquired from ten persons with transfemoral amputations who were fitted with either single-axis or four-bar knees, in a cross-over experiment. Toe clearance during prosthetic swing phase was measured from these trials. To differentiate between effects on toe clearance attributed to knee mechanisms and those produced by compensatory gait deviations used by subjects, 3-D swing-leg simulations, incorporating models of the knee mechanisms, were applied to these empirical data. Additionally, two currently available prosthetic ankle mechanisms that dorsiflex to facilitate toe clearance during swing phase were modeled. Four-bar mechanisms had an empirical average of 2.2 cm more minimum toe clearance than single-axis knee mechanisms (p <; 0.01). In the simulation study, single-axis knees provided the least clearance (4.6 cm) during prosthetic swing phase, followed by four-bar knees (5.8 cm), and the two ankle mechanisms (6.4 and 8.7 cm). Four-bar knees provide greater foot clearance during prosthetic swing phase than single-axis knees, but prosthetic ankle mechanisms that dorsiflex during swing phase provide substantially more.


Towards Procedural Quest Generation : A Structural Analysis of RPG Quests


title: NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets

abstract: In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a message (term-level task). Among submissions from 44 teams in a competition, our submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. We implemented a variety of surface-form, semantic, and sentiment features. We also generated two large word‐sentiment association lexicons, one from tweets with sentiment-word hashtags, and one from tweets with emoticons. In the message-level task, the lexicon-based features provided a gain of 5 F-score points over all others. Both of our systems can be replicated using freely available resources. 1


title: Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

abstract: Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.


title: Design and Simulation Low Voltage Single-Phase Transformerless Photovoltaic Inverter

abstract: The last Deckard years developing Inverter technology for renewable energy and high efficiency and more improvement solar and wind power plant. Transformer less inverter technology is best and improves efficiency, power quality and reduces switching loss. single-phase grid connected transformerless photovoltaic (PV) inverter for residential application is presented. The inverter is derived from a boost cascaded with buck converter along with a line frequency unfolding circuitIn this research paper transformer less photovoltaic (PV) inverter is going to be more adopted in order to achieve high benefit. The selected inverters are the full-bridge inverter with bipolar modulation, full-bridge inverter with DC bypass and the Highly Efficient and Reliable Inverter Concept (HERIC). A low voltage single-phase grid-connected PV system is analyzed to verify the discussions. Full Text: PDF DOI: http://dx.doi.org/10.11591/telkomnika.v12i7.6013


A Novel Design Of BranchLine Coupler 3 dB Microstrip Using Open


Face Beautification and Color Enhancement with Scene Mode Detection


title: Fully integrated 5.6–6.4 GHz power amplifier using transformer combining

abstract: A fully integrated 5.6-6.4 GHz power amplifier is implemented in a 0.25 um SiGe-HBT technology using an onchip transformer combining structure. The novel combiner topology combines the output power of two push pull pairs and leads to a reduced transistor size compared to a conventional combiner while enhancing the efficiency and maintaining the maximum output power. Electromagnetic modeling of the whole chip layout has been carried out to optimize the performance of the presented circuit. At 6 GHz and a supply voltage of 1.2/1.8 V the single-stage power amplifier achieves a measured output power of 18/21 dBm at 1 dB power compression and 21/24 dBm in saturation region. The maximum power added efficiency is 24.7%. A small signal gain of 12 dB was observed at the center frequency of 6 GHz.


title: Activity recognition using one triaxial accelerometer: a neuro-fuzzy classifier with feature reduction

abstract: This paper presents a neuro-fuzzy classifer for activity recognition using one triaxial accelerometer and feature reduction approaches. We use a triaxial accelerometer to acquire subjects' acceleration data and train the neurofuzzy classifier to distinguish different activities/movements. To construct the neuro-fuzzy classifier, a modified mapping-constrained agglomerative clustering algorithm is devised to reveal a compact data configuration from the acceleration data. In addition, we investigate two different feature reduction methods, a feature subset selection and linear discriminate analysis. These two methods are used to determine the significant feature subsets and retain the characteristics of the data distribution in the feature space for training the neuro-fuzzy classifier. Experimental results have successfully validated the effectiveness of the proposed classifier.


Chatbot for IT Security Training: Using Motivational Interviewing to Improve Security Behaviour


title: Monogenic, Polygenic, and MicroRNA Markers for Ischemic Stroke

abstract: Ischemic stroke (IS) is a leading disease with high mortality and disability, as well as with limited therapeutic window. Biomarkers for earlier diagnosis of IS have long been pursued. Family and twin studies confirm that genetic variations play an important role in IS pathogenesis. Besides DNA mutations found previously by genetic linkage analysis for monogenic IS (Mendelian inheritance), recent studies using genome-wide associated study (GWAS) and microRNA expression profiling have resulted in a large number of DNA and microRNA biomarkers in polygenic IS (sporadic IS), especially in different IS subtypes and imaging phenotypes. The present review summarizes genetic markers discovered by clinical studies and discusses their pathogenic molecular mechanisms involved in developmental or regenerative anomalies of blood vessel walls, neuronal apoptosis, excitotoxic death, inflammation, neurogenesis, and angiogenesis. The possible impact of environment on genetics is addressed as well. We also include a perspective on further studies and clinical application of these IS biomarkers.


title: Heuristic methods for vehicle routing problem with time windows

abstract: Abstract This paper documents our investigation into various heuristic methods to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions. The objective of the VRPTW is to serve a number of customers within predefined time windows at minimum cost (in terms of distance travelled), without violating the capacity and total trip time constraints for each vehicle. Combinatorial optimisation problems of this kind are non-polynomial-hard (NP-hard) and are best solved by heuristics. The heuristics we are exploring here are mainly third-generation artificial intelligent (AI) algorithms, namely simulated annealing (SA), Tabu search (TS) and genetic algorithm (GA). Based on the original SA theory proposed by Kirkpatrick and the work by Thangiah, we update the cooling scheme and develop a fast and efficient SA heuristic. One of the variants of Glover's TS, strict Tabu, is evaluated and first used for VRPTW, with the help of both recency and frequency measures. Our GA implementation, unlike Thangiah's genetic sectoring heuristic, uses intuitive integer string representation and incorporates several new crossover operations and other advanced techniques such as hybrid hill-climbing and adaptive mutation scheme. We applied each of the heuristics developed to Solomon's 56 VRPTW 100-customer instances, and yielded 18 solutions better than or equivalent to the best solution ever published for these problems. This paper is also among the first to document the implementation of all the three advanced AI methods for VRPTW, together with their comprehensive results.


Hydraulic


Deep Action Conditional Neural Network for Frame Prediction in Atari Games


title: Read/write-optimized tree indexing for solid-state drives

abstract: Flash-memory-based solid-state drives (SSDs) are used widely for secondary storage. To be effective for SSDs, traditional indices have to be redesigned to cope with the special properties of flash memory, such as asymmetric read/write latencies (fast reads and slow writes) and out-of-place updates. Previous flash-optimized indices focus mainly on reducing random writes to SSDs, which is typically accomplished at the expense of a substantial number of extra reads. However, modern SSDs show a narrowing gap between read and write speeds, and read operations on SSDs increasingly affect the overall performance of indices on SSDs. As a consequence, how to optimize SSD-aware indices by reducing both write and read costs is a pertinent and open challenge. We propose a new tree index for SSDs that is able to reduce both writes and extra reads. In particular, we use an update buffer and overflow pages to reduce random writes, and we further exploit Bloom filters to reduce the extra reads to the overflow nodes in the tree. With this mechanism, we construct a read/write-optimized index that is capable of offering better overall performance than previous flash-aware indices. In addition, we present an analysis of the proposed index and show that the read and write costs of the operations on the index can be balanced by only tuning the false-positive rate of the Bloom filters. Our experimental results suggest that our proposal is efficient and represents an improvement over existing methods.


title: The Role of Gender Information in Pronoun Resolution: Evidence from Chinese

abstract: Although previous studies have consistently demonstrated that gender information is used to resolve pronouns, the mechanisms underlying the use of gender information continue to be controversial. The present study used event-related potentials (ERPs) to investigate whether working memory modulates the effect of gender information on pronoun resolution. The critical pronoun agreed or disagreed with its antecedent in gender. Moreover, the distance between a pronoun and its antecedent was varied to assess the influence of working memory. Compared with the congruent pronouns, the incongruent pronouns elicited an N400 effect in the short distance condition and a P600 effect in the long distance condition. The results suggest that the effect of gender information on pronoun comprehension is modulated by working memory.


title: Micro-Machinations: A DSL For Game Economies

abstract: In the multi-billion dollar game industry, time to market limits the time developers have for improving games. Game designers and software engineers usually live on opposite sides of the fence, and both lose time when adjustments best understood by designers are implemented by engineers. Designers lack a common vocabulary for expressing gameplay, which hampers specification, communication and agreement. We aim to speed up the game development process by improving designer productivity and design quality. The language Machinations has introduced a graphical notation for expressing the rules of game economies that is close to a designer’s vocabulary. We present the language Micro- Machinations (MM) that details and formalizes the meaning of a significant subset of Machination’s language features and adds several new features most notably modularization. Next we describe MM Analysis in Rascal (MM AiR), a framework for analysis and simulation of MM models using the Rascal meta-programming language and the Spin model checker. Our approach shows that it is feasible to rapidly simulate game economies in early development stages and to separate concerns. Today’s meta-programming technology is a crucial enabler to achieve this.


title: In-network computing to the rescue of faulty links

abstract: Failing network links are usually disabled, and packets are routed around them until the links are repaired. While it is often possible to utilize some of a failing link's capacity, losing what remains of a link's capacity is typically deemed preferable to the erratic effect that unreliable links can have on application-level behavior. We describe a new network function that relies on in-network computing to limit the erratic effect of failing network links, to enable the continued use of those links until they can be repaired. We explore the design space using ns-3, and evaluate our implementation on a physical test-bed that includes programmable switches and reconfigurable hardware. Our current hardware prototype can almost saturate a 10GbE link while using around 10% of our FPGA's resources.


title: Using an interferometric synthetic aperture sonar to inspect the Skagerrak World War II chemical munitions dump site

abstract: In a collaboration between the Norwegian Defence Research Establishment (FFI) and the The Norwegian Coastal Administration, a HUGIN autonomous underwater vehicle (AUV) with a high resolution interferometric synthetic aperture sonar (SAS) was brought out to the Skagerrak World War II chemical munitions dump site. It mapped and documented parts of the site and located/relocated 35 shipwrecks. In this paper, we study the SAS technology for such applications. SAS technology have the potential to produce images and maps of very high resolution while maintaining large area coverage rates. There are, however, specific considerations to be taken when mapping and imaging shipwrecks which may be large complex elevated structures. We show data examples from the dump site, both of large shipwrecks and of small objects. We compare SAS images with sidescan images acquired earlier, and with multibeam echosounder data. We find SAS interferometry to be well suited for this type of documentation when operated and processed correctly.


title: Tracing eye movement protocols with cognitive process models

abstract: In using eye movements to develop cognitive models, researchers typically analyze eye movement protocols with aggregate measures and test models with respect to these measures. Because aggregate analyses sometimes conceal informative low-level behavior, protocol analyses comparing model predictions to individual trial protocols are frequently desirable; however, protocol analysis for eye movement data is often tedious and time-consuming. We describe how to automate the protocol analysis of eye movements using hidden Markov models. Working with data from an equation-solving task, we demonstrate two methods of tracing eye movement data—that is, mapping eye movements to the sequential predictions of a cognitive process model. We evaluated these tracing methods in an experiment where participants were instructed to execute given equation-solving strategies. When coding the experimental protocols in terms of the given strategies, the automated tracing methods performed as well as human expert coders in a fraction of the time.


title: Brain potentials elicited by garden-path sentences: evidence of the application of verb information during parsing

abstract: Event-related potentials were recorded from 13 scalp locations while participants read sentences containing a syntactic ambiguity. In Experiment 1, syntactically disambiguating words that were inconsistent with the «favored» syntactic analysis elicited a positive-going brain potential (P600). Experiment 2 examined whether syntactic ambiguities are resolved by application of a phrasestructure-based minimal attachment principle or by word-specific subcategorization information. P600 amplitude was a function of subcategorization biases rather than syntactic complexity. These findings indicate that such biases exist and can influence the parser under certain conditions and that P600 amplitude is a function of the perceived syntactic well-formedness of the sentence


title: Using discriminant analysis for multi-class classification: an experimental investigation

abstract: Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminant analysis approaches are well known to learn discriminative feature transformations in the statistical pattern recognition literature and can be easily extend to multi-class cases. The use of discriminant analysis, however, has not been fully experimented in the data mining literature. In this paper, we explore the use of discriminant analysis for multi-class classification problems. We evaluate the performance of discriminant analysis on a large collection of benchmark datasets and investigate its usage in text categorization. Our experiments suggest that discriminant analysis provides a fast, efficient yet accurate alternative for general multi-class classification problems.


title: EKV MOSFET model implementation in Matlab and Verilog-A

abstract: The present paper presents a straightforward yet effective approach to implementing a MOS transistor compact model (EKV v.2.6) in two different environments - Matlab and Verilog-A. This is useful for circuit-level design purposes enabling the designer to manipulate open-source models having direct access to model equation sets and program structure. EKV v.2.6 is selected for its simple model core and efficiency in describing device behaviors. A comparison of the simulation results between the two model implementations as well as a comparison to a reference model (BSIM3v3) has been illustrated in details.


title: Data driven prognostics using a Kalman filter ensemble of neural network models

abstract: This paper details the winning method in the IEEE GOLD category of the PHM psila08 Data Challenge. The task was to estimate the remaining useable life left of an unspecified complex system using a purely data driven approach. The method involves the construction of Multi-Layer Perceptron and Radial Basis Function networks for regression. A suitable selection of these networks has been successfully combined in an ensemble using a Kalman filter. The Kalman filter provides a mechanism for fusing multiple neural network model predictions over time. The essential initial stages of pre-processing and data exploration are also discussed.


title: Automatic classification of diabetic macular edema in digital fundus images

abstract: Diabetic macular edema is a common complication of diabetic retinopathy due to the presence of exudates in proximity with the fovea. In this paper, an automated method to classify diabetic macular edema is presented. The fovea is localized and the regions of macula are marked based on the Early Treatment Diabetic Retinopathy Studies (ETDRS) grading scale. Extraction method using marker-controlled watershed transformation is adopted and modified from the previous research. The location of the extracted exudates on the marked macular regions is computed to classify diabetic macular edema into normal, stage 1 and stage 2 diabetic macular edema. The performance of the proposed method is evaluated using 88 images of publicly available MESSIDOR database. The overall sensitivity, specificity and accuracy of the proposed method are 80.9%, 90.2% and 85.2%, respectively.


abstract: The success of visual analytics is predicated on the ability of users to interactively explore information. Humans think about their data through interactive visual exploration, including testing hypotheses, exploring anomalies, and other cognitive processes of building understanding from data. The claim that these insights are generated as a result of the interaction led the attendees at the Pacific Northwest National Laboratory (PNNL) workshop on "Semantic Interaction: Coupling Cognition and Computation through Usable Interactive Analytics" to posit that user interaction must play a more central role in visual analytics systems, serving as the method for coupling cognition and computation. The claims and design principles discussed in this workshop report present research directions to advance visual analytics via a user interaction approach called semantic interaction.


title: Machine learning for finding bugs: An initial report

abstract: Static program analysis is a technique to analyse code without executing it, and can be used to find bugs in source code. Many open source and commercial tools have been developed in this space over the past 20 years. Scalability and precision are of importance for the deployment of static code analysis tools - numerous false positives and slow runtime both make the tool hard to be used by development, where integration into a nightly build is the standard goal. This requires one to identify a suitable abstraction for the static analysis which is typically a manual process and can be expensive. In this paper we report our findings on using machine learning techniques to detect defects in C programs. We use three offthe- shelf machine learning techniques and use a large corpus of programs available for use in both the training and evaluation of the results. We compare the results produced by the machine learning technique against the Parfait static program analysis tool used internally at Oracle by thousands of developers. While on the surface the initial results were encouraging, further investigation suggests that the machine learning techniques we used are not suitable replacements for static program analysis tools due to low precision of the results. This could be due to a variety of reasons including not using domain knowledge such as the semantics of the programming language and lack of suitable data used in the training process.


title: Sender-intended functions of emojis in US messaging

abstract: Emojis are an extremely common occurrence in mobile communications, but their meaning is open to interpretation. We investigate motivations for their usage in mobile messaging in the US. This study asked 228 participants for the last time that they used one or more emojis in a conversational message, and collected that message, along with a description of the emojis' intended meaning and function. We discuss functional distinctions between: adding additional emotional or situational meaning, adjusting tone, making a message more engaging to the recipient, conversation management, and relationship maintenance. We discuss lexical placement within messages, as well as social practices. We show that the social and linguistic function of emojis are complex and varied, and that supporting emojis can facilitate important conversational functions.


title: Dual-Rotor Multiphase Permanent Magnet Machine With Harmonic Injection to Enhance Torque Density

abstract: Torque density of a novel dual-rotor, radial-flux multiphase permanent magnet (PM) machine is enhanced by injecting stator current harmonics without increasing stator yoke thickness. In order to enhance a regular PM machine torque capability by injecting harmonic currents, the no-load air-gap flux density should be designed to be trapezoidal, which increases the flux per pole, and results in a thicker stator yoke. However, this drawback can be avoided in a dual-rotor, radial-flux PM machine. In such a dual-rotor PM machine, the main flux can be designed to go directly from the inner rotor poles, to stator inner and outer teeth, then to the outer rotor poles, or reverse, without through the stator yoke. Therefore, the stator yoke thickness is not affected by air-gap flux at all and can actually be reduced to minimum for mechanical reasons. This paper applies stator current harmonic injection technique to a dual-rotor, radial-flux PM machine. Sizing equations of the multiphase PM machines are deduced and used for initial design and theoretical comparison. The torque performance of the proposed dual-rotor, radial-flux multiphase PM machine with stator harmonic current injection is evaluated using finite element simulation, and compared to a traditional three-phase dual-rotor, radial-flux PM machine. Simulation results validate the feasibility of torque density enhancement. The same technique is proven that it is also suitable in a dual-rotor axial-flux PM machine topology.


title: Descending control of pain

abstract: Upon receipt in the dorsal horn (DH) of the spinal cord, nociceptive (pain-signalling) information from the viscera, skin and other organs is subject to extensive processing by a diversity of mechanisms, certain of which enhance, and certain of which inhibit, its transfer to higher centres. In this regard, a network of descending pathways projecting from cerebral structures to the DH plays a complex and crucial role. Specific centrifugal pathways either suppress (descending inhibition) or potentiate (descending facilitation) passage of nociceptive messages to the brain. Engagement of descending inhibition by the opioid analgesic, morphine, fulfils an important role in its pain-relieving properties, while induction of analgesia by the adrenergic agonist, clonidine, reflects actions at alpha(2)-adrenoceptors (alpha(2)-ARs) in the DH normally recruited by descending pathways. However, opioids and adrenergic agents exploit but a tiny fraction of the vast panoply of mechanisms now known to be involved in the induction and/or expression of descending controls. For example, no drug interfering with descending facilitation is currently available for clinical use. The present review focuses on: (1) the organisation of descending pathways and their pathophysiological significance; (2) the role of individual transmitters and specific receptor types in the modulation and expression of mechanisms of descending inhibition and facilitation and (3) the advantages and limitations of established and innovative analgesic strategies which act by manipulation of descending controls. Knowledge of descending pathways has increased exponentially in recent years, so this is an opportune moment to survey their operation and therapeutic relevance to the improved management of pain.


Scaling up Correlation Clustering through Parallelism and Concurrency Control


title: Now All Together: Overview of Virtual Health Assistants Emulating Face-to-Face Health Interview Experience

abstract: We discuss a large research project aimed at building socially expressive virtual health agents or assistants (VHA) that can deliver brief motivational interventions (BMI) for behavior change, in a communication style that individuals and patients not only accept, but also find emotionally supportive and socially appropriate. Because of their well-defined sequential structure, BMIs lend themselves well to automation, and are adaptable to address a variety of target behaviors, from obesity, to alcohol and drug use, to lack treatment adherence, among others. We discuss the advantages that VHAs provide for the delivery of health interventions. We describe components of our intelligent agent architecture that enables our virtual health agents to dialogue with users in realtime while delivering the appropriate intervention based on the patient’s specific needs at the time. We conclude by identifying open research challenges in developing virtual health agents.


title: Sprite: Generalizing Topic Models with Structured Priors

abstract: We introduce SPRITE, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing SPRITE to be tailored to particular settings. We demonstrate this flexibility by constructing a SPRITE-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.


OPTICAL CHARACTER RECOGNITION: A REVIEW


AN IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR PATTERN SYNTHESIS OF PHASED ARRAYS


title: Enabling seamless WiGig/WiFi handovers in tri-band wireless systems

abstract: WiGig enables wireless multi-gigabit communication over 60GHz band. However, its usage scenarios may be constrained by two major limitations: line-of-sight propagation and very short transmission range. We seek to boost the WiGig's usability by using WiFi to complement its limitations in tri-band (2.4/5/60GHz) wireless systems. Our goal is to let a tri-band client have multimedia services at the WiGig's very high speed without any hassle. When the WiGig link is down or performs bad, not only can the client temporarily handover to WiFi without service interruption, but its ongoing multimedia services can also adapt to the WiFi's slower link. Though the IEEE 802.11ad standard has proposed an FST (Fast Session Transfer) mechanism to support handover operations at the link layer, it does not satisfy our goal due to two reasons. First, it does not specify when to perform WiGig/WiFi handovers. Second, it is not application-aware to achieve the service adaptation. To this end, we design and implement an application-aware, seamless WiGig/WiFi handover solution above the network layer. It ensures timely handover trigger for the WiGig's abrupt link interruption, keeps service continuity during handovers, and adapts multimedia service qualities to different WiGig/WiFi links. Our demo confirms its viability. We show that a video streaming service at the client is not interrupted during WiGig/WiFi handovers, which are triggered by mobility or the WiGig's signal blockage, but smoothly switches between different resolutions according to different links.


title: Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos.

abstract: Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address recognizing utterance-level emotions in dyadic conversational videos. We propose a deep neural framework, termed conversational memory network, which leverages contextual information from the conversation history. The framework takes a multimodal approach comprising audio, visual and textual features with gated recurrent units to model past utterances of each speaker into memories. Such memories are then merged using attention-based hops to capture inter-speaker dependencies. Experiments show an accuracy improvement of 3-4% over the state of the art.


title: Dynamic spectrum access in open spectrum wireless networks

abstract: One of the reasons for the limitation of bandwidth in current generation wireless networks is the spectrum policy of the Federal Communications Commission (FCC). But, with the spectrum policy reform, open spectrum wireless networks, and spectrum agile radios are set to drive next general wireless networks. In this paper, we investigate continuous-time Markov models for dynamic spectrum access in open spectrum wireless networks. Both queueing and no queueing cases are considered. Analytical results are derived based on the Markov models. A random access protocol is proposed that is shown to achieve airtime fairness. A distributed version of this protocol that uses only local information is also proposed based on homo egualis anthropological model. Inequality aversion by the radio systems to achieve fairness is captured by this model. These protocols are then extended to spectrum agile radios. Extensive simulation results are presented to compare the performances of fixed versus agile radios.


title: Affective computing: Challenges

abstract: A number of researchers around the world have built machines that recognize, express, model, communicate, and respond to emotional information, instances of "affective computing." This article raises and responds to several criticisms of affective computing, articulating state-of-the art research challenges, especially with respect to affect in human-computer interaction.


title: Fuzzy logic based fault detector and classifier for three phase transmission lines with STATCOM

abstract: This paper presents the fault detection and classification for shunt faults on transmission lines with Static Synchronous Compensator (STATCOM) using fuzzy logic. The fuzzy logic is implemented by using the fuzzy logic toolbox in MATLAB. The 500kV, 60Hz system is simulated using simulink and simpower systems toolbox in MATLAB. The proposed method uses only the three phase currents for fault detection and classification of faulted phase. The system is simulated for various shunt faults with different fault resistances and with different fault locations between 10 percent and 90 percent of the transmission line. The results from the simulation show that fuzzy logic based fault detector and classifier accurately detects and classifies the faults on transmission system. The proposed method is able to detect and classify the faults for varying fault resistances and fault distances from the relaying point.


title: Selecting training data for cross-corpus speech emotion recognition: Prototypicality vs. generalization

abstract: We investigate strategies for selection of databases and instances for training cross-corpus emotion recognition systems, that is, systems that generalize across different labelling concepts, languages and interaction scenarios. We propose objective measures for prototypicality based on distances in a large space of brute-forced acoustic features and show their relation to the expected performance in cross-corpus testing. We perform extensive evaluation on eight commonly used corpora of emotional speech reaching from acted to fully natural emotion and limited phonetic content to conversational speech. In the result, selecting prototypical training instances by the proposed criterion can deliver a gain of up to 7.5 % unweighted accuracy in cross-corpus arousal recognition, and there is a correlation of .571 between the proposed prototypicality measure of databases and the expected unweighted accuracy in cross-corpus testing by Support Vector Machines.


title: LOF: identifying density-based local outliers

abstract: For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical.


title: On Geometric Algebra representation of Binary Spatter Codes

abstract: Kanerva's Binary Spatter Codes are reformulated in terms of geometric algebra. The key ingredient of the construction is the representation of XOR binding in terms of geometric product.