query: Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

positive: Intrusion Detection with Neural Networks With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to detect illegitimate use is through monitoring unusual user activity. Methods of intrusion detection based on hand-coded rule sets or predicting commands on-line are laborous to build or not very reliable. This paper proposes a new way of applying neural networks to detect intrusions. We believe that a user leaves a ’print’ when using the system; a neural network can be used to learn this print and identify each user much like detectives use thumbprints to place people at crime scenes. If a user’s behavior does not match his/her print, the system administrator can be alerted of a possible security breech. A backpropagation neural network called NNID (Neural Network Intrusion Detector) was trained in the identification task and tested experimentally on a system of 10 users. The system was 96% accurate in detecting unusual activity, with 7% false alarm rate. These results suggest that learning user profiles is an effective way for detecting intrusions.

positive: Training Support Vector Machines: an Application to Face Detection Detection (To appear in the Proceedings of CVPR'97, June 17-19, 1997, Puerto Rico.) Edgar Osunay? Robert Freund? Federico Girosiy yCenter for Biological and Computational Learning and ?Operations Research Center Massachusetts Institute of Technology Cambridge, MA, 02139, U.S.A. Abstract We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classi ers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.

positive: Network intrusion detection Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current "open" mode. The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. The intrusion detection problem is becoming a challenging task due to the proliferation of heterogeneous computer networks since the increased connectivity of computer systems gives greater access to outsiders and makes it easier for intruders to avoid identification. Intrusion detection systems (IDSs) are based on the beliefs that an intruder's behavior will be noticeably different from that of a legitimate user and that many unauthorized actions are detectable. Typically, IDSs employ statistical anomaly and rulebased misuse models in order to detect intrusions. A number of prototype IDSs have been developed at several institutions, and some of them have also been deployed on an experimental basis in operational systems. In the present paper, several host-based and network-based IDSs are surveyed, and the characteristics of the corresponding systems are identified. The host-based systems employ the host operating system's audit trails as the main source of input to detect intrusive activity, while most of the network-based IDSs build their detection mechanism on monitored network traffic, and some employ host audit trails as well. An outline of a statistical anomaly detection algorithm employed in a typical IDS is also included.<<ETX>>

positive: Comparison of learning algorithms for handwritten digit recognition This paper compares the performance of several classi er algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition time, and memory requirements.

positive: Efficient Progressive Sampling Having access to massive amounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size rarely is obvious. We analyze methods for progressive samplingusing progressively larger samples as long as model accuracy improves. We explore several notions of efficient progressive sampling. We analyze efficiency relative to induction with all instances; we show that a simple, geometric sampling schedule is asymptotically optimal, and we describe how best to take into account prior expectations of accuracy convergence. We then describe the issues involved in instantiating an efficient progressive sampler, including how to detect convergence. Finally, we provide empirical results comparing a variety of progressive sampling methods. We conclude that progressive sampling can be remarkably efficient .

negative: Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices Indoor localization is one of the key enablers for various application and service areas that rely on precise locations of people, goods, and assets, ranging from home automation and assisted living to increased automation of production and logistic processes and wireless network optimization. Existing solutions provide various levels of precision, which also depends on the complexity of the indoor radio environment. In this paper, we propose two methods for reducing the localization error in indoor non-line-of-sight (NLoS) conditions using raw channel impulse response (CIR) information obtained from ultra-wide band radios requiring no prior knowledge about the radio environment. The methods are based on NLoS channel classification and ranging error regression models, both using convolutional neural networks (CNNs) and implemented in the TensorFlow computational framework. We first show that NLoS channel classification using raw CIR data outperforms existing approaches that are based on derived input signal features. We further demonstrate that the predicted NLoS channel state and predicted ranging error information, used in combination with least squares (LS) and weighted LS location estimation algorithms, significantly improve indoor localization performance. We also evaluate the computational performance and suitability of the proposed CNN-based algorithms on various computing platforms with a wide range of different capabilities and show that in a distributed localization system, they can also be used on computationally restricted devices.

negative: Feature Extraction Methods for Time Series Data in SAS ® Enterprise Miner TM Because time series data have a unique data structure, it is not easy to apply some existing data mining tools directly to the data. For example, in classification and clustering problems, each time point is often considered a variable and each time series is considered an observation. As the time dimension increases, the number of variables also increases, in proportion to the time dimension. Therefore, data mining tasks require some feature extraction techniques to summarize each time series in a form that has a significantly lower dimension. This paper describes various feature extraction methods for time series data that are implemented in SAS Enterprise MinerTM. INTRODUCTION Time series data mining has four major tasks: clustering, indexing, classification, and segmentation. Clustering finds groups of time series that have similar patterns. Indexing finds similar time series in order, given a query series. Classification assigns each time series to a known category by using a trained model. Segmentation partitions time series. Time series data can be considered multidimensional data; this means that there is one observation per time unit, and each time unit makes one dimension. Some common data mining techniques are used for the tasks. However, in the real world, each time series is usually a high-dimensional sequence, such as stock market data whose prices change over time or data collected by a medical device. When the time unit is seconds, the dimensions of data that accumulate in just one hour are 3,600. Moreover, the dimensions are highly correlated to one another. The number of variables in training data increases proportionally as the time dimension increases. Most existing data mining tools cannot be used efficiently on time series data without a dimension reduction. Therefore, a dimension reduction is required through feature extraction techniques that map each time series to a lower-dimensional space. The most common techniques of dimension reduction in time series are singular value decomposition (SVD), discrete Fourier transformation (DFT), discrete wavelet transformation (DWT), and line segment methods. Some classical time series analyses, such as seasonal, trend, seasonal decomposition, and correlation analyses, are also used for feature extraction. This paper shows how to use SAS Enterprise Miner to implement the feature extraction techniques. But it does not attempt to explain these time series techniques in detail and instead invites the interested reader to explore some of the literature on the subject: Keogh and Pazzani (2000a), Keogh et al. (2000), and Gavrilov et al. (2000) for time series dimensionality reduction and the TIMESERIES procedure documentation in SAS/ETS for the classical time series analyses. This paper is organized as follows. First, it explains feature extraction and dimension reduction techniques that use classical time series analysis, and then it describes feature extraction techniques that use some well-known mathematical functions for dimension reduction. Next, it demonstrates the performance of dimension reduction techniques by presenting some examples. The paper uses SAS Enterprise Miner 13.1 to demonstrate these techniques.

negative: On Evaluation of 6D Object Pose Estimation A pose of a rigid object has 6 degrees of freedom and its full knowledge is required in many robotic and scene understanding applications. Evaluation of 6D object pose estimates is not straightforward. Object pose may be ambiguous due to object symmetries and occlusions, i.e. there can be multiple object poses that are indistinguishable in the given image and should be therefore treated as equivalent. The paper defines 6D object pose estimation problems, proposes an evaluation methodology and introduces three new pose error functions that deal with pose ambiguity. The new error functions are compared with functions commonly used in the literature and shown to remove certain types of non-intuitive outcomes. Evaluation tools are provided at: https : //github.com/thodan/obj pose eval

negative: Bach 10 Dataset---- A Versatile Polyphonic Music Dataset This is a polyphonic music dataset which can be used for versatile research problems, such as Multi-pitch Estimation and Tracking, Audio-score Alignment, Source Separation, etc. This dataset consists of the audio recordings of each part and the ensemble of ten pieces of four-part J.S. Bach chorales, as well as their MIDI scores, the ground-truth alignment between the audio and the score, the ground-truth pitch values of each part and the ground-truth notes of each piece. The audio recordings of the four parts (Soprano, Alto, Tenor and Bass) of each piece are performed by violin, clarinet, saxophone and bassoon, respectively.

negative: Deep Recurrent Regression for Facial Landmark Detection We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple long short-term memory components. Extensive evaluations on several benchmark data sets show that the proposed deep architecture has superior performance against the state-of-the-art methods.

negative: The Lone-Actor Terrorist and the TRAP-18 An open source sample of 111 lone-actor terrorists from the United States and Europe were studied through the lens of the Terrorist Radicalization Assessment Protocol (TRAP-18). This investigative template consists of 8 proximal warning behaviors and 10 distal characteristics for active risk management or active monitoring, respectively, by national security threat assessors. Several aspects of criterion validity were tested in this known outcome sample. Seventy percent of the terrorists were positive for at least half or more of the indicators. When the sample was divided into Islamic extremists, right-wing extremists, and single-issue terrorists, there were no significant differences across all 18 indicators except for 4. When the sample was divided according to successful versus thwarted attackers, the successful attackers were significantly more fixated, creative, and innovative, and failed to have a prior sexually intimate pair bond. They were significantly less likely to have displayed pathway warning behavior and be dependent on a virtual community of likeminded true believers. Effect sizes were small to medium (! " 0.190–0.317). The TRAP-18 appears to have promise as an eventual structured professional judgment risk assessment instrument according to some of the individual terrorist content domains outlined by Monahan (2012, 2016).

negative: Word and Phrase Translation with word2vec Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements. The information captured by these embeddings can be exploited for bilingual translation by learning a transformation matrix that allows matching relative positions across two monolingual vector spaces. This method aims to identify high-quality candidates for word and phrase translation more costeffectively from unlabeled data. This paper expands the scope of previous attempts of bilingual translation to four languages (English, German, Spanish, and French). It shows how to process the source data, train a neural network to learn the high-dimensional embeddings for individual languages and expands the framework for testing their quality beyond the English language. Furthermore, it shows how to learn bilingual transformation matrices and obtain candidates for word and phrase translation, and assess their quality.

negative: Feature Selection Methods: Genetic Algorithms vs. Greedy-like Search This paper presents a comparison between two feature selection methods, the Importance Score (IS) which is based on a greedy-like search and a genetic algorithm-based (GA) method, in order to better understand their strengths and limitations and their area of application. The results of our experiments show a very strong relation between the nature of the data and the behavior of both systems. The Importance Score method is more efficient when dealing with little noise and small number of interacting features, while the genetic algorithms can provide a more robust solution at the expense of increased computational effort.

negative: Fast texture synthesis using tree-structured vector quantization Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.

negative: ClothCap: seamless 4D clothing capture and retargeting Designing and simulating realistic clothing is challenging. Previous methods addressing the capture of clothing from 3D scans have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our ClothCap approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the minimally clothed body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. ClothCap is able to capture a clothed person in motion, extract their clothing, and retarget the clothing to new body shapes; this provides a step towards virtual try-on.

negative: Representation and Mimesis in Generative Art : Creating Fifty Sisters Fifty Sisters is a generative artwork commissioned for the Ars Electronica Museum in Linz. The work consists of fifty 1m ∞ 1m images of computer-synthesized plant-forms, algorithmically ‘grown’ from computer code using artificial evolution and generative grammars. Each plant-like form is derived from the primitive graphic elements of oil company logos. The title of the work refers to the original ‘Seven Sisters’ — a cartel of seven oil companies that dominated the global petrochemical industry and Middle East oil production from the mid–1940s until the oil crisis of the 1970s. In this paper I discuss the issue of representation in generative art and how dialogues in mimesis inform the production of a generative artwork, using Fifty Sisters as an example. I also provide information on how these concepts translate into the technical and how issues of representation necessarily pervade all computer-based generative art. C o m p u t i n g C o m m u n i c a t i o n A e s t h e t i c s a n d X . B e r g a m o , I t a l y . x c o a x . o r g

negative: Arrays in Blitz++ Numeric arrays in Blitz++ rival the eeciency of Fortran, but without any extensions to the C++ language. Blitz++ has features unavailable in Fortran 90/95, such as arbitrary transpose operations, array renaming, tensor notation, partial reductions, multicomponent arrays and stencil operators. The library handles parsing and analysis of array expressions on its own using the expression templates technique, and performs optimizations (such as loop transformations) which have until now been the responsibility of compilers.

negative: Integration of a Wireless I/O Interface for PROFIBUS and PROFINET for Factory Automation Cost related to the wiring of sensors and actuators in the manufacturing industry is rapidly increasing with the number of field devices. Complex engineering as well as cable wear and tear are important factors. For over a decade, fieldbuses have been the dominating approach to simplify installation, reduce engineering efforts, and increase reliability at the field level. The next step in this process is to introduce wireless communication in certain problematic parts of the field network. In order to do this, a reliable real-time communication system, designed to cope with the harsh environments where factory automation systems are employed, is required. The wireless interface for sensors and actuators (WISA), with its two subconcepts WISA-power and WISA-com, is such a system. This paper discusses how the WISA concept can be efficiently integrated into wired field networks for factory automation-both state-of-the-art [process fieldbus decentral peripherals (PROFIBUS DP)] and new (PROFINET IO) networks. We propose amendments to WISA, which will improve the 802.11b/g coexistence and harmonize the integration of WISA for sensor/actuator communication in, e.g., PROFINET IO systems. The suggested improvements relate to both the radio protocol in order to improve coexistence with other wireless technologies as well as concepts for the integration of WISA into the field network of PROFINET/PROFIBUS.

negative: Dual-band circularly polarized microstrip antenna A new dual-band circularly polarized microstrip antenna is presented experimentally. In this paper, using dual-radiator a new design dual-band antenna can be obtained. The frequency ratio of this design is flexible which more than 1.4.

negative: A Transceiver Module for FMCW Radar Sensors Using 94-GHz Dot-Type Schottky Diode Mixer In this study, we fabricated a 94-GHz transceiver module for a millimeter-wave (MMW) frequency modulation continuous wave (FMCW) radar sensor. The transceiver modules consist of a waveguide voltage-controlled oscillator (VCO) and Rx module using a single balanced mixer. We designed a mixer with a conversion loss of 6.4 dB, without using an amplifier. Also, the waveguide VCO consisted of an InP Gunn diode, a varactor diode, two bias posts with LPF, and a Magic Tee for the MMW radar transceiver. The fabricated VCO has a tuning range of 1280 MHz by a varactor bias of 0~20 V, 1.69% linearity range of 680 MHz, and current consumption of 154 to 157 mA. The completed module has a good conversion loss of 10.6 dB with an LO power of 11.4 dBm at 94 GHz. With this RF and LO input power, the conversion loss was maintained between 10.2-11.5 dB in the RF frequency range of 93.67-94.95 GHz.

negative: The Spectrum Kernel: A String Kernel for SVM Protein Classification We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.

negative: A half-bridge resonant inverter with three-phase PWM rectifier for induction heating A half-bridge resonant inverter with three-phase PWM rectifier for induction heating is proposed in this paper. An adjustable frequency limit is introduced to maintain the inverter's switching frequency at a value higher than the resonant frequency for zero voltage operation. In case of no work piece in the induction coil, the dc bus can be adjusted while the switching frequency of the inverter remains at the limit. The important advantages of the proposed system are reduced switching loss, small size of filter and faster response at a no load condition. The proposed technique is implemented on the created hardware setup. The inverter operates at in a frequency range of 20–40 kHz, while the three-phase PWM rectifier runs at 23.8 kHz to deliver output power at 1.5 kW.

negative: Predicting Friends and Foes in Signed Networks Using Inductive Inference and Social Balance Theory Besides the notion of friendship, trust or support in social networking sites (SNSs), quite often social interactions also reflect users' antagonistic attitude towards each other. Thus, the hidden knowledge contained in social network data can be considered as an important resource to discover the formation of such positive and negative links. In this work, an inductive learning framework is presented to suggest 'friends' and 'foes' links to individuals which envisage the social balance among users in the corresponding friends and foes networks (FFN). First we learn a model by applying C4.5, the most widely adopted decision tree based classification algorithm, to exploit the feature patterns presented in the users' FFN and utilizing it to further predict friend/foe relationship of unknown links. Secondly, a quantitative measure of social balance, balance index, is used to support our decision on the recommendation of new friends and foes links (FFL) to avoid possible imbalance in the extended FFN with newly suggested links. The proposed scheme ensures that the recommendation of new FFLs either maintains or enhances the balancing factor of the existing FFN of an individual. Experimental results show the effectiveness of our proposed schemes.

negative: A review on 3 D micro-additive manufacturing technologies New microproducts need the utilization of a diversity of materials and have complicated three-dimensional (3D) microstructures with high aspect ratios. To date, many micromanufacturing processes have been developed but specific class of such processes are applicable for fabrication of functional and true 3D microcomponents/assemblies. The aptitude to process a broad range of materials and the ability to fabricate functional and geometrically complicated 3D microstructures provides the additive manufacturing (AM) processes some profits over traditional methods, such as lithography-based or micromachining approaches investigated widely in the past. In this paper, 3D micro-AM processes have been classified into three main groups, including scalable micro-AM systems, 3D direct writing, and hybrid processes, and the key processes have been reviewed comprehensively. Principle and recent progress of each 3D micro-AM process has been described, and the advantages and disadvantages of each process have been presented.

negative: Semantic audio content-based music recommendation and visualization based on user preference examples Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user’s musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user’s musical preferences by creating a humanoid cartoon-like character – the Musical Avatar – automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-ofthe-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems. 2012 Elsevier Ltd. All rights reserved.

negative:
An estimation of the number of cells in the human body. BACKGROUND
All living organisms are made of individual and identifiable cells, whose number, together with their size and type, ultimately defines the structure and functions of an organism. While the total cell number of lower organisms is often known, it has not yet been defined in higher organisms. In particular, the reported total cell number of a human being ranges between 10(12) and 10(16) and it is widely mentioned without a proper reference.

AIM
To study and discuss the theoretical issue of the total number of cells that compose the standard human adult organism.

SUBJECTS AND METHODS
A systematic calculation of the total cell number of the whole human body and of the single organs was carried out using bibliographical and/or mathematical approaches.

RESULTS
A current estimation of human total cell number calculated for a variety of organs and cell types is presented. These partial data correspond to a total number of 3.72 × 10(13).

CONCLUSIONS
Knowing the total cell number of the human body as well as of individual organs is important from a cultural, biological, medical and comparative modelling point of view. The presented cell count could be a starting point for a common effort to complete the total calculation.

negative: Galvanic Skin Response : A Physiological Sensor System for Affective Computing system is the change in electrical properties of skin due to variation in physiological and psychological conditions. The change is caused by the degree to which a person's sweat glands are active. Psychological status of a person tends to make the glands active and this change the skin resistance. Drier the skin is, higher will be the skin resistance. This variation in skin resistance ranges from 5kΩ to 25kΩ. In the current work, a subject whose Galvanic skin response (GSR) is to be measured, has been shown/played a movie clipping, images or recorded audio signals. Depending upon the theme, emotion will be evoked in the subject. Due to the change in emotion, GSR varies. This variation in GSR is recorded for a fixed time interval. In the present work, a total of 75 subjects are selected. People from different age groups and social background, both male and female have been carefully considered. Data acquisition and analysis is carried out by using LabVIEW. The results obtained are convincing and follows the ground reality with a high level of accuracy. As the subject also gives the feedback about the emotions he/she experienced, the results obtained are validated.

negative: VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.

negative: A dual-polarity grappa kernel for the robust reconstruction of accelerated EPI data The quality of high-resolution Echo Planar images of the human brain has improved greatly in recent years, enabled by novel multi-channel receiver coil arrays and parallel imaging. However, in regions with local field inhomogeneity, EPI artifacts limit which parts of the brain can be imaged successfully. In this work, we present evidence that certain image artifacts can be attributed to nonlinear phase errors that are present in regions of local susceptibility gradients and certain coil array elements. Because these phase errors cannot be corrected with conventional Nyquist ghost correction, we propose a new method that integrates ghost correction with parallel imaging reconstruction. The proposed Dual-Polarity GRAPPA method operates directly on raw EPI data to estimate k-space data from the under-sampled acquisition while simultaneously correcting inherent EPI phase errors. We present examples of this method successfully removing strong phase-error artifacts in high-resolution 7T EPI data.

negative: Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to phenotypes, or medical concepts, that clinical researchers need or use. Existing phenotyping approaches typically require labor intensive supervision from medical experts. We propose Marble, a novel sparse non-negative tensor factorization method to derive phenotype candidates with virtually no human supervision. Marble decomposes the observed tensor into two terms, a bias tensor and an interaction tensor. The bias tensor represents the baseline characteristics common amongst the overall population and the interaction tensor defines the phenotypes. We demonstrate the capability of our proposed model on both simulated and patient data from a publicly available clinical database. Our results show that Marble derived phenotypes provide at least a 42.8% reduction in the number of non-zero element and also retains predictive power for classification purposes. Furthermore, the resulting phenotypes and baseline characteristics from real EHR data are consistent with known characteristics of the patient population. Thus it can potentially be used to rapidly characterize, predict, and manage a large number of diseases, thereby promising a novel, data-driven solution that can benefit very large segments of the population.

negative: KeLP: a Kernel-based Learning Platform for Natural Language Processing Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernelmachine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks.

negative: Multi-document Summarization Using Support Vector Regression Most multi-document summarization systems follow the extractive framework based on various features. While more and more sophisticated features are designed, the reasonable combination of features becomes a challenge. Usually the features are combined by a linear function whose weights are tuned manually. In this task, Support Vector Regression (SVR) model is used for automatically combining the features and scoring the sentences. Two important problems are inevitably involved. The first one is how to acquire the training data. Several automatic generation methods are introduced based on the standard reference summaries generated by human. Another indispensable problem in SVR application is feature selection, where various features will be picked out and combined into different feature sets to be tested. With the aid of DUC 2005 and 2006 data sets, comprehensive experiments are conducted with consideration of various SVR kernels and feature sets. Then the trained SVR model is used in the main task of DUC 2007 to get the extractive summaries.

negative: Statistical Nested Sensor Array Signal Processing OF THE DISSERTATION Statistical Nested Sensor Array Signal Processing by Keyong Han Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2015 Professor Arye Nehorai, Chair Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral xi information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods.

negative: The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing, and machine learning. This paper introduces the 5th CHiME Challenge, which considers the task of distant multimicrophone conversational ASR in real home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech and recorded by 6 Kinect microphone arrays and 4 binaural microphone pairs. The challenge features a single-array track and a multiple-array track and, for each track, distinct rankings will be produced for systems focusing on robustness with respect to distant-microphone capture vs. systems attempting to address all aspects of the task including conversational language modeling. We discuss the rationale for the challenge and provide a detailed description of the data collection procedure, the task, and the baseline systems for array synchronization, speech enhancement, and conventional and end-to-end ASR.

negative: OpenCL framework for ARM processors with NEON support The state-of-the-art ARM processors provide multiple cores and SIMD instructions. OpenCL is a promising programming model for utilizing such parallel processing capability because of its SPMD programming model and built-in vector support. Moreover, it provides portability between multicore ARM processors and accelerators in embedded systems. In this paper, we introduce the design and implementation of an efficient OpenCL framework for multicore ARM processors. Computational tasks in a program are implemented as OpenCL kernels and run on all CPU cores in parallel by our OpenCL framework. Vector operations and built-in functions in OpenCL kernels are optimized using the NEON SIMD instruction set. We evaluate our OpenCL framework using 37 benchmark applications. The result shows that our approach is effective and promising.

negative: Review of Portable and Low-Cost Sensors for the Ambient Air Monitoring of Benzene and Other Volatile Organic Compounds This article presents a literature review of sensors for the monitoring of benzene in ambient air and other volatile organic compounds. Combined with information provided by stakeholders, manufacturers and literature, the review considers commercially available sensors, including PID-based sensors, semiconductor (resistive gas sensors) and portable on-line measuring devices as for example sensor arrays. The bibliographic collection includes the following topics: sensor description, field of application at fixed sites, indoor and ambient air monitoring, range of concentration levels and limit of detection in air, model descriptions of the phenomena involved in the sensor detection process, gaseous interference selectivity of sensors in complex VOC matrix, validation data in lab experiments and under field conditions.

negative: Chunking with Support Vector Machines We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.


query: Morphological Computation – A Potential Solution for the Control Problem in Soft Robotics

positive: An overview of reservoir computing: theory, applications and implementations Training recurrent neural networks is hard. Recently it has however been discovered that it is possible to just construct a random recurrent topology, and only train a single linear readout layer. State-ofthe-art performance can easily be achieved with this setup, called Reservoir Computing. The idea can even be broadened by stating that any high dimensional, driven dynamic system, operated in the correct dynamic regime can be used as a temporal ‘kernel’ which makes it possible to solve complex tasks using just linear post-processing techniques. This tutorial will give an overview of current research on theory, application and implementations of Reservoir Computing.

positive: Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.

positive: Adaptive Nonlinear System Identification with Echo State Networks Echo state networks (ESN) are a novel approach to recurrent neural network training. An ESN consists of a large, fixed, recurrent "reservoir" network, from which the desired output is obtained by training suitable output connection weights. Determination of optimal output weights becomes a linear, uniquely solvable task of MSE minimization. This article reviews the basic ideas and describes an online adaptation scheme based on the RLS algorithm known from adaptive linear systems. As an example, a 10-th order NARMA system is adaptively identified. The known benefits of the RLS algorithms carryover from linear systems to nonlinear ones; specifically, the convergence rate and misadjustment can be determined at design time.

positive: Spine dynamics as a computational resource in spine-driven quadruped locomotion Recent results suggest that compliance and non-linearity in physical bodies of soft robots may not be disadvantageous properties with respect to control, but rather of advantage. In the context of morphological computation one could see such complex structures as potential computational resources. In this study, we implement and exploit this view point in a spine-driven quadruped robot called Kitty by using its flexible spine as a computational resource. The spine is an actuated multi-joint structure consisting of a sequence of soft silicone blocks. Its complex dynamics are captured by a set of force sensors and used to construct a closed-loop to drive the motor commands. We use simple static, linear readout weights to combine the sensor values to generate multiple gait patterns (bounding, trotting, turning behavior). In addition, we demonstrate the robustness of the setup by applying strong external perturbations in form of additional loads. The system is able to fully recover to its nominal gait patterns (which are encoded in the linear readout weights) after the perturbation has vanished.

positive: Variable impedance actuators: A review Variable Impedance Actuators (VIA) have received increasing attention in recent years as many novel applications involving interactions with an unknown and dynamic environment including humans require actuators with dynamics that are not well-achieved by classical stiff actuators. This paper presents an overview of the different VIAs developed and proposes a classification based on the principles through which the variable stiffness and damping are achieved. The main classes are active impedance by control, inherent compliance and damping actuators, inertial actuators, and combinations of them, which are then further divided into subclasses. This classification allows for designers of new devices to orientate and take inspiration and users of VIA’s to be guided in the design and implementation process for their targeted application.

negative: The politics of comments: predicting political orientation of news stories with commenters' sentiment patterns Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification.

negative: A machine learning approach for filtering Monte Carlo noise The most successful approaches for filtering Monte Carlo noise use feature-based filters (e.g., cross-bilateral and cross non-local means filters) that exploit additional scene features such as world positions and shading normals. However, their main challenge is finding the optimal weights for each feature in the filter to reduce noise but preserve scene detail. In this paper, we observe there is a complex relationship between the noisy scene data and the ideal filter parameters, and propose to learn this relationship using a nonlinear regression model. To do this, we use a multilayer perceptron neural network and combine it with a matching filter during both training and testing. To use our framework, we first train it in an offline process on a set of noisy images of scenes with a variety of distributed effects. Then at run-time, the trained network can be used to drive the filter parameters for new scenes to produce filtered images that approximate the ground truth. We demonstrate that our trained network can generate filtered images in only a few seconds that are superior to previous approaches on a wide range of distributed effects such as depth of field, motion blur, area lighting, glossy reflections, and global illumination.

negative: Connecting Intelligent Things in Smart Hospitals Using NB-IoT The widespread use of Internet of Things (IoT), especially smart wearables, will play an important role in improving the quality of medical care, bringing convenience for patients and improving the management level of hospitals. However, due to the limitation of communication protocols, there exists non unified architecture that can connect all intelligent things in smart hospitals, which is made possible by the emergence of the Narrowband IoT (NB-IoT). In light of this, we propose an architecture to connect intelligent things in smart hospitals based on NB-IoT, and introduce edge computing to deal with the requirement of latency in medical process. As a case study, we develop an infusion monitoring system to monitor the real-time drop rate and the volume of remaining drug during the intravenous infusion. Finally, we discuss the challenges and future directions for building a smart hospital by connecting intelligent things.

negative: MADMAC: Multiple Attribute Decision Methodology for Adoption of Clouds Cloud Adoption decisions tend to involve multiple, conflicting criteria (attributes) with incommensurable units of measurements, which must be compared among multiple alternatives using imprecise and incomplete available information. Multi-attribute Decision Making (MADM) has been shown to provide a rational basis to aid decision making in such scenarios. We present a MADMAC framework for cloud adoption, consisting of 3 Decision Areas (DA) referred to as the Cloud Switch, Cloud Type and Vendor Choice. It requires the definition of Attributes, Alternatives and Attribute Weights, to construct a Decision Matrix and arrive at a relative ranking to identify the optimal alternative. We also present a taxonomy organized in a two level hierarchy: Server-centric clouds, Client-centric clouds and Mobile-centric clouds, which further map to detailed, specific applications or workloads. DSS presented showing algorithms derived from MADMAC can compute and optimize CA decisions separately for the three stages, where the attributes differently influence CA decisions. A modified Wide-band Delphi method is proposed for assessing the relative weights for each attribute, by workload. Relative ranks are calculated using these weights, and the Simple Additive Weighting (SAW) method is used to generate value functions for all the alternatives, and rank the alternatives by their value to finally choose the best alternative. Results from application of the method to four different types of workloads show that the method converges on reasonable cloud adoption decisions. MADMAC's key advantage is its fully quantitative and iterative convergence approach based on proven multi-attribute decision methods, which enables decision makers to comparatively assess the relative robustness of alternative cloud adoption decisions in a defensible manner. Being amenable to automation, it can respond well to even complex arrays of decision criteria inputs, unlike human decision makers. It can be implemented as a web-based DSS to readily support cloud decision making world-wide, and improved further using fuzzy TOPSIS methods, to address concerns about preferential inter-dependence of attributes, insufficient input data or judgment expertise.

negative: BRUTE: A High Performance and Extensibile Traffic Generator Evaluating the performance of high speed networks is a critical task due to the lack of reliable tools to generate traffic workloads at high rates and to the inaccessibility of network equipments. To address the problem of reliable device testing, we developed an extensible traffic generator, called BRUTE (Browny and RobUst Traffic Engine), running on top of Linux operating system. BRUTE takes advantage of the Linux kernel potential in order to accurately generate traffic flows up to very high bitrates. This software is an extensible framework that provides a number of optimized functions that easily allow the implementation of ad hoc modules for customized traffic patterns generation. A number of library modules implementing common traffic profiles (like CBR, Poisson process and Poissonian Arrival of Burst process) are already implemented and integrated into the package. Performance of BRUTE has been compared to the one of several widespread traffic generators over Fast-Ethernet and Gigabit-Ethernet networks. The results of our tests show the superior performance of BRUTE in terms of both the achieved throughput and the level of bitrate precision, under all possible values of the frame length.

negative: Nuclei segmentation for computer-aided diagnosis of breast cancer Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.

negative: Environmental sound classification with convolutional neural networks This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds. A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectrograms) with deltas. The accuracy of the network is evaluated on 3 public datasets of environmental and urban recordings. The model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches.

negative: Normally-off AlGaN/GaN HFET with p-type GaN gate and AlGaN buffer A 1.5 A normally-off GaN transistor for power applications in p-type GaN gate technology with a modified epitaxial concept is presented. A higher threshold voltage is achieved while keeping the on-state resistance low by using an Al-GaN buffer instead of a GaN buffer. Additionally, the AlGaN buffer acts as a back-barrier and suppresses source-drain punch-through currents in the off-state. P-GaN gate GaN transistors with AlGaN buffer will therefore yield higher breakdown voltages as compared to standard GaN buffer versions which results in an excellent VBr-to-RON ratio. The proposed normally-off technology shows save operation under elevated ambient temperature up to 200 °C without thermal runaway. In contrast to standard normally-on AlGaN/GaN HEMTs, a reverse diode operation is possible for offstate conditions which may enable improved inverter circuits.

negative: Directed Multi-Objective Optimization While evolutionary computing inspired approaches to multi-objective optimization have many advantages over conventional approaches; they generally do not explicitly exploit directional/gradient information. This can be inefficient if the underlying objectives are reasonably smooth, and this may limit the application of such approaches to real-world problems. This paper develops a local framework for such problems by geometrically analyzing the multiobjective concepts of descent, diversity and convergence/optimality. It is shown that locally optimal, multi-objective descent direction can be calculated that maximally reduce all the objectives and a local sub-space also exists that is a basis for diversity updates. Local convergence of a point towards the optimal Pareto set is therefore assured. The concept of a population of points is also considered and it is shown that it can be used to locally increase the diversity of the population while still ensuring convergence and a method to extract the local directional information from the population is also described. The paper describes and introduces the basic theoretical concepts as well as demonstrating how they are used on simple test problems.

negative: The Usable Privacy Policy Project : Combining Crowdsourcing , Machine Learning and Natural Language Processing to Semi-Automatically Answer Those Privacy Questions Users Care About Natural language privacy policies have become a de facto standard to address expectations of “notice and choice” on the Web. However, users generally do not read these policies and those who do read them struggle to understand their content. Initiatives aimed at addressing this problem through the development of machine-readable standards have run into obstacles, with many website operators showing reluctance to commit to anything more than what they currently do. This project builds on recent advances in natural language processing, privacy preference modeling, crowdsourcing, formal methods, and privacy interface design to develop a practical framework based on websites’ existing natural language privacy policy that empowers users to more meaningfully control their privacy, without requiring additional cooperation from website operators. Our approach combines fundamental research with the development of scalable technologies to (1) semi-automatically extract key privacy policy features from natural language privacy policies, and (2) present these features to users in an easy-to-digest format that enables them to make more informed privacy decisions as they interact with different websites. This work will also involve the systematic collection and analysis of website privacy policies, looking for trends and deficiencies both in the wording and content of these policies across different sectors and using this analysis to inform public policy. This report outlines the project’s research agenda and overall approach.

negative: Revisiting Snodgrass and Vanderwart's object pictorial set: the role of surface detail in basic-level object recognition. Theories of object recognition differ to the extent that they consider object representations as being mediated only by the shape of the object, or shape and surface details, if surface details are part of the representation. In particular, it has been suggested that color information may be helpful at recognizing objects only in very special cases, but not during basic-level object recognition in good viewing conditions. In this study, we collected normative data (naming agreement, familiarity, complexity, and imagery judgments) for Snodgrass and Vanderwart's object database of 260 black-and-white line drawings, and then compared the data to exactly the same shapes but with added gray-level texture and surface details (set 2), and color (set 3). Naming latencies were also recorded. Whereas the addition of texture and shading without color only slightly improved naming agreement scores for the objects, the addition of color information unambiguously improved naming accuracy and speeded correct response times. As shown in previous studies, the advantage provided by color was larger for objects with a diagnostic color, and structurally similar shapes, such as fruits and vegetables, but was also observed for man-made objects with and without a single diagnostic color. These observations show that basic-level 'everyday' object recognition in normal conditions is facilitated by the presence of color information, and support a 'shape + surface' model of object recognition, for which color is an integral part of the object representation. In addition, the new stimuli (sets 2 and 3) and the corresponding normative data provide valuable materials for a wide range of experimental and clinical studies of object recognition.

negative: A shape-based similarity measure for time series data with ensemble learning This paper introduces a shape-based similarity measure, called the angular metric for shape similarity (AMSS), for time series data. Unlike most similarity or dissimilarity measures, AMSS is based not on individual data points of a time series but on vectors equivalently representing it. AMSS treats a time series as a vector sequence to focus on the shape of the data and compares data shapes by employing a variant of cosine similarity. AMSS is, by design, expected to be robust to time and amplitude shifting and scaling, but sensitive to short-term oscillations. To deal with the potential drawback, ensemble learning is adopted, which integrates data smoothing when AMSS is used for classification. Evaluative experiments reveal distinct properties of AMSS and its effectiveness when applied in the ensemble framework as compared to existing measures.

negative: Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks. An analysis of its internal activation patterns reveals three problems: Most importantly, the lack of key-value separation makes the address distribution resulting from content-based look-up noisy and flat, since the value influences the score calculation, although only the key should. Second, DNC’s deallocation of memory results in aliasing, which is a problem for content-based look-up. Thirdly, chaining memory reads with the temporal linkage matrix exponentially degrades the quality of the address distribution. Our proposed fixes of these problems yield improved performance on arithmetic tasks, and also improve the mean error rate on the bAbI question answering dataset by 43%.

negative: Supervised N-gram topic model We propose a Bayesian nonparametric topic model that rep- resents relationships between given labels and the corre- sponding words/phrases, from supervised articles. Unlike existing supervised topic models, our proposal, supervised N-gram topic model (SNT), focuses on both a number of topics and power-law distribution in the word frequencies to extract topic specific N-grams. To achieve this goal, SNT takes a Bayesian nonparametric approach to topic sampling, which generates word distribution jointly with the given variable in textual order, and then form each N-gram word as a hierarchy of Pitman-Yor process priors. Experiments on labeled text data show that SNT is useful as a generative model for discovering more phrases that complement human experts and domain specific knowledge than the existing al- ternatives. The results show that SNT can be applied to various tasks such as automatic annotation.

negative: A Planning based Framework for Essay Generation Generating an article automatically with computer program is a challenging task in artificial intelligence and natural language processing. In this paper, we target at essay generation, which takes as input a topic word in mind and generates an organized article under the theme of the topic. We follow the idea of text planning (Reiter and Dale, 1997) and develop an essay generation framework. The framework consists of three components, including topic understanding, sentence extraction and sentence reordering. For each component, we studied several statistical algorithms and empirically compared between them in terms of qualitative or quantitative analysis. Although we run experiments on Chinese corpus, the method is language independent and can be easily adapted to other language. We lay out the remaining challenges and suggest avenues for future research.

negative: Automatic Personality and Interaction Style Recognition from Facebook Profile Pictures In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.

negative: A Genetic Algorithm Approach for Creating Neural-Network Ensembles A neural-network ensemble is a successful technique where the outputs of a set of separately trained neural networks are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm called Addemup that uses genetic algorithms to explicitly search for a highly diverse set of accurate trained networks. Addemup works by first creating an initial population, then uses genetic operators to continually create new networks, keeping the set of networks that are highly accurate while disagreeing with each other as much as possible. Experiments on four real-world domains show that Addemup is able to generate a set of trained networks that is more accurate than several existing ensemble approaches. Experiments also show that Addemup is able to effectively incorporate prior knowledge, if available, to improve the quality of its ensemble.

negative: Client-Server Computing in Mobile Environments Recent advances in wireless data networking and portable information appliances have engendered a new paradigm of computing, called mobile computing, in which users carrying portable devices have access to data and information services regardless of their physical location or movement behavior. In the meantime, research addressing information access in mobile environments has proliferated. In this survey, we provide a concrete framework and categorization of the various ways of supporting mobile client-server computing for information access. We examine characteristics of mobility that distinguish mobile client-server computing from its traditional counterpart. We provide a comprehensive analysis of new paradigms and enabler concepts for mobile client-server computing, including mobile-aware adaptation, extended client-server model, and mobile data access. A comparative and detailed review of major research prototypes for mobile information access is also presented.

negative: Android Forensics: Simplifying Cell Phone Examinations. It is hardly appropriate to call the devices many use to receive the occasional phone call a telephone any more. The capability of these devices is growing, as is the number of people utilizing them. By the end of 2009, 46.3% of mobile phones in use in the United States were reported to be smart phones (AdMob, 2010). With the increased availability of these powerful devices, there is also a potential increase for criminals to use this technology as well. Criminals could use smart phones for a number of activities such as committing fraud over e-mail, harassment through text messages, trafficking of child pornography, communications related to narcotics, etc. The data stored on smart phones could be extremely useful to analysts through the course of an investigation. Indeed, mobile devices are already showing themselves to have a large volume of probative information that is linked to an individual with just basic call history, contact, and text message data; smart phones contain even more useful information, such as e-mail, browser history, and chat logs. Mobile devices probably have more probative information that can be linked to an individual per byte examined than most computers -and this data is harder to acquire in a forensically proper fashion. Part of the problem lies in the plethora of cell phones available today and a general lack of hardware, software, and/or interface standardization within the industry. These differences range from the media on which data is stored and the file system to the operating system and the effectiveness of certain tools. Even different model cell phones made by the same manufacture may require different data cables and software to access the phone's information. The good news is there are numerous people in the field working on making smart phone forensics easier. Already there is material available on how to conduct an examination on Blackberry phones and a growing number of resources about the iPhone. However, there is a new smart phone OS on the market named Android and it will likely gain in appeal and market share over the next year. While Android initially launched with only one phone on T-Mobile, phones are now available on Sprint, Verizon and AT&T as well.

negative: Soft start-up for high frequency LLC resonant converter with optimal trajectory control This paper investigates the soft start-up for high frequency LLC resonant converter with optimal trajectory control. Two methods are proposed to realize soft start-up for high frequency LLC converter by commercial low-cost microcontrollers (MCU). Both methods can achieve soft start-up with minimum stress and optimal energy delivery. One method is mixed-signal implementation by sensing resonant tank to minimize the digital delay. Another method is digital implementation with look-up table. Experimental results are demonstrated on 500kHz 1kW 400V/12V LLC converter.

negative: Letter-based classification of Arabic scripts style in ancient Arabic manuscripts: Preliminary results Classifying ancient Arabic manuscripts based on handwriting styles is one of the important roles in the field of paleography. Recognizing the style of handwriting in Arabic manuscripts helps in identifying the origin and date of ancient documents. In this paper we proposed using segmented letters from Arabic manuscripts to recognize handwriting style. Both Gabor Filters (GF) and Local Binary Pattern (LBP) are used to extract features from letters. The fused features are sent to Support Vector Machine (SVM) classifier. Experimental results have been implemented using manuscripts images from the Qatar National Library (QNL) and other online datasets. Better results are achieved when both GF and LBP descriptors are combined. The recognized Handwritten Arabic styles are Diwani, Kufic, Naskh, Farsi, Ruq'ah and Thuluth.

negative: Managing Knowledge in Organizations: An Integrative Framework and Review of Emerging Themes In this concluding article to the Management Science special issue on “Managing Knowledge in Organizations: Creating, Retaining, and Transferring Knowledge,” we provide an integrative framework for organizing the literature on knowledge management. The framework has two dimensions. The knowledge management outcomes of knowledge creation, retention, and transfer are represented along one dimension. Properties of the context within which knowledge management occurs are represented on the other dimension. These properties, which affect knowledge management outcomes, can be organized according to whether they are properties of a unit (e.g., individual, group, organization) involved in knowledge management, properties of relationships between units or properties of the knowledge itself. The framework is used to identify where research findings about knowledge management converge and where gaps in our understanding exist. The article discusses mechanisms of knowledge management and how those mechanisms affect a unit’s ability to create, retain and transfer knowledge. Emerging themes in the literature on knowledge management are identified. Directions for future research are suggested. (Knowledge Management; Organizational Learning; Knowledge Transfer; Innovation; Organizational Memory )

negative: Broken Promises : An Experiment We test whether promises per se are effective in enhancing cooperative behavior in a form of trust game. In a new treatment, rather than permitting free-form messages, we instead allow only a bare promise-only message to be sent (or not). We find that bare promises are much less effective in achieving good social outcomes than free-form messages; in fact, bare promise-only messages lead to behavior that is much the same as when no messages are feasible. Our design also permits us to test the predictions of guilt aversion against the predictions of lying aversion. Our experimental results provide evidence that mainly supports the guilt-aversion predictions, but we also find some support for the presence of lying aversion.

negative: Adversarial Perturbations Against Deep Neural Networks for Malware Classification Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs. These inputs are derived from regular inputs by minor yet carefully selected perturbations that deceive machine learning models into desired misclassifications. Existing work in this emerging field was largely specific to the domain of image classification, since the highentropy of images can be conveniently manipulated without changing the images’ overall visual appearance. Yet, it remains unclear how such attacks translate to more securitysensitive applications such as malware detection–which may pose significant challenges in sample generation and arguably grave consequences for failure. In this paper, we show how to construct highly-effective adversarial sample crafting attacks for neural networks used as malware classifiers. The application domain of malware classification introduces additional constraints in the adversarial sample crafting problem when compared to the computer vision domain: (i) continuous, differentiable input domains are replaced by discrete, often binary inputs; and (ii) the loose condition of leaving visual appearance unchanged is replaced by requiring equivalent functional behavior. We demonstrate the feasibility of these attacks on many different instances of malware classifiers that we trained using the DREBIN Android malware data set. We furthermore evaluate to which extent potential defensive mechanisms against adversarial crafting can be leveraged to the setting of malware classification. While feature reduction did not prove to have a positive impact, distillation and re-training on adversarially crafted samples show promising results.

negative:
miRCancer: a microRNA-cancer association database constructed by text mining on literature MOTIVATION
Research interests in microRNAs have increased rapidly in the past decade. Many studies have showed that microRNAs have close relationships with various human cancers, and they potentially could be used as cancer indicators in diagnosis or as a suppressor for treatment purposes. There are several databases that contain microRNA-cancer associations predicted by computational methods but few from empirical results. Despite the fact that abundant experiments investigating microRNA expressions in cancer cells have been carried out, the results have remain scattered in the literature. We propose to extract microRNA-cancer associations by text mining and store them in a database called miRCancer.

RESULTS
The text mining is based on 75 rules we have constructed, which represent the common sentence structures typically used to state microRNA expressions in cancers. The microRNA-cancer association database, miRCancer, is updated regularly by running the text mining algorithm against PubMed. All miRNA-cancer associations are confirmed manually after automatic extraction. miRCancer currently documents 878 relationships between 236 microRNAs and 79 human cancers through the processing of >26 000 published articles.

AVAILABILITY
miRCancer is freely available on the web at http://mircancer.ecu.edu/

negative: Soft Robot Arm Inspired by the Octopus The octopus is a marine animal whose body has no rigid structures. It has eight arms composed of a peculiar muscular structure, named a muscular hydrostat. The octopus arms provide it with both locomotion and grasping capabilities, thanks to the fact that their stiffness can change over a wide range and can be controlled through combined contractions of the muscles. The muscular hydrostat can better be seen as a modifiable skeleton. Furthermore, the morphology the arms and the mechanical characteristics of their tissues are such that the interaction with the environment (i.e., water) is exploited to simplify control. Thanks to this effective mechanism of embodied intelligence, the octopus can control a very high number of degrees of freedom, with relatively limited computing resources. From these considerations, the octopus emerges as a good model for embodied intelligence and for soft robotics. The prototype of a robot arm has been built based on an artificial muscular hydrostat inspired to the muscular hydrostat of the Octopus vulgaris. The prototype presents the morphology of the biological model and the broad arrangement of longitudinal and transverse muscles. Actuation is obtained with cables (longitudinally) and with shape memory alloy springs (transversally). The robot arm combines contractions and it can show the basic movements of the octopus arm, like elongation, shortening and bending, in water. © Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2012

negative: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding In 1994, Karl Sims' evolved virtual creatures showed the potential of evolutionary algorithms to produce natural, complex morphologies and behaviors [30]. One might assume that nearly 20 years of improvements in computational speed and evolutionary algorithms would produce far more impressive organisms, yet the creatures evolved in the field of artificial life today are not obviously more complex, natural, or intelligent. Fig. 2 demonstrates an example of similar complexity in robots evolved 17 years apart.

negative: A Hybrid Gripper With Soft Material and Rigid Structures Various robotic grippers have been developed over the past several decades for robotic manipulators. Especially, the soft grippers based on the soft pneumatic actuator (SPA) have been studied actively, since it offers more pliable bending motion, inherent compliance, and a simple morphological structure. However, few studies have focused on simultaneously improving the fingertip force and actuation speed within the specified design parameters. In this study, we developed a hybrid gripper that incorporates both soft and rigid components to improve the fingertip force and actuation speed simultaneously based on three design principles: first, the degree of bending is proportional to the ratio of the rigid structure; second, a concave chamber design is preferred for large longitudinal strain; and third, a round shape between soft and rigid materials increases the fingertip force. The suggested principles were verified using the finite element methods. The improved performance of the hybrid gripper was verified experimentally and compared with the performance of a conventional SPAs. The ability of the hybrid gripper to grasp different objects was evaluated and was applied in a teleoperated system.

negative: Soft robotic glove for combined assistance and at-home rehabilitation This paper presents a portable, assistive, soft robotic glove designed to augment hand rehabilitation for individuals with functional grasp pathologies. The robotic glove utilizes soft actuators consisting of molded elastomeric chambers with fiber reinforcements that induce specific bending, twisting and extending trajectories under fluid pressurization. These soft actuators were mechanically programmed tomatch and support the range of motion of individual fingers. They demonstrated the ability to generate significant force when pressurized and exhibited low impedance when un-actuated. To operate the soft robotic glove, a control hardware system was designed and included fluidic pressure sensors in line with the hydraulic actuators and a closed-loop controller to regulate the pressure. Demonstrations with the complete system were performed to evaluate the ability of the soft robotic glove to carry out gross and precise functional grasping. Compared to existing devices, the soft robotic glove has the potential to increase user freedom and independence through its portable waist belt pack and open palm design. © 2014 Elsevier B.V. All rights reserved.

negative: Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

negative: Monolithic fabrication of sensors and actuators in a soft robotic gripper In this paper, we present a fluidically functionalized soft-bodied robot that integrates both sensing and actuation. Rather than combining these functions as an afterthought, we design sensors and actuators into the robot at the onset, both reducing fabrication complexity and optimizing component interactions. We utilize liquid metal strain sensors and pneumatic actuators embedded into a silicone robotic gripper. The robot's body is formed by curing the silicone in complex 3D printed molds. We show that the liquid metal strain gauges provide a repeatable resistance response during robotic actuation. We further show that, given sufficient control over other time-dependent variables, it is possible to determine when the robot begins gripping an object during actuation.

negative: Pneumatic muscle actuators within robotic and mechatronic systems Pneumatic artificial muscles (PAMs) as soft, lightweight and compliant actuators have great potential in applications for the actuations of new types of robots and manipulators. The favourable characteristics of fluidic muscles, such as high power-to-weight ratio and safe interaction with humans are also very suitable during the process of musculoskeletally rehabilitating patients and are often used in making artificial orthoses. This technology, despite the problems of control relatng to nonlinear phenomena, may also have wide future applications within industrial and mechatronic systems. This paper presents several experimental systems actuated by PAMs, which have been designed as test models within the fields of mobile robots, mechatronics, fluid power systems and the feedback control education of mechanical engineering students. This paper first presents the design and construction of a four legged walking robot actuated by pneumatic muscles. The robot has a fully autonomous system with a wireless application platform and can be controlled using a cell phone. Then the paper describes the design and construction of the prototype of an actively-powered ankle foot orthosis. This orthosis device actuated by a single PAM is able to provide the appropriate functions required during the rehabilitations of patients and the loss of mobility. Then the paper focuses on the design and control of a ball and beam system with an antagonistic muscle pair for generating the necessary torque for beam rotation. This mechatronic balancing mechanism falls into the category of unstable, under-actuated, multivariable systems with highly nonlinear dynamics. The final section of the article presents the design and control of a single-joint manipulator arm with pneumatic muscle actuators that enable some new features for the controlled systems. Fluid Power 2015 176