title: Understanding large text corpora via sparse machine learning

abstract: Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. The approach has been successfully used in many areas, such as signal and image processing. This article posits that these methods can be extremely useful in the analysis of large collections of text documents, without requiring user expertise in machine learning. Our approach relies on three main ingredients: (i) multidocument text summarization; (ii) comparative summarization of two corpora, both using sparse regression or classification; (iii) sparse principal components and sparse graphical models for unsupervised analysis and visualization of large text corpora. We validate our methods using a corpus of Aviation Safety Reporting System (ASRS) reports and demonstrate that the methods can reveal causal and contributing factors in runway incursions. Furthermore, we show that the methods automatically discover four main tasks that pilots perform during flight, which can aid in further understanding the causal and contributing factors to runway incursions and other drivers for aviation safety incidents. We also provide a comparative study involving other commonly used datasets, and report on the competitiveness of sparse machine learning compared to state-of-the-art methods such as latent Dirichlet allocation (LDA).  2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013


title: Delicate manipulations with compliant mechanism and electrostatic adhesion

abstract: Traditional rigid robotic hand manipulator has been used in many field nowadays due to its advantages of large gripping force and stable performance. However, this kind of rigid manipulator is not suitable for gripping fragile objects since it is motorized and force control can be a problem. It is also not suitable to grip object with different shapes since the manipulator is rigid and not compliant. In this study, a novel manipulator with gripping capability is designed and fabricated. The manipulator combines electrostatic adhesion actuation with soft manipulators. The manipulator has high flexibility and can be compliant to different shapes due to the property of the materials. It is very promising to do delicate manipulations in industry field and biomedical field.


title: Unsupervised Learning of Evolving Relationships Between Literary Characters

abstract: Understanding inter-character relationships is fundamental for understanding character intentions and goals in a narrative. This paper addresses unsupervised modeling of relationships between characters. We model relationships as dynamic phenomenon, represented as evolving sequences of latent states empirically learned from data. Unlike most previous work our approach is completely unsupervised. This enables data-driven inference of inter-character relationship types beyond simple sentiment polarities, by incorporating lexical and semantic representations, and leveraging large quantities of raw text. We present three models based on rich sets of linguistic features that capture various cues about relationships. We compare these models with existing techniques and also demonstrate that relationship categories learned by our model are semantically coherent.


title: 5G New Radio: Waveform, Frame Structure, Multiple Access, and Initial Access

abstract: Different from conventional mobile networks designed to optimize the transmission efficiency of one particular service (e.g., streaming voice/ video) primarily, the industry and academia are reaching an agreement that 5G mobile networks are projected to sustain manifold wireless requirements, including higher mobility, higher data rates, and lower latency. For this purpose, 3GPP has launched the standardization activity for the first phase 5G system in Release 15 named New Radio (NR). To fully understand this crucial technology, this article offers a comprehensive overview of the state-of-the-art development of NR, including deployment scenarios, numerologies, frame structure, new waveform, multiple access, initial/random access procedure, and enhanced carrier aggregation (CA) for resource requests and data transmissions. The provided insights thus facilitate knowledge of design and practice for further features of NR.


title: Plausible Deniability for Privacy-Preserving Data Synthesis

abstract: Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanism for differential privacy are often computationally impractical to use for releasing high dimensional data or cannot preserve high utility of original data due to their extensive data perturbation. ::: ::: This paper presents a criterion called plausible deniability that provides a formal privacy guarantee, notably for releasing sensitive datasets: an output record can be released only if a certain amount of input records are indistinguishable, up to a privacy parameter. This notion does not depend on the background knowledge of an adversary. Also, it can efficiently be checked by privacy tests. We present mechanisms to generate synthetic datasets with similar statistical properties to the input data and the same format. We study this technique both theoretically and experimentally. A key theoretical result shows that, with proper randomization, the plausible deniability mechanism generates differentially private synthetic data. We demonstrate the efficiency of this generative technique on a large dataset; it is shown to preserve the utility of original data with respect to various statistical analysis and machine learning measures.


title: A Cross-Shaped Dielectric Resonator Antenna for Multifunction and Polarization Diversity Applications

abstract: This letter proposes a multifunction cross-shaped dielectric resonator antenna (DRA) with separately fed broadside circularly polarized (CP) and omnidirectional linearly polarized (LP) radiation patterns. These distinct radiation patterns are achieved in overlapping frequency bands by exciting two different modes in a single dielectric resonator (DR) volume. This letter also investigates the effect of the feeding geometry on the mutual coupling between the modes and concludes that an asymmetric feeding degrades the orthogonality of the modes and thus increases the interport coupling coefficient. By using a symmetric feeding, the coupling coefficient can be significantly reduced to below - 30 dB in the frequency band common to both operation modes. The experimental results show a good agreement with simulation and demonstrate a broadside CP operation over a bandwidth of 6.8%, which overlaps with the omnidirectional LP impedance bandwidth of 38.5%. The proposed antenna could be used not only as multifunction, but also as polarization diversity antenna due to the overlapping dual-feed CP and LP operation.


Model-Based Automatic Building Extraction From LIDAR and Aerial Imagery


An Integrated Solution for Snoring Sound Classification Using Bhattacharyya Distance Based GMM Supervectors with SVM, Feature Selection with Random Forest and Spectrogram with CNN


title: Approaches for heuristically biasing RRT growth

abstract: This paper presents several modifications to the basic rapidly-exploring random tree (RRT) search algorithm. The fundamental idea is to utilize a heuristic quality function to guide the search. Results from a relevant simulation experiment illustrate the benefit and drawbacks of the developed algorithms. The paper concludes with several promising directions for future research.


title: Image Based Localization in Indoor Environments

abstract: Image based localization is an important problem with many applications. In our previous work, we presented a two step pipeline for performing image based localization of mobile devices in outdoor environments. In the first step, a query image is matched against a georeferenced 3D image database to retrieve the "closest" image. In the second step, the pose of the query image is recovered with respect to the "closest" image using cell phone sensors. As such, a key ingredient of our outdoor image based localization is a 3D georeferenced image database. In this paper, we extend this approach to indoors by utilizing a 3D locally referenced image database generated by an ambulatory depth acquisition backpack that is originally developed for 3D modeling of indoor environments. We demonstrate retrieval rate of 94% over a set of 83 query images taken in an indoor shopping center and characterize pose recovery accuracy of the same set.


title: Deep reinforcement learning for time series: playing idealized trading games

abstract: Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input. Experiments are conducted on two idealized trading games. 1) Univariate: the only input is a wave-like price time series, and 2) Bivariate: the input includes a random stepwise price time series and a noisy signal time series, which is positively correlated with future price changes. The Univariate game tests whether the agent can capture the underlying dynamics, and the Bivariate game tests whether the agent can utilize the hidden relation among the inputs. Stacked Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) units, Convolutional Neural Network (CNN), and multi-layer perceptron (MLP) are used to model Q values. For both games, all agents successfully find a profitable strategy. The GRU-based agents show best overall performance in the Univariate game, while the MLP-based agents outperform others in the Bivariate game.


title: Compact UWB Bandpass Filter With Dual Notch Bands Using Open Circuited Stubs

abstract: In this letter, an ultra-wideband (UWB) bandpasss filter with multiple notch bands is presented. The UWB bandpass characteristic is achieved using a modified distributed highpass filter (HPF) and suppressing higher order harmonics of the HPF by realizing defected stepped impedance resonator. The dual band notches at 5.75 and 8.05 GHz are obtained by embedding two open stubs on the main microstrip line. The in band and out of band performance obtained from fullwave electromagnetic simulation, equivalent circuit model and measurement are in good agreement.


title: The tempting illusion of genetic virtue

abstract: M ark Walker has put forth a proposal which he calls the Genetic Virtue Program or GVP, whose kernel is that it is both possible and desirable to improve virtue by preimplantation selection or in utero engineering. Walker lists caveats to his thesis, although he consistently implies that their validity is doubtful by stating at each instance that he is including them ‘‘merely for the sake of completeness.’’ I will discuss the GVP from various angles of biology, my area of expertise. Personally, I’m in favor of genetic engineering freely undertaken for specific concrete outcomes; for example, adjustment to a radically new habitat. At the same time, as a research biologist I appreciate that even a small change will alter not only the attributes of the target but also additional processes and behaviors. Although Walker approaches the topic philosophically, there are so many errors in the scientific basis of his proposal that I find it hard to discuss them pithily. Analysis of the GVP is like elaborating Lysenko’s Lamarckian take on plant genetics or preparing a crewed expedition to Mars based on the Ptolemaic view of the universe. The most fundamental problem with the GVP is that there are no genes for virtue or even inclination for it, just as there are no genes for intelligence. Genes encode regulatory, catalytic and structural proteins and RNAs. They do not encode the nervous system; even less do they encode complex behavior. As one example, recent work out of the Plomin lab indicates that the six genomic regions defined by single nucleotide polymorphisms that contribute the most to IQ—itself a population sorting tool rather than an intelligence indicator—influence IQ by a paltry 1 percent. Walker further weakens his terminally wobbly foundation by invoking superceded genetics references that are a decade or more older and slipping back and forth between human and non-human studies. Rodent studies, in particular, are useless in illuminating causes of human behavior, since radically different foundations drive it in the respective species. About half of our genes contribute directly to brain function; the rest do so indirectly, since brain function depends crucially on signal processing and body feedback. This makes the brain/mind a bona fide complex (though knowable) system. This attribute underlines the intrinsic infeasibility of instilling virtue by changing ‘‘a few’’ genes, as the GVP proposes. Furthermore, although many diseases develop from malfunctions of single genes, this does not prove, or even indicate, that any single gene is responsible for a complex attribute. Instead a gene is the equivalent of a screw or a belt, whose malfunction can stop a car but does not make it run. The constant harping of the GVP on trait heritability ‘‘in spite of environment’’ is a straw argument. No serious biologist suggests (or has done so for a long time) that genes do not decisively influence outcomes. However, the nature/nurture debate has been largely resolved by the gene/environment (G 3 E) interplay model, a non-reductive approximation closer to reality. The genetic program is flexible and dynamic throughout our lifespan—we see this in the lab every day, even in homogeneous undifferentiated cell cultures. If genetic programs were as fixed, simple and one-to-one mapped as Walker’s article implies, we would have answered most questions about brain doi: 10.2990/29_1_76


title: The challenge of new drug discovery for tuberculosis

abstract: Tuberculosis (TB) is more prevalent in the world today than at any other time in human history. Mycobacterium tuberculosis, the pathogen responsible for TB, uses diverse strategies to survive in a variety of host lesions and to evade immune surveillance. A key question is how robust are our approaches to discovering new TB drugs, and what measures could be taken to reduce the long and protracted clinical development of new drugs. The emergence of multi-drug-resistant strains of M. tuberculosis makes the discovery of new molecular scaffolds a priority, and the current situation even necessitates the re-engineering and repositioning of some old drug families to achieve effective control. Whatever the strategy used, success will depend largely on our proper understanding of the complex interactions between the pathogen and its human host. In this review, we discuss innovations in TB drug discovery and evolving strategies to bring newer agents more quickly to patients.


title: Deep-learning: A potential method for tuberculosis detection using chest radiography

abstract: Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic feature extractors. In this paper, we present a potential method for tuberculosis detection using deep-learning which classifies CXR images into two categories, that is, normal and abnormal. We have used CNN architecture with 7 convolutional layers and 3 fully connected layers. The performance of three different optimizers has been compared. Out of these, Adam optimizer with an overall accuracy of 94.73% and validation accuracy of 82.09% performed best amongst them. All the results are obtained using Montgomery and Shenzhen datasets which are available in public domain.


title: Senior executives' IT management responsibilities: serious IT-related deficiencies and CEO/CFO turnover

abstract: While the information systems scholarly and practice literatures both stress the importance of senior executive engagement with IT management, the recommendations for doing so remain, at best, limited and general. Examining the influence of serious IT-related deficiencies on CEO/CFO turnover within the post-SOX financial reporting context, specific CEO/CFO IT management responsibilities are identified: CEOs are shown to be held accountable for global IT management responsibilities, and CFOs are shown to be held accountable for demand-side IT management responsibilities. Implications for information systems research, management research, and information systems practice are provided.


title: Robust Digital Twin Compositions for Industry 4.0 Smart Manufacturing Systems

abstract: Industry 4.0 is an emerging business paradigm that is reaping the benefits of enabling technologies driving intelligent systems and environments. By acquiring, processing and acting upon various kinds of relevant context information, smart automated manufacturing systems can make well-informed decisions to adapt and optimize their production processes at runtime. To manage this complexity, the manufacturing world is proposing the 'Digital Twin' model to represent physical products in the real space and their virtual counterparts in the virtual space, with data connections to tie the virtual and real products together for an augmented view of the manufacturing workflow. The benefits of such representations are simplified process simulations and efficiency optimizations, predictions, early warnings, etc. However, the robustness and fidelity of digital twins are a critical concern, especially when independently developed production systems and corresponding digital twins interfere with one another in a manufacturing workflow and jeopardize the proper behavior of production systems. We therefore evaluate the addition of safeguards to digital twins for smart cyber-physical production systems (CPPS) in an Industry 4.0 manufacturing workflow in the form of feature toggles that are managed at runtime by software circuit breakers. Our evaluation shows how these improvements can increase the robustness of interacting digital twins by avoiding local errors from cascading through the distributed production or manufacturing workflow.


title: EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

abstract: Convolutional neural networks have been successfully applied to semantic segmentation problems. However, there are many problems that are inherently not pixel-wise classification problems but are nevertheless frequently formulated as semantic segmentation. This ill-posed formulation consequently necessitates hand-crafted scenario-specific and computationally expensive post-processing methods to convert the per pixel probability maps to final desired outputs. Generative adversarial networks (GANs) can be used to make the semantic segmentation network output to be more realistic or better structure-preserving, decreasing the dependency on potentially complex post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate the discussed problem using an embedding loss. With EL-GAN, we discriminate based on learned embeddings of both the labels and the prediction at the same time. This results in more stable training due to having better discriminative information, benefiting from seeing both `fake' and `real' predictions at the same time. This substantially stabilizes the adversarial training process. We use the TuSimple lane marking challenge to demonstrate that with our proposed framework it is viable to overcome the inherent anomalies of posing it as a semantic segmentation problem. Not only is the output considerably more similar to the labels when compared to conventional methods, the subsequent post-processing is also simpler and crosses the competitive 96% accuracy threshold.


title: Grounded Textual Entailment

abstract: Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise P entails a hypothesis H iff in all worlds where P is true, H is also true. Statistical models view this relationship probabilistically, addressing it in terms of whether a human would likely infer H from P. In this paper, we wish to bridge these two perspectives, by arguing for a visually-grounded version of the Textual Entailment task. Specifically, we ask whether models can perform better if, in addition to P and H, there is also an image (corresponding to the relevant"world"or"situation"). We use a multimodal version of the SNLI dataset (Bowman et al., 2015) and we compare"blind"and visually-augmented models of textual entailment. We show that visual information is beneficial, but we also conduct an in-depth error analysis that reveals that current multimodal models are not performing"grounding"in an optimal fashion.


title: PENGARUH EPS, PER, DER, ROE TERHADAP HARGA SAHAM PADA PERUSAHAAN SEKTOR PERTAMBANGAN YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI) TAHUN 2010-2012

abstract: One source of funds obtained by the company is derived from the sale of shares. Stock price changes up or down time by time, and it is influenced by many factors. This research was conducted to examine the effect of Earning Per Share (EPS), Price Earning Ratio (PER), Debt To Equity Ratio (DER), Return on Equity (ROE) on stock price in the Mining Sector Listed in Indonesia Stock Exchange (IDX) in the period 2010-2012.The data used in this research is a secondary data obtained from Indonesia Stock Exchange. The analysis technique using linear regression, while hypothesis testing using t-test and test-F at a significance level of 5%. The classical assumptions test used in this reasearch include normality, multicolinearity, heteroscedasticity and autocorrelation test.Based on the research result that the Earning Per Share (EPS), Price Earning Ratio (PER), and Return On Equity (ROE) and a significant positive effect on Stock Price. Meanwhile, Debt To Equity Ratio (DER) has negative affect on the stock price. Simoultaneously there is a significant effect between Earning Per Share, Price Earning Ratio, Debt To Equity Ratio, dan Return On Equity to the Stock Price.For investors who want to invest stocks, should pay attention to the level of Earning Per Share too, as in this research say that Earning Per Share provides the most powerful influence on Stock Price.


title: AuDroid: Preventing Attacks on Audio Channels in Mobile Devices

abstract: Voice control is a popular way to operate mobile devices, enabling users to communicate requests to their devices. However, adversaries can leverage voice control to trick mobile devices into executing commands to leak secrets or to modify critical information. Contemporary mobile operating systems fail to prevent such attacks because they do not control access to the speaker at all and fail to control when untrusted apps may use the microphone, enabling authorized apps to create exploitable communication channels. In this paper, we propose a security mechanism that tracks the creation of audio communication channels explicitly and controls the information flows over these channels to prevent several types of attacks. We design and implement AuDroid, an extension to the SE Linux reference monitor integrated into the Android operating system for enforcing lattice security policies over the dynamically changing use of system audio resources. To enhance flexibility, when information flow errors are detected, the device owner, system apps and services are given the opportunity to resolve information flow errors using known methods, enabling AuDroid to run many configurations safely. We evaluate our approach on 17 widely-used apps that make extensive use of the microphone and speaker, finding that AuDroid prevents six types of attack scenarios on audio channels while permitting all 17 apps to run effectively. AuDroid shows that it is possible to prevent attacks using audio channels without compromising functionality or introducing significant performance overhead.


title: StructCap: Structured Semantic Embedding for Image Captioning

abstract: Image captioning has attracted ever-increasing research attention in multimedia and computer vision. To encode the visual content, existing approaches typically utilize the off-the-shelf deep Convolutional Neural Network (CNN) model to extract visual features, which are sent to Recurrent Neural Network (RNN) based textual generators to output word sequence. Some methods encode visual objects and scene information with attention mechanism more recently. Despite the promising progress, one distinct disadvantage lies in distinguishing and modeling key semantic entities and their relations, which are in turn widely regarded as the important cues for us to describe image content. In this paper, we propose a novel image captioning model, termed StructCap. It parses a given image into key entities and their relations organized in a visual parsing tree, which is transformed and embedded under an encoder-decoder framework via visual attention. We give an end-to-end formulation to facilitate joint training of visual tree parser, structured semantic attention and RNN-based captioning modules. Experimental results on two public benchmarks, Microsoft COCO and Flickr30K, show that the proposed StructCap model outperforms the state-of-the-art approaches under various standard evaluation metrics.


title: Analysis of End-Effector position and orientation for 2P-3R Planer Pneumatic Robotic Arm

abstract: In order to control a Robotic arm it involves study of kinematics and dynamics. In this paper we study forward kinematics of Planer Pneumatic Robotic Arm with two prismatic and three revolute (2P-3R) joints. Forward kinematics involves computing End Effector orientation and position provided with a set of joint angles. In this paper we used Denavit Hartenberg D-H to model links and joints of the Robotic arm. Because of simplicity of design and control, reliability and safety Pneumatic robotic arm is very commonly used in industries for part handling and pick and place purpose and it is also used in laboratory for training purposes.


title: Perceived Information Security, Financial Liability and Consumer Trust in Electronic Commerce Transactions

abstract: Electronic commerce (EC) transactions are subject to multiple information security threats. Proposes that consumer trust in EC transactions is influenced by perceived information security and distinguishes it from the objective assessment of security threats. Proposes mechanisms of encryption, protection, authentication, and verification as antecedents of perceived information security. These mechanisms are derived from technological solutions to security threats that are visible to consumers and hence contribute to actual consumer perceptions. Tests propositions in a study of 179 consumers and shows a significant relationship between consumers’ perceived information security and trust in EC transactions. Explores the role of limited financial liability as a surrogate for perceived security. However, the findings show that there is a minimal effect of financial liability on consumers’ trust in EC. Engenders several new insights regarding the role of perceived security in EC transactions.


Assessing The Impact Of Gender And Personality On Film Preferences


title: Driver behaviour with adaptive cruise control

abstract: This paper reports on the evaluation of adaptive cruise control (ACC) from a psychological perspective. It was anticipated that ACC would have an effect upon the psychology of driving, i.e. make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but workload might be reduced and driving might be less stressful. Drivers were asked to drive in a driving simulator under manual and ACC conditions. Analysis of variance techniques were used to determine the effects of workload (i.e. amount of traffic) and feedback (i.e. degree of information from the ACC system) on the psychological variables measured (i.e. locus of control, trust, workload, stress, mental models and situation awareness). The results showed that: locus of control and trust were unaffected by ACC, whereas situation awareness, workload and stress were reduced by ACC. Ways of improving situation awareness could include cues to help the driver predict vehicle trajectory and identify conflicts.


title: Resource Allocation Optimization for Device-to-Device Communication Underlaying Cellular Networks

abstract: Device-to-Device (D2D) communication will become an important technology in future networks with the increase of the requirements of local communication services. The interference between cellular communication and D2D communication can be coordinated by proper power control and resource allocation. In this paper, we analyze the resource allocation methods for D2D communication underlaying cellular networks. A novel resource allocation method that D2D can reuse the resources of more than one cellular user is proposed. After that, we discuss the selection of the optimal resource allocation method from the proposed method and the conventional methods. Finally, the performance of different methods is evaluated through numerical simulation. The simulation results show that the proposed method is the optimal resource allocation method when the D2D pair locates at the most part of the cell area in both uplink and downlink. The proposed method can improve the sum throughput of cellular communication and D2D communication significantly.


title: Principled missing data methods for researchers

abstract: The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.


title: JENNIFER M. SAUL WHAT IS SAID AND PSYCHOLOGICAL REALITY; GRICE’S PROJECT AND RELEVANCE THEORISTS ’ CRITICISMS

abstract: One of the most important aspects of Grice’s theory of conversation is the drawing of a borderline between what is said and what is implicated. Grice’s views concerning this borderline have been strongly and influentially criticised by relevance theorists. In particular, it has become increasingly widely accepted that Grice’s notion of what is said is too limited, and that pragmatics has a far larger role to play in determining what is said than Grice would have allowed. (See for example Bezuidenhuit 1996; Blakemore 1987; Carston 1991; Recanati 1991, 1993, 2001; Sperber and Wilson 1986; Wilson and Sperber 1981.) In this paper, I argue that the rejection of Grice has moved too swiftly, as a key line of objection which has led to this rejection is flawed. The flaw, we will see, is that relevance theorists rely on a misunderstanding of Grice’s project in his theory of conversation. I am not arguing that Grice’s versions of saying and implicating are right in all details, but simply that certain widespread reasons for rejecting his theory are based on misconceptions. 1 Relevance theorists, I will suggest, systematically misunderstand Grice by taking him to be engaged in the same project that they are: making sense of the psychological processes by which we interpret utterances. Notions involved with this project will need to be ones that are relevant to the psychology of utterance interpretation. Thus, it is only reasonable that relevance theorists will require that what is said and what is implicated should be psychologically real to the audience. (We will see that this requirement plays a crucial role in their arguments against Grice.) Grice, I will argue, was not pursuing this project. Rather, I will suggest that he was trying to make sense of quite a different notion of what is said: one on which both speaker and audience may be wrong about what is said. On this sort of notion, psychological reality is not a requirement. So objections to Grice based on a requirement of psychological reality will fail.


title: Fuzzy Prototypes: From a Cognitive View to a Machine Learning Principle

abstract: Cognitive psychology works have shown that the cognitive representation of categories is based on a typicality notion: all objects of a category do not have the same representativeness, some are more characteristic or more typical than others, and better exemplify their category. Categories are then defined in terms of prototypes, i.e. in terms of their most typical elements. Furthermore, these works showed that an object is all the more typical of its category as it shares many features with the other members of the category and few features with the members of other categories.


title: Heart Rate Prediction Based on Physical Activity Using Feedforwad Neural Network

abstract: The technique of combining heart rate (HR) and physical activity (PA) has been adopted in a number of research areas, such as energy expenditure measurement, autonomic nervous system assessment, sports research, etc. However, there have been few studies on the direct relationship between HR and PA. This paper proposes a HR prediction model based on the relationship between HR and PA. The predictor has the potential to be used in various areas, such as: cardiopathy research and diagnosis, heart attack warning indicator, sports capability measure and mental activity evaluation. The method has the following steps: first, the recorded HR and PA signals are preprocessed as two synchronized time sequences: HR(n) and PA(n). The inputs of the predictor are HR(n) and PA(n) in the current time step, and the output is the predicted sequence HR(n + 1) in the next time step. The feed forward neural network (FFNN) was chosen as the mathematical model of the predictor. Experiments was conducted based on the real-life signals from a healthy male. A set of 90 minute signals were collected. One half of the signal set was used to train the FFNN and the other half to validate the training. The mean absolute error of the predicted heart rate was restricted inside 5. The result shows the potential of the proposed method.


title: Automatic Verification of Pipelined Microprocessor Control

abstract: We describe a technique for verifying the control logic of pipelined microprocessors. It handles more complicated designs, and requires less human intervention, than existing methods. The technique automatically compares a pipelined implementation to an architectural description. The CPU time needed for verification is independent of the data path width, the register file size, and the number of ALU operations. Debugging information is automatically produced for incorrect processor designs. Much of the power of the method results from an efficient validity checker for a logic of uninterpreted functions with equality. Empirical results include the verification of a pipelined implementation of a subset of the DLX architecture.


title: Research challenges and perspectives on Wisdom Web of Things (W2T)

abstract: The rapid development of the Internet and the Internet of Things accelerates the emergence of the hyper world. It has become a pressing research issue to realize the organic amalgamation and harmonious symbiosis among humans, computers, and things in the hyper world, which consists of the social world, the physical world, and the information world (cyber world). In this paper, the notion of Wisdom Web of Things (W2T) is proposed in order to address this issue. As inspired by the material cycle in the physical world, the W2T focuses on the data cycle, namely "from things to data, information, knowledge, wisdom, services, humans, and then back to things." A W2T data cycle system is designed to implement such a cycle, which is, technologically speaking, a practical way to realize the harmonious symbiosis of humans, computers, and things in the emerging hyper world.


title: Employee Empowerment with Computer Based Learning: An Empirical Investigation

abstract: Enterprises are confronted with frequent changes in their business environment which require quick responses. Thereby, highly skilled and flexi- ble employees play a major role since they are able to respond promptly. To en- hance competencies and flexibility, the concept of employee empowerment has been proposed. In this respect, the workforce is given an increased level of au- tonomy and offered support during their decision-making processes. It is evi- dent that technology can contribute within this context. However, the role of computer based learning with regard to the support of decision-making activi- ties and the acquisition of competencies, especially in combination with in- creased employee autonomy, has been neglected until now. On the basis of an empirical case study, we find that the usage of computer based learning within employee empowerment initiatives fosters the acquisition of competencies and increases employee flexibility. Additionally, enhanced employee autonomy is found to have a positive moderating effect on both relationships.


title: Competitive Learning: From Interactive Activation to Adaptive Resonance

abstract: Functional ond mechanistic comparisons are mode between several network models of cognitive processing: competitive learning, interactive activation, adaptive resonance, and back propagation. The starting point of this comparison is the article of Rumelhart ond Zipser (1985) on feature discovery through competitive learning. All the models which Rumelhart and Zipser (1985) have described were shown in Grossberg (1976b) to exhibit a type of learning which is temporally unstable. Competitive learning mechanisms con be stabilized in response to an arbitrary input environment by being supplemented with mechanisms for learning top-down expectancies, or templates; for matching bottom-up input patterns with the top-down expectancies; and for releasing orienting reactions in o mismatch situation, thereby updating short-term memory ond searching for another internal representation. Network architectures which embody all of these mechonisms were called adaptive resonance models by Grossberg (1976~). Self-stabilizing learning models are candidates for use in real-world applications where unpredictable changes can occur in complex input environments. Competitive learning postulates ore inconsistent with the postulates of the interactive activotion model of McClelland and Rumelhart (1981). and suggest different levels of processing and interaction rules for the analysis of word recognition. Adaptive resonance models use these alternative levels and interaction rules. The selforganizing learning of on odaptive resonance model is compared ond contrasted with the teacher-directed learning of a back propagation model. A number of criteria for evaluating reol-time network models of cognitive processing ore described and applied.


title: Building a Dataset for Summarization and Keyword Extraction from Emails

abstract: This paper introduces a new email dataset, consisting of both single and thread emails, manually annotated with summaries and keywords. A total of 349 emails and threads have been annotated. The dataset is our first step toward developing automatic methods for summarization and keyword extraction from emails. We describe the email corpus, along with the annotation interface, annotator guidelines, and agreement studies.


title: An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry

abstract: Abstract Since the early 1980s, the concept of relationship management in marketing area has gained its importance. Acquiring and retaining the most profitable customers are serious concerns of a company to perform more targeted marketing campaigns. For effective customer relationship management, it is important to gather information on customer value. Many researches have been performed to calculate customer value based on Customer lifetime value (LTV). It, however, has some limitations. It is difficult to consider the defection of customers. Prediction models have focused mainly on expected future cash flow derived from customers' past profit contribution. In this paper we suggest an LTV model considering past profit contribution, potential benefit, and defection probability of a customer. We also cover a framework for analyzing customer value and segmenting customers based on their value. Customer value is classified into three categories: current value, potential value, and customer loyalty. Customers are segmented according to three types of customer value. A case study on calculating customer value and segmenting customers of a wireless communication company will be illustrated.


title: Deeply-Learned Feature for Age Estimation

abstract: Human age provides key demographic information. It is also considered as an important soft biometric trait for human identification or search. Compared to other pattern recognition problems (e.g., object classification, scene categorization), age estimation is much more challenging since the difference between facial images with age variations can be more subtle and the process of aging varies greatly among different individuals. In this work, we investigate deep learning techniques for age estimation based on the convolutional neural network (CNN). A new framework for age feature extraction based on the deep learning model is built. Compared to previous models based on CNN, we use feature maps obtained in different layers for our estimation work instead of using the feature obtained at the top layer. Additionally, a manifold learning algorithm is incorporated in the proposed scheme and this improves the performance significantly. Furthermore, we also evaluate different classification and regression schemes in estimating age using the deep learned aging pattern (DLA). To the best of our knowledge, this is the first time that deep learning technique is introduced and applied to solve the age estimation problem. Experimental results on two datasets show that the proposed approach is significantly better than the state-of-the-art.


title: Improving the accuracy of top-N recommendation using a preference model

abstract: In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well-suited for representing qualitative user preferences. In particular, it can be easily implemented with less than 100 lines of codes on top of existing CF algorithms such as user-based, item-based, and matrix-factorization-based algorithms. When our preference model is combined to three kinds of CF algorithms, experimental results demonstrate that the preference model can improve the accuracy of all existing CF algorithms such as ATOP and [email protected] by 3-24% and 6-98%, respectively.


title: Consensus algorithms for biased labeling in crowdsourcing

abstract: Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms, DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.


title: Pressure Distribution in Transtibial Prostheses Socket and the Stump Interface

abstract: Abstract The paper deals with the problem of a biomechanical solution for human locomotion after amputation of a lower limb. With an inappropriately designed transtibial prostheses socket, the interaction between the socket and the stump occurs, leading to increased friction and subsequent surface damage to the soft tissue. In individual types of transtibial prostheses, locations are defined which can be loaded and which cannot be loaded. On the basis of the study of the anterior side of the stump, three loadable and two non-loadable areas were monitored using the TACTILUS tactile pressure sensor (Sensor Products Inc., Madison, New Jersey, USA). Methods of measuring individual values were proposed for the purpose of sensing the pressure in the socket and the liner interface, the stump and the liner interface. Each method includes the methodological preparation of the patient, the preparation of the room, the preparation of the measuring system, and the subsequent method of data processing. In the submitted paper, 11 stumps were non-invasively monitored. The method of assessing the pressure exerted on the stumps was focused on the monitoring of the pressure distribution in the selected areas. The results obtained by the pressure measurements were statistically processed. In both cases of the pressure system placement, the p-value was higher than 0.05; therefore, we can state that the sets are equal.


title: Activity identification using body-mounted sensors—a review of classification techniques

abstract: With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.


title: Method for 315/433 MHz Remote Keyless Entry sensitivity and range statistical analysis for automotive applications

abstract: The paper describes methodology for modelling and simulative estimation of sensitivity and range of ISM band Remote Keyless Entry transmitter-receiver system for automotive applications. The method is based on electrical simulation including PCB extraction data and parameters statistical distribution to evaluate best- and worst case scenarios. Both transmitter and receiver circuits are considered, as well as link environmental conditions. Examples of results are given for presented methodology.


title: Single-Feed Ultra-Wideband Circularly Polarized Antenna With Enhanced Front-to-Back Ratio

abstract: This communication presents a single-feed ultra-wideband circularly polarized (CP) antenna with high front-to-back ratio (FBR). The antenna is composed of two orthogonally placed elliptical dipoles printed on both sides of a substrate. To realize high FBR, a novel composite cavity is also proposed and integrated with the presented crossed dipoles, which effectively reduces the backlobe of the crossed dipoles. Simulation results are in good agreement with the measured results that demonstrate an impedance bandwidth from 0.9 to 2.95 GHz (106.5%) and a 3-dB axial ratio (AR) bandwidth from 1 to 2.87 GHz (96.6%). The measured FBR is about 30 dB across the whole global navigation satellite system (GNSS) band. Compared with other reported single-feed wideband CP antennas, the antenna has advantages such as a wider CP bandwidth and lower backlobe radiation.


title: An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets

abstract: Most classifiers work well when the class distribution in the response variable of the dataset is well balanced. Problems arise when the dataset is imbalanced. This paper applied four methods: Oversampling, Undersampling, Bagging and Boosting in handling imbalanced datasets. The cardiac surgery dataset has a binary response variable (1 = Died, 0 = Alive). The sample size is 4976 cases with 4.2 % (Died) and 95.8 % (Alive) cases. CART, C5 and CHAID were chosen as the classifiers. In classification problems, the accuracy rate of the predictive model is not an appropriate measure when there is imbalanced problem due to the fact that it will be biased towards the majority class. Thus, the performance of the classifier is measured using sensitivity and precision Oversampling and undersampling are found to work well in improving the classification for the imbalanced dataset using decision tree. Meanwhile, boosting and bagging did not improve the Decision Tree performance.


Image Inpainting and Object Removal with Deep Convolutional GAN


title: Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli

abstract: Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. Here, we propose a novel multivariate regression-based method extracting onset-related brain responses from the ongoing EEG. We analyse EEG recordings of nine subjects who passively listened to stimuli from various sound categories encompassing simple tone sequences, full-length romantic piano pieces and natural (non-music) soundscapes. The regression approach reduces the 61-channel EEG to one time course optimally reflecting note onsets. The neural signatures derived by this procedure indeed resemble canonical onset-related ERPs, such as the N1-P2 complex. This EEG projection was then utilized to determine the Cortico-Acoustic Correlation (CACor), a measure of synchronization between EEG signal and stimulus. We demonstrate that a significant CACor (i) can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii) it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity. In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment. Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.


title: The basic building blocks and evolution of CRISPR-CAS systems.

abstract: CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) is an adaptive immunity system in bacteria and archaea that functions via a distinct self/non-self recognition mechanism that involves unique spacers homologous with viral or plasmid DNA and integrated into the CRISPR loci. Most of the Cas proteins evolve under relaxed purifying selection and some underwent dramatic structural rearrangements during evolution. In many cases, CRISPR-Cas system components are replaced either by homologous or by analogous proteins or domains in some bacterial and archaeal lineages. However, recent advances in comparative sequence analysis, structural studies and experimental data suggest that, despite this remarkable evolutionary plasticity, all CRISPR-Cas systems employ the same architectural and functional principles, and given the conservation of the principal building blocks, share a common ancestry. We review recent advances in the understanding of the evolution and organization of CRISPR-Cas systems. Among other developments, we describe for the first time a group of archaeal cas1 gene homologues that are not associated with CRISPR-Cas loci and are predicted to be involved in functions other than adaptive immunity.


Link prediction in multiplex online social networks


A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task


Towards an argumentative content search engine using weak supervision


title: Comments on scale and conformal invariance in four dimensions

abstract: There has been recent interest in the question of whether four dimensional scale invariant unitary quantum field theories are actually conformally invariant. In this note we present a complete analysis of possible scale anomalies in correlation functions of the trace of the stress-energy tensor in such theories. We find that 2-, 3- and 4-point functions have a non-trivial anomaly while connected higher point functions are non-anomalous. We pay special attention to semi-local contributions to correlators (terms with support on a set containing both coincident and separated points) and show that the anomalies in 3- and 4-point functions can be accounted for by such contributions. We discuss the implications of the our results for the question of scale versus conformal invariance.


title: Social recommendation across multiple relational domains

abstract: Social networks enable users to create different types of personal items. In dealing with serious information overload, the major problems of social recommendation are sparsity and cold start. In existing approaches, relational and heterogeneous domains can not be effectively utilized for social recommendation, which brings a challenge to model users and multiple types of items together on social networks. In this paper, we consider how to represent social networks with multiple relational domains and alleviate the major problems in an individual domain by transferring knowledge from other domains. We propose a novel Hybrid Random Walk (HRW), which can integrate multiple heterogeneous domains including directed/undirected links, signed/unsigned links and within-domain/cross-domain links into a star-structured hybrid graph with user graph at the center. We perform random walk until convergence and use the steady state distribution for recommendation. We conduct experiments on a real social network dataset and show that our method can significantly outperform existing social recommendation approaches.


title: 27.12MHz GaN Bi-directional resonant power converter

abstract: Resonant dc-dc converter regularly use Schottky diodes for high frequency rectification. Si Schottky diodes have relatively low voltage ratings and present reverse-recovery type losses when driven hard enough. SiC Schottky diodes can operate at high frequencies, but incur higher conduction losses. This paper presents a bidirectional dc-dc resonant converter having two ground referenced switches. The prototype converter delivers 400 W between a source of 170 V and a load of 50 V while delivering 250 W in the opposite direction. The converter presented herein can potentially be used in applications such as wireless power transfer, fuel cell and battery applications, bus converters, etc.


title: ALOHOMORA: a tool for linkage analysis using 10K SNP array data

abstract: Summary: ALOHOMORA is a software tool designed to facilitate genome-wide linkage studies performed with high-density single nucleotide polymorphism (SNP) marker panels such as the Affymetrix GeneChip® Human Mapping 10K Array. Genotype data are converted into appropriate formats for a number of common linkage programs and subjected to standard quality control routines before linkage runs are started. ALOHOMORA is written in Perl and may be used to perform state-of-the-art linkage scans in small and large families with any genetic model. Options for using different genetic maps or ethnicity-specific allele frequencies are implemented. Graphic outputs of whole-genome multipoint LOD score values are provided for the entire dataset as well as for individual families. ::: ::: Availability: ALOHOMORA is available free of charge for non-commercial research institutions. For more details, see http://gmc.mdc-berlin.de/alohomora/ ::: ::: Contact: fruesch@mdc-berlin.de


title: Compact design of MIMO antennas for LTE 700 application

abstract: This paper proposes a monopole type MIMO antenna for Long Tern Evolution 700 (LTE 700) band application. The overall dimensions of 70×30×1.6 mm3 of the proposed design are consisting of an antenna portion of 30×30×1.6 mm3 and system ground portion of 45×30×1.6 mm3. The antenna portion is composed of two identical monopole antennas located on FR4 substrate's top portion. The strips on the front side and those on the back side of the substrate are connected by via-holes. Two strips resonant two modes, respectively. The isolation element extended from the system ground plane is placed between the two monopole antennas to eliminate the induced current between them. This isolation mechanism not only can improve the impedance matching to cover LTE 700 band, but also the antenna's isolation. The proposed antenna design also has a benefit of planar structure with a compact size.


Application of Markov chain analysis to trend prediction of stock indices


title: Be Your Own Prada: Fashion Synthesis with Structural Coherence

abstract: We present a novel and effective approach for generating new clothing on a wearer through generative adversarial learning. Given an input image of a person and a sentence describing a different outfit, our model "redresses" the person as desired, while at the same time keeping the wearer and her/his pose unchanged. Generating new outfits with precise regions conforming to a language description while retaining wearer's body structure is a new challenging task. Existing generative adversarial networks are not ideal in ensuring global coherence of structure given both the input photograph and language description as conditions. We address this challenge by decomposing the complex generative process into two conditional stages. In the first stage, we generate a plausible semantic segmentation map that obeys the wearer's pose as a latent spatial arrangement. An effective spatial constraint is formulated to guide the generation of this semantic segmentation map. In the second stage, a generative model with a newly proposed compositional mapping layer is used to render the final image with precise regions and textures conditioned on this map. We extended the DeepFashion dataset [8] by collecting sentence descriptions for 79K images. We demonstrate the effectiveness of our approach through both quantitative and qualitative evaluations. A user study is also conducted. The codes and the data are available at this http URL edu.hk/projects/FashionGAN/.


title: A new hyperchaotic map and its application in an image encryption scheme

abstract: Abstract Recently, the hyperchaotic maps have been investigated in order to develop more secure encryption schemes. In this paper we propose a new hyperchaotic map derived from parametric equations of the serpentine curve. Its complex behavior was proven theoretically and numerically, using Lyapunov exponents, bifurcation diagram and correlation dimension of the attractor. The proposed map is then used in a new image encryption scheme with a classic bi-modular architecture: a diffusion stage, in which the pixels of the plain image are shuffled using a random permutation generated with a new algorithm, and a confusion stage, in which the pixels are modified with a XOR-scheme based on the proposed map. The results of its statistical analysis show that the proposed image encryption scheme provides an efficient and secure way for image encryption.


title: Robust iterative closest point algorithm for registration of point sets with outliers

abstract: The problem of registering point sets with outliers including noises and missing data is discussed in this paper. To solve this problem, a novel objective function is proposed by introducing an overlapping percentage for partial registration. Moreover, a novel robust iterative closest point (ICP) algorithm is proposed which can compute rigid transformation, correspondence, and overlapping percentage automatically at each iterative step. This new algorithm uses as many point pairs as possible to yield a more reliable and accurate registration result between two m-D point sets with outliers. Experimental results demonstrate that our algorithm is more robust than the traditional ICP and the state-of-the-art algorithms.


SHP EPORTR A ASHP Therapeutic Guidelines on Stress Ulcer Prophylaxis


Digital Health Data: A Comprehensive Review of Privacy and Security Risks and Some Recommendations


title: Neuro-fuzzy control of a robotic exoskeleton with EMG signals

abstract: We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user's upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method.


title: Fast Prediction for Sparse Time Series: Demand Forecast of EV Charging Stations for Cell Phone Applications

abstract: This paper proposes a new cellphone application algorithm which has been implemented for the prediction of energy consumption at electric vehicle (EV) charging stations at the University of California, Los Angeles (UCLA). For this interactive user application, the total time for accessing the database, processing the data, and making the prediction needs to be within a few seconds. We first analyze three relatively fast machine learning-based time series prediction algorithms and find that the nearest neighbor (NN) algorithm (k NN with k = 1) shows better accuracy. Considering the sparseness of the time series of the charging records, we then discuss the new algorithm based on the new proposed time-weighted dot product (TWDP) dissimilarity measure to improve the accuracy and processing time. Two applications have been designed on top of the proposed prediction algorithm: one predicts the expected available energy at the outlet and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is approximately 1 s for both applications. The granularity of the prediction is 1 h and the horizon is 24 h; data have been collected from 20 EV charging outlets.


title: On the Extraction, Ordering, and Usage of Landmarks in Planning

abstract: Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and use them for guiding search, in the hope to speed up the planning process. We go beyond the previous approaches by defining ordering constraints not only over the (top level) goals, but also over the sub-goals that will arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We show how such landmarks can be found, how their inherent ordering constraints can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant performance improvements in both heuristic forward search and Graphplan-style planning.


title: A distributed cloud-based dialog system for conversational application development

abstract: We have previously presented HALEF‐an open-source spoken dialog system‐that supports telephonic interfaces and has a distributed architecture. In this paper, we extend this infrastructure to be cloud-based, and thus truly distributed and scalable. This cloud-based spoken dialog system can be accessed both via telephone interfaces as well as through web clients with WebRTC/HTML5 integration, allowing in-browser access to potentially multimodal dialog applications. We demonstrate the versatility of the system with two conversation applications in the educational domain.


PD-Fuzzy Control of Single Lower Limb Exoskeleton for Hemiplegia Mobility


title: Internet use and verbal aggression: The moderating role of parents and peers

abstract: This research investigated the influence of parent-adolescent communication quality, as perceived by the adolescents, on the relationship between adolescents' Internet use and verbal aggression. Adolescents (N=363, age range 10-16, M"T"1=12.84, SD=1.93) were examined twice with a six-month delay. Controlling for social support in general terms, moderated regression analyses showed that Internet-related communication quality with parents determined whether Internet use is associated with an increase or a decrease in adolescents' verbal aggression scores over time. A three way interaction indicated that high Internet-related communication quality with peers can have disadvantageous effects if the communication quality with parents is low. Implications on resources and risk factors related to the effects of Internet use are discussed.


title: Imputation-Enhanced Prediction of Septic Shock in ICU Patients

abstract: Sepsis and septic shock are potentially fatal complications that frequently occur in intensive care unit patients. The ability to predict which patients are at risk for sepsis and septic shock is therefore crucial to limiting the eects of these complications. Potential indications for sepsis risk are scattered in a wide range of clinical measurements, including high-temporal resolution physiological waveforms, Xrays and gene expression levels, etc., leading to a non-trivial prediction problem. Thus previous works on sepsis prediction have used very small, carefully curated datasets, with limited applicability. Recently however, a large, rich ICU dataset called MIMIC-II has been made publicly available, providing opportunity for more extensive modeling of this problem. However, such a large dataset inevitably comes with a substantial higher amount of missing data. In this paper, we investigate how dierent imputation methods can overcome the handicap of missing information while leveraging such a large dataset. Our results show that imputation approaches in conjunction with predictive modeling lead to a decent boost in accuracy of sepsis risk prediction and a huge improvement in prediction of septic shock, even when one is restricted to only using non-invasive measurements. Our models can be applied to any ICU patient and lead to a generalized approach for predicting sepsis related complications.


title: A refactoring approach for optimizing mobile networks

abstract: Mobile networks are expected to serve a wide range of verticals, however the Long Term Evolution (LTE) network is optimized for basic mobile operator services only. The network functions serving LTE networks are largely implemented as dedicated single function devices that offer poor customization options. This intrinsic inflexibility makes current LTE networks unable to meet the requirements of future mobile networks. For example, LTE networks experience signaling storms because the signals exchanged by the network functions cannot be optimized according to the current usage pattern of mobile services. Modularizing these network functions would enable a refactoring of the LTE network, allowing operators to compose networks that adapt and evolve with the influx of verticals. In this article, we present a new approach for refactoring the network functions serving LTE networks which can be leveraged to compose a modular mobile network optimized for the verticals using its services. As an example, we demonstrate that deploying network functions at the edge significantly reduces the signals exchanged within a mobile network.


title: Time-Shifting Tracking and Locating Algorithm for RLTLUM System

abstract: more and more real-time applications apply RFID technology to identify, track and locate objects automatically in real time. However current research on RFID-based tracking and locating system focuses on RFID hardware, conflict detection and so on. RFID hardware which is applied to RFID-based mining system has heavy influences on design and selection of tracking and locating algorithm. There is not a general tracking and locating algorithm for RFID-based mining system. In this paper, we propose, design and implement a time-shifting tracking and locating algorithm (TSTLA) for RFID-based tracking and locating underground mine system. TSTLA algorithm has high generality and is independent of RFID hardware. We describe the system topology structure and make the rules of distributing RFID readers in underground mine. And then we present essential information for TSTLA algorithm and describe the steps of performing TSTLA algorithm. We verify TSTLA algorithm by implementing RLTLUM prototype system. Finally, we conclude the paper and indicate the future work.


title: A Graph-to-Sequence Model for AMR-to-Text Generation

abstract: The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.


title: A Two-Point Method for PTZ Camera Calibration in Sports

abstract: Calibrating narrow field of view soccer cameras is challenging because there are very few field markings in the image. Unlike previous solutions, we propose a two-point method, which requires only two point correspondences given the prior knowledge of base location and orientation of a pan-tilt-zoom (PTZ) camera. We deploy this new calibration method to annotate pan-tilt-zoom data from soccer videos. The collected data are used as references for new images. We also propose a fast random forest method to predict pan-tilt angles without image-to-image feature matching, leading to an efficient calibration method for new images. We demonstrate our system on synthetic data and two real soccer datasets. Our two-point approach achieves superior performance over the state-of-the-art method.


title: Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training

abstract: Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with multiple lines) to an image for automatic poetry creation. This task involves multiple challenges, including discovering poetic clues from the image (e.g., hope from green), and generating poems to satisfy both relevance to the image and poeticness in language level. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. To extract poetic clues from images, we propose to learn a deep coupled visual-poetic embedding, in which the poetic representation from objects, sentiments and scenes in an image can be jointly learned. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator. To facilitate the research, we have released two poem datasets by human annotators with two distinct properties: 1) the first human annotated image-to-poem pair dataset (with 8,292 pairs in total), and 2) to-date the largest public English poem corpus dataset (with 92,265 different poems in total). Extensive experiments are conducted with 8K images, among which 1.5K image are randomly picked for evaluation. Both objective and subjective evaluations show the superior performances against the state-of-the-art methods for poem generation from images. Turing test carried out with over 500 human subjects, among which 30 evaluators are poetry experts, demonstrates the effectiveness of our approach.


title: Eval all, trust a few, do wrong to none: Comparing sentence generation models

abstract: In this paper, we study recent neural generative models for text generation related to variational autoencoders. Previous works have employed various techniques to control the prior distribution of the latent codes in these models, which is important for sampling performance, but little attention has been paid to reconstruction error. In our study, we follow a rigorous evaluation protocol using a large set of previously used and novel automatic and human evaluation metrics, applied to both generated samples and reconstructions. We hope that it will become the new evaluation standard when comparing neural generative models for text.


title: www.IJCSI.org Effective Approaches For Extraction Of Keywords

abstract: Keywords are index terms that contain most important information. Automatic keyword extraction is the task to identify a small set of words , keyphrases or keywords from a document that can describe the meaning of document. Keyword extraction is considered as core technology of all automatic processing for text materials. In this paper, a Survey of Keyword Extraction techniques have been presented that can be applied to extract effective keywords that uniquely identify a document.


title: A 60 GHz Phase Shifter Integrated With LNA and PA in 65 nm CMOS for Phased Array Systems

abstract: This paper presents the design of a 60 GHz phase shifter integrated with a low-noise amplifier (LNA) and power amplifier (PA) in a 65 nm CMOS technology for phased array systems. The 4-bit digitally controlled RF phase shifter is based on programmable weighted combinations of I/Q paths using digitally controlled variable gain amplifiers (VGAs). With the combination of an LNA, a phase shifter and part of a combiner, each receiver path achieves 7.2 dB noise figure, a 360° phase shift range in steps of approximately 22.5°, an average insertion gain of 12 dB at 61 GHz, a 3 dB-bandwidth of 5.5 GHz and dissipates 78 mW. Consisting of a phase shifter and a PA, one transmitter path achieves a maximum output power of higher than +8.3 dBm, a 360° phase shift range in 22.5° steps, an average insertion gain of 7.7 dB at 62 GHz, a 3 dB-bandwidth of 6.5 GHz and dissipates 168 mW.


title: Solving Large Imperfect Information Games Using CFR+

abstract: Counterfactual Regret Minimization and variants (e.g. Public Chance Sampling CFR and Pure CFR) have been known as the best approaches for creating approximate Nash equilibrium solutions for imperfect information games such as poker. This paper introduces CFR$^+$, a new algorithm that typically outperforms the previously known algorithms by an order of magnitude or more in terms of computation time while also potentially requiring less memory.


title: A low cross-polarized antipodal Vivaldi antenna array for wideband operation

abstract: An antipodal Vivaldi antenna gives good performance over a wide bandwidth, but the cross-polarization level is unfortunately high due to the slot flares on different layers. As a simple technique to reduce the cross-polarization level in the array, the antipodal antenna and its mirror image are placed alternately in the H-plane. The cross-polarization level is reduced more than 20 dB at broadside.


title: Sensory processing abilities of children with ADHD

abstract: Objective: To assess and compare the sensory processing abilities of children with Attention Deficit/ Hyperactivity Disorder (ADHD) and children without disabilities, and to analyze the relationship between sensory processing difficulties and behavioural symptoms presented by children with ADHD. Method: Thirty-seven children with ADHD were compared with thirty-seven controls using a translated and adapted version of the "Sensory Profile" answered by the parents/caregivers. For the ADHD group, Sensory Profile scores were correlated to behavioural symptoms assessed using the Child Behaviour Check List (CBCL) and the Behavioural Teacher Rating Scale (EACI-P). The statistical analyses were conducted using the Mann Whitney test and Pearson correlation coefficients. Results: Children with ADHD showed significant impairments compared to the control group in sensory processing and modulation, as well as in behavioural and emotional responses as observed in 11 out of 14 sections and 6 out of 9 factors. Differences in all Sensory Profile response patterns were also observed between the two groups of children. Sensory Profile scores showed a moderately negative correlation with CBCL and EACI-P scores in the ADHD group. Conclusion: These results indicate that children with ADHD may present sensory processing impairments, which may contribute to the inappropriate behavioural and learning responses displayed by children with ADHD. It also suggests the importance of understanding the sensory processing difficulties and its possible contribution to the ADHD symptomatology.


title: Raimond: Quantitative Data Extraction from Twitter to Describe Events

abstract: Social media play a decisive role in communicating and spreading information during global events. In particular, real-time microblogging platforms such as Twitter have become prevalent. Researchers have used microblogging for a number of tasks, including past events analysis, predictions, and information retrieval. Nevertheless, little attention has been given to quantitative data extraction. In this paper, we address two questions: can we develop a mechanism to extract quantitative data from a collection of tweets, and can we use the salient findings to describe an event? To answer the first question, we introduce Raimond, a virtual text curator, specialized in quantitative data extraction from Twitter. To address the second question, we use our system on three events and evaluate its output using a crowdsourcing strategy. We demonstrate the effectiveness of our approach with a number of real world examples.


title: Atomic wedgie: efficient query filtering for streaming time series

abstract: In many applications, it is desirable to monitor a streaming time series for predefined patterns. In domains as diverse as the monitoring of space telemetry, patient intensive care data, and insect populations, where data streams at a high rate and the number of predefined patterns is large, it may be impossible for the comparison algorithm to keep up. We propose a novel technique that exploits the commonality among the predefined patterns to allow monitoring at higher bandwidths, while maintaining a guarantee of no false dismissals. Our approach is based on the widely used envelope-based lower bounding technique. Extensive experiments demonstrate that our approach achieves tremendous improvements in performance in the offline case, and significant improvements in the fastest possible arrival rate of the data stream that can be processed with guaranteed no false dismissal.


Practice and Theory of Blendshape Facial Models


title: Image Steganography Using LSB and Edge - Detection Technique

abstract: Steganography is the technique of hiding the fact that communication is taking place, by hiding data in other data. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Steganalysis, the detection of this hidden information, is an inherently difficult problem and requires a thorough investigation so we are using "Edge detection Filter". In this paper search how the edges of the images can be used to hiding text message in Steganography .It give the depth view of image steganography and Edge detection Filter techniques. Method: In this paper search how the edges of the images can be used to hiding text message in Steganography. For that gray image has been presented. In this method tried to find binary value of each character of text message and then in the next stage, tried to find dark places of gray image (black) by converting the original image to binary image for labeling each object of image by considering on 8 pixel connectivity. Then these images have been converted to RGB image in order to find dark places. Because in this way each sequence of gray color turns into RGB color and dark level of grey image is found by this way if the Gary image is very light the histogram must be changed manually to find just dark places. In the final stage each 8 pixels of dark places has been considered as a byte and binary value of each character has been put in low bit of each byte that was created manually by dark places pixels for increasing security of the main way of least Significant bit steganography. Steganalysis then used to evaluate the hiding process to ensure the data can be hidden in best possible way.


title: Group Scissor: Scaling Neuromorphic Computing Design to Large Neural Networks

abstract: Synapse crossbar is an elementary structure in neuromorphic computing systems (NCS). However, the limited size of crossbars and heavy routing congestion impede the NCS implementation of large neural networks. In this paper, we propose a two-step framework (namely, group scissor) to scale NCS designs to large neural networks. The first step rank clipping integrates low-rank approximation into the training to reduce total crossbar area. The second step is group connection deletion, which structurally prunes connections to reduce routing congestion between crossbars. Tested on convolutional neural networks of LeNet on MNIST database and ConvNet on CIFAR-10 database, our experiments show significant reduction of crossbar and routing area in NCS designs. Without accuracy loss, rank clipping reduces the total crossbar area to 13.62% or 51.81% in the NCS design of LeNet or ConvNet, respectively. The following group connection deletion further decreases the routing area of LeNet or ConvNet to 8.1% or 52.06%, respectively.


Mining to Analyze News for Stock Market Prediction


title: On Bayesian Network and Outlier Detection

abstract: Existing studies on data mining has largely focused on the design of measures and algorithms to identify outliers in large and high dimensional categorical and numeric databases. However, not much stress has been given on the interestingness of the reported outlier. One way to ascertain interestingness and usefulness of the reported outlier is by making use of a domain knowledge. In this paper, we present a new measure to discover outliers based on background knowledge, represented by a Bayesian network. We define outliers as “unlikely events under the current favored theory of the domain”. We introduce two quantitative rules derived from the Bayesian network to uncover outliers. Furthermore, we use these rules to rank the instances based on joint probability distribution in the Bayesian network. In our approach, we not only identified outliers but also explain why they are likely to be so. A critical analysis on distance based technique is also presented to show why there is a mismatch between outliers as entities “which are far away from their neighbors” and “real” outliers as identified using Bayesian Networks.


title: Stereotypes help people connect with others in the community: A situated functional analysis of the stereotype consistency bias in communication.

abstract: Communicators tend to share more stereotype-consistent than stereotype-inconsistent information. The authors propose and test a situated functional model of this stereotype consistency bias: Stereotype-consistent and inconsistent information differentially serve 2 central functions of communication-sharing information and regulating relationships; depending on the communication context, information seen to serve these different functions better is more likely communicated. Results showed that stereotype-consistent information is perceived as more socially connective but less informative than inconsistent information, and when the stereotype is perceived to be highly shared in the community, more stereotype-consistent than inconsistent information is communicated due to its greater social connectivity function. These results highlight the need to examine communication as a dynamic and situated social activity. © 2007 American Psychological Association.


title: Measuring the Objectness of Image Windows

abstract: We present a generic objectness measure, quantifying how likely it is for an image window to contain an object of any class. We explicitly train it to distinguish objects with a well-defined boundary in space, such as cows and telephones, from amorphous background elements, such as grass and road. The measure combines in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. These include an innovative cue to measure the closed boundary characteristic. In experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to outperform a state-of-the-art saliency measure, and the combined objectness measure to perform better than any cue alone. We also compare to interest point operators, a HOG detector, and three recent works aiming at automatic object segmentation. Finally, we present two applications of objectness. In the first, we sample a small numberof windows according to their objectness probability and give an algorithm to employ them as location priors for modern class-specific object detectors. As we show experimentally, this greatly reduces the number of windows evaluated by the expensive class-specific model. In the second application, we use objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives. As shown in several recent papers, objectness can act as a valuable focus of attention mechanism in many other applications operating on image windows, including weakly supervised learning of object categories, unsupervised pixelwise segmentation, and object tracking in video. Computing objectness is very efficient and takes only about 4 sec. per image.


title: Unlimited FTTH-PON scaling by exploiting the AWG free spectral range

abstract: This paper investigates the performance of a novel 2.5 Gbps FTTH-PON network architecture which employs a passive optical booster configuration for future user scaling. The passive booster configuration uses an N/spl times/N AWG and exploits its cyclical routing table which is made possible by the device's free spectral range (FSR) property configuration for future user and services scaling. The new configuration is compared with a conventional access network configuration which is based on the FTTH-EPON standard.


title: Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data

abstract: Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions.,,,,,,The YOLO object detector achieves an AUC of impressively 90.49% for daysequence1, which is an improvement of 50.32% compared to the latest ACF entry in the VIVAchallenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3%, which is in an increase of 18.13%.


title: Employee Motivation: "Just Ask Your Employees"

abstract: Employee motivation is an intricate and sophisticated subject; however, contemporary managers must face and deal with this topic to obtain organizational success. To enhance understanding of employee motivation, mangers must recognize the imperativeness of employee motivation, its concepts, and differences in individual needs. Subsequently, managers need to be aware of a variety of employee motivational factors and the changes in priorities of these factors over time. Moreover, managers have to learn previous and current motivational programs, examples, and theories behind them because understanding of these fundamentals can enhance their ability to identify rewards systems that could be matched with employee needs. This understanding of the employee motivation process requires a systematic approach, and managers must realize that employee motivation and its process are there to motivate their employees; therefore, employee input must be valued and included throughout this process.


title: Radar-based fall detection based on Doppler time–frequency signatures for assisted living

abstract: Falls are a major public health concern and main causes of accidental death in the senior U.S. population. Timely and accurate detection permit immediate assistance after a fall and, thereby, reduces complications of fall risk. Radar technology provides an effective means for this purpose because it is non-invasive, insensitive to lighting conditions as well as obstructions, and has less privacy concerns. In this study, the authors develop an effective fall detection scheme for the application in continuous-wave radar systems. The proposed scheme exploits time-frequency characteristics of the radar Doppler signatures, and the motion events are classified using the joint statistics of three different features, including the extreme frequency, extreme frequency ratio, and the length of event period. Sparse Bayesian classifier based on the relevance vector machine is used to perform the classification. Laboratory experiments are performed to collect radar data corresponding to different motion patterns to verify the effectiveness of the proposed algorithm. Language: en


title: Partially Dielectric-Loaded Ridged Horn Antenna Design for Ultrawideband Gain and Radiation Performance Enhancement

abstract: In this letter, a partially dielectric-loaded double-ridged horn antenna (PD-DRHA) that operates at 1-18-GHz frequency band is proposed for ultrawideband applications. The partially dielectric loading technique using a small lens inward the aperture has been implemented to enhance the gain of the standard double-ridged horn antenna (DRHA), without changing the physical dimensions. Approximately 5 dB gain increment level is achieved from 5 to 15 GHz with slight changes on return loss characteristics. Moreover, the pattern distortion of DRHA typically between 12-18-GHz band is improved. The antenna gain, radiation pattern, and input reflection performance measurements are presented with comparisons.


title: QuickSync: Improving Synchronization Efficiency for Mobile Cloud Storage Services

abstract: Mobile cloud storage services have gained phenomenal success in recent few years. In this paper, we identify, analyze, and address the synchronization ( sync ) inefficiency problem of modern mobile cloud storage services. Our measurement results demonstrate that existing commercial sync services fail to make full use of available bandwidth, and generate a large amount of unnecessary sync traffic in certain circumstances even though the incremental sync is implemented. For example, a minor document editing process in Dropbox may result in sync traffic 10 times that of the modification. These issues are caused by the inherent limitations of the sync protocol and the distributed architecture. Based on our findings, we propose QuickSync, a system with three novel techniques to improve the sync efficiency for mobile cloud storage services, and build the system on two commercial sync services. Our experimental results using representative workloads show that QuickSync is able to reduce up to 73.1 percent sync time in our experiment settings.


The Foundations for Scalable Multi-Core Software in Intel ® Threading Building Blocks


title: PAM-4 receiver with integrated linear TIA and 2-bit ADC in 0.13 μm SiGe:C BiCMOS for high-speed optical communications

abstract: This paper presents the design and electrical characterization of an optical receiver and demodulator for PAM-4 encoded data signals. The prototype, fabricated in a 0.13 μm SiGe:C BiCMOS technology, comprises a linear TIA input stage followed by a 2-bit flash ADC, and is designed to support up to 100 Gb/s data rate while dissipating 650 mW of average power. The TIA stage was independently characterized, featuring 54 dB-Ω differential transimpedance, 3-dB bandwidth of 60 GHz and less than 12 pA/VWz average input referred current noise density. The complete module was measured to receive up to 24 GBd (setup-limited) PAM-4 PRBS7 data signals at a BER of 4 · 10−12 and 1·10−13 for the LSB and MSB, respectively, with input amplitude as low as 580 μA pp . Clear NRZ eye diagrams up to 50 Gb/s are also reported, which demonstrate the high-speed operation capability. The integration of both TIA and dedicated ADC in the same chip allows for a custom design, optimized in terms of power dissipation and footprint, for the next generation optical transceivers.


DLR's Robotic Lab - Recent Developments in Space Robotics


title: PWM control of dual active bridge: comprehensive analysis and experimental verification

abstract: The dual-active-bridge (DAB) topology is ideally suited for high-power dc-dc conversion, especially when bidirectional power transfer is required. However, it has the drawback of high circulating currents and hard switching at light loads, if wide variation in input and output is expected. To address these issues, this paper presents a comprehensive analysis and experimental results with pulsewidth-modulation (PWM) control of the DAB. The PWM control is in addition to phase-shift modulation between the two H-bridges. The analysis addresses PWM of one bridge at a time and of both bridges simultaneously. In the latter, five distinct modes arise based on the choice of PWM and load condition. The possibilities are analyzed for optimizing power density and efficiency for low-load operation. Finally, a composite scheme combining single and dual PWM is proposed that extends the soft-switching range down to zero-load condition, reduces rms and peak currents, and results in significant size reduction of the transformer. Experimental results are presented with a 10-kW prototype.