Music Education and Its Impact on Students with Special Needs

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Composite Scale of Morningness: psychometric properties, validity with Munich ChronoType Questionnaire and age/sex differences in Poland.

The present study aimed at testing psychometric properties of the Composite Scale of Morningness (CSM) and validating it with mid sleep on free days (MSF) derived from the Munich ChronoType Questionnaire (MCTQ) in Poland, along with analyzing age and sex differences in the CSM and MSF. A sample of 952 Polish residents (62.6% females) aged between 13 and 46 was tested. Additionally, a sample of 33 university students were given MCTQ and filled in a sleep diary for 8 days. MSF derived from MCTQ was related to the one from sleep diary (r=.44). The study revealed good reliability of the CSM (α=.84) and its validity: greater morningness preference was associated with earlier MSF from MCTQ (r=-.52). CSM scores were distributed over its full range, with a mean of 34, and did not differ between sexes, although females were earlier than males by 23minutes in MSF. Regarding age, eveningness estimated with both CSM and MSF was greatest in subjects aged 16-18years, and a shift toward eveningness during puberty and a shift back toward morningness in older age was observed. The Polish version of the CSM consisted of two components of morningness. Cutoff scores were: for evening types (lower 10%) 24 or less, for morning types (upper 10%) 43 or more. The Polish CSM presents good psychometric properties, which are similar to those reported in other language versions, and also presents sex/age patterns similar to those found previously.


Analysis of different PM machines with concentrated windings and flux barriers in stator core

The new stator structure with magnetic flux-barriers in the stator yoke or tooth region represents an efficient method for reducing the sub-harmonics of electric machines with fractional slots, tooth-concentrated windings. In this paper the both flux-barriers techniques are considered during the analysis of different PM machines. The 12-teeth single-layer and double-layer concentrated winding in combination with a 10-poles and 14-poles PM rotor are investigated. For the all machine topologies the new stator design is used to improve their performances and characteristics. The flux-barrier effects on the main machine parameters, such as in the air-gap flux density harmonics, dq-machine parameters, characteristic currents, electromagnetic torque, and so on, are studied carefully. Comparisons performed with the analogous conventional machines (with conventional stator) show that, the new stator design offers significant advantages.


Solving the Multiple Instance Problem with Axis-Parallel Rectangles

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.


A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy-based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field.


Tree-structured survival analysis.

In this note, tree-structured recursive partitioning schemes for classification, probability class estimation, and regression are adapted to cover censored survival analysis. The only assumptions required are those which guarantee identifiability of conditional distributions of lifetime given covariates. Thus, the techniques are applicable to more general situations than are those of the famous semi-parametric model of Cox.


A Brief Introduction to Boosting

Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting. Some examples of recent applications of boosting are also described.


Tree-Based Batch Mode Reinforcement Learning

Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered from a system. In batch mode, i t can be achieved by approximating the so-calledQ-function based on a set of four-tuples (xt ,ut , rt ,xt+1) wherext denotes the system state at timet, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the contro l policy from this Q-function. The Q-function approximation may be obtained from the limit of a s equence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) a d two newly proposed ensemble algorithms, namelyextremelyandtotally randomized trees. We study their performances on several examples and find that the ensemble methods based on regressi on trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accurac y results are provided by the extremely randomized trees.


Visual exploration and evaluation of climate-related simulation data

Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e.g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.


Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation

The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) algorithms can assist scholars in accessing needed publications, in this study, we propose an innovative publication ranking method with PRF by leveraging a number of meta-paths on the heterogeneous bibliographic graph. Different meta-paths on the graph address different ranking hypotheses, whereas the pseudo-relevant papers (from the retrieval results) are used as the seed nodes on the graph. Meanwhile, unlike prior studies, we propose "restricted meta-path" facilitated by a new context-rich heterogeneous network extracted from full-text publication content along with citation context. By using learning-to-rank, we integrate 18 different meta-path-based ranking features to derive the final ranking scores for candidate cited papers. Experimental results with ACM full-text corpus show that meta-path-based ranking with PRF on the new graph significantly (p < 0.0001) outperforms text retrieval algorithms with text-based or PageRank-based PRF.


Convex Formulations for Fair Principal Component Analysis

Though there is a growing literature on fairness for supervised learning, incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first define fairness for dimensionality reduction, and our definition can be interpreted as saying a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs, and we demonstrate their effectiveness using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.


Feature-Rich Memory-Based Classification for Shallow NLP and Information Extraction

Memory-Based Learning (MBL) is based on the storage of all available training data, and similarity-based reasoning for handling new cases. By interpreting tasks such as POS tagging and shallow parsing as classification tasks, the advantages of MBL (implicit smoothing of sparse data, automatic integration and relevance weighting of information sources, handling exceptional data) contribute to state-of-the-art accuracy. However, Hidden Markov Models (HMM) typically achieve higher accuracy than MBL (and other Machine Learning approaches) for tasks such as POS tagging and chunking. In this paper, we investigate how the advantages of MBL, such as its potential to integrate various sources of information, come to play when we compare our approach to HMMs on two Information Extraction (IE) datasets: the well-known Seminar Announcement data set and a new German Curriculum Vitae data set. 1 Memory-Based Language Processing Memory-Based Learning (MBL) is a supervised classification-based learning method. A vector of feature values (an instance) is associated with a class by a classifier that lazily extrapolates from the most similar set (nearest neighbors) selected from all stored training examples. This is in contrast to eager learning methods like decision tree learning [26], rule induction [9], or Inductive Logic Programming [7], which abstract a generalized structure from the training set beforehand (forgetting the examples themselves), and use that to derive a classification for a new instance. In MBL, a distance metric on the feature space defines what are the nearest neighbors of an instance. Metrics with feature weights based on information-theory or other relevance statistics allow us to use rich representations of instances and their context, and to balance the influences of diverse information sources in computing distance. Natural Language Processing (NLP) tasks typically concern the mapping of an input representation (e.g., a series of words) into an output representation (e.g., the POS tags corresponding to each word in the input). Most NLP tasks can therefore easily be interpreted as sequences of classification 34 Zavrel, Daelemans tasks: e.g., given a word and some representation of its context, decide what tag to assign to each word in its context. By creating a separate classification instance (a “moving window” approach) for each word and its context, shallow syntactic or semantic structures can be produced for whole sentences or texts. In this paper, we argue that more semantic and complex input-output mappings, such as Information Extraction, can also effectively be modeled by such a Memory-based classification-oriented framework, and that this approach has a number of very interesting advantages over rivalling methods, most notably that each classification decision can be made dependent on a very rich and diverse set of features. The properties of MBL as a lazy, similarity-based learning method seem make a good fit to the properties of typical disambiguation problems in NLP: • Similar input representations lead to similar output. E.g., words occurring in a similar context in general have the same POS tag. Similarity-based reasoning is the core of MBL. • Many sub-generalizations and exceptions. By keeping in memory all training instances, exceptions included, an MBL approach can capture generalization from exceptional or low-frequency cases according to [12]. • Need for integration of diverse types of information. E.g., in Information Extraction, lexical features, spelling features, syntactic as well as phrasal context features, global text structure, and layout features can potentially be very relevant. • Automatic smoothing in very rich event spaces. Supervised learning of NLP tasks regularly runs into problems of sparse data; not enough training data is available to extract reliable parameters for complex models. MBL incorporates an implicit robust form of smoothing by similarity [33]. In the remainder of this Section, we will show how a memory-, similarity-, and classification-based approach can be applied to shallow syntactic parsing, and can lead to state-of-the-art accuracy. Most of the tasks discussed here can also easily be modeled using Hidden Markov Models (HMM), and often with surprising accuracy. We will discuss the strengths of the HMMs and draw a comparison between the classification-based MBL method and the sequence-optimizing HMM approach (Section 1.2). 1.1 Memory-Based Shallow Parsing Shallow parsing is an important component of most text analysis systems in Text Mining applications such as information extraction, summary generation, and question answering. It includes discovering the main constituents of sentences (NPs, VPs, PPs) and their heads, and determining syntactic relationships like subject, object, adjunct relations between verbs and heads of other constituents. This is an important first step to understanding the who, what, when, and where of sentences in a text. Feature-Rich Memory-Based Classification for Information Extraction 35 In our approach to memory-based shallow parsing, we carve up the syntactic analysis process into a number of classification tasks with input vectors representing a focus item and a dynamically selected surrounding context. These classification tasks can be segmentation tasks (e.g., decide whether a focus word or tag is the start or end of an NP) or disambiguation tasks (e.g., decide whether a chunk is the subject NP, the object NP or neither). Output of some memory-based modules is used as input by other memory-based modules (e.g., a tagger feeds a chunker and the latter feeds a syntactic relation assignment module). Similar ideas about cascading of processing steps have also been explored in other approaches to text analysis: e.g., finite state partial parsing [1,18], statistical decision tree parsing [23], and maximum entropy parsing [30]. The approach briefly described here is explained and evaluated in more detail in [10,11,6] . Chunking The phrase chunking task can be defined as a classification task by generalizing the approach of [28], who proposed to convert NP-chunking to tagging each word with I for a word inside an NP, O for outside an NP, and B for between two NPs). The decision on these so called IOB tags for a word can be made by looking at the Part-of-Speech tag and the identity of the focus word and its local context. For the more general task of chunking other non-recursive phrases, we simply extend the tag set with IOB tags for each type of phrase. To illustrate this encoding with the extended IOB tag set, we can tag the sentence: But/CC [NP the/DT dollar/NN NP] [ADVP later/RB ADVP] [VP rebounded/VBD VP] ,/, [VP finishing/VBG VP] [ADJP slightly/RB higher/R ADJP] [Prep against/IN Prep] [NP the/DT yen/NNS NP] [ADJP although/IN ADJP] [ADJP slightly/RB lower/JJR ADJP] [Prep against/IN Prep] [NP the/DT mark/NN NP] ./. as: But/CCO the/DTI−NP dollar/NNI−NP later/RBI−ADV P rebounded/VBDI−V P ,/,O finishing/VBGI−V P slightly/RBI−ADV P higher/RBRI−ADV P against/INI−Prep the/DTI−NP yen/NNSI−NP although/INI−ADJP slightly/RBB−ADJP lower/JJRI−ADJP against/INI−Prep the/DTI−NP mark/NNI−NP ./.O Table 1 (from [6]) shows the accuracy of this memory-based chunking approach when training and testing on Wall Street Journal material. We report Precision, Recall, and Fβ=1 scores, a weighted harmonic mean of Recall and Precision (Fβ = (β+1)∗P∗R β2∗P+R) ). 1 An online demonstration of the Memory-Based Shallow Parser can be found at http://ilk.kub.nl . 36 Zavrel, Daelemans type precision recall Fβ=1 NPchunks 92.5 92.2 92.3 VPchunks 91.9 91.7 91.8 ADJPchunks 68.4 65.0 66.7 ADVPchunks 78.0 77.9 77.9 Prepchunks 95.5 96.7 96.1 PPchunks 91.9 92.2 92.0 ADVFUNCs 78.0 69.5 73.5 Table 1. Results of chunking–labeling experiments. Reproduced from [6]. Grammatical Relation Finding After POS tagging, phrase chunking and labeling, the last step of the shallow parsing consists of resolving the (types of) attachment between labeled phrases. This is done by using a classifier to assign a grammatical relation (GR) between pairs of words in a sentence. In our approach, one of these words is always a verb, since this yields the most important GRs. The other word (focus) is the head of the phrase which is annotated with this grammatical relation in the treebank (e.g., a noun as head of an NP). An instance for such a pair of words is constructed by extracting a set of feature values from the sentence. The instance contains information about the verb and the focus: a feature for the word form and a feature for the POS of both. It also has similar features for the local context of the focus. Experiments on the training data suggest an optimal context width of two words to the left and one to the right. In addition to the lexical and the local context information, superficial information about clause structure was included: the distance from the verb to the focus, counted in words. A negative distance means that the focus is to the left of the verb. Other features contain the number of other verbs between the verb and the focus, and the number of intervening commas. These features were chosen by manual “feature engineering”. Table 2 shows some of the feature-value instances corresponding to the following sentence (POS tags after the slash, chunks denoted with square and curly brackets, and adverbial functions after the dash): [ADVP Not /RB surprisingly /RB ADVP] ,/, [NP Peter /NNP Miller /NNP NP] ,/, [NP who /WP NP] [VP organized /VBD VP] [NP the /DT conference /NN NP] {PP-LOC [Prep in /IN Prep] [NP New /NNP York /NNP NP] PP-LOC} ,/, [VP does /VBZ not /RB want /VB to /TO come /VB VP] {PP-DIR [Prep to /IN Prep] [NP Paris /NNP NP] PP-DIR} [Prep without /IN Prep] [VP bringing /VBG VP] [NP his /PRP$ wife /NN NP]. Table 3 shows the results of the experiments. In the first row, only POS tag features are used. Other rows show the results when adding several types of chunk informati


An "AI readability" Formula for French as a Foreign Language

This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well as some of the specificities of the FFL context. We report comparisons between several techniques for feature selection and various learning algorithms. Our best model, based on support vector machines (SVM), significantly outperforms previous FFL formulas. We also found that semantic features behave poorly in our case, in contrast with some previous readability studies on English as a first language.


Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series

Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise.


Apple disease classification using color, texture and shape features from images

The presence of diseases in several kinds of fruits is the major factor of production and the economic degradation of the agricultural industry worldwide. An approach for the apple disease classification using color, texture and shape based features is investigated and experimentally verified in this paper. The primary steps of the introduced image processing based method is as follows; 1) infected fruit part detection is done with the help of K-Means clustering method, 2) color, texture and shape based features are computed over the segmented image and combined to form the single descriptor, and 3) multi-class Support Vector Machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with 3 categories of diseases, namely blotch, rot and scab as well as healthy apples. The experimentation points out that the introduced method based is better as compared to the individual features. It also point out that shape feature is not better suited for this purpose.


Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.


Exploring the musical taste of expert listeners: musicology students reveal tendency toward omnivorous taste

Musicology students are engaged with music on an academic level and usually have an extensive musical background. They have a considerable knowledge of music history and theory and listening to music may be regarded as one of their primary occupations. Taken together, these factors qualify them as ≫expert listeners≪, who may be expected to exhibit a specific profile of musical taste: interest in a broad range of musical styles combined with a greater appreciation of ≫sophisticated≪ styles. The current study examined the musical taste of musicology students as compared to a control student group. Participants (n = 1003) completed an online survey regarding the frequency with which they listened to 22 musical styles. A factor analysis revealed six underlying dimensions of musical taste. A hierarchical cluster analysis then grouped all participants, regardless of their status, according to their similarity on these dimensions. The employed exploratory approach was expected to reveal potential differences between musicology students and controls. A three-cluster solution was obtained. Comparisons of the clusters in terms of musical taste revealed differences in the listening frequency and variety of appreciated music styles: the first cluster (51% musicology students/27% controls) showed the greatest musical engagement across all dimensions although with a tendency toward ≫sophisticated≪ musical styles. The second cluster (36% musicology students/46% controls) exhibited an interest in ≫conventional≪ music, while the third cluster (13% musicology students/27% controls) showed a strong liking of rock music. The results provide some support for the notion of specific tendencies in the musical taste of musicology students and the contribution of familiarity and knowledge toward musical omnivorousness. Further differences between the clusters in terms of social, personality, and sociodemographic factors are discussed.


Markov Logic Networks for Optical Chemical Structure Recognition

Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it.


An LTE Base-Station Magnetoelectric Dipole Antenna with Anti-Interference Characteristics and Its MIMO System Application

In this research, a wideband long-term evolution (LTE) base-station antenna with flexible band-notch characteristics is introduced by two window-type slots etched on radiation patches of the magnetoelectric dipole. The presented antenna can provide a band-notch range with an impedance bandwidth of 2.39-2.52 GHz in the band of 1.825-2.925 GHz, which well applies the wideband antenna for LTE with the good anti-interference from wireless local area network narrow communication band. The typical parameters are investigated to show that the antenna with anti-interference characteristics exhibits large scope band-notch adjustability in the whole wideband region. Based on above element, an LTE base station antenna multiple-input, multiple-output system with anti-interference characteristics is investigated and measured to show its good performances: anti-interference, high isolation, high gain, unidirectional radiation, low back radiation, and low cross-polarization.


An AUC-based permutation variable importance measure for random forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html


SiCloPe: Silhouette-Based Clothed People

We introduce a new silhouette-based representation for modeling clothed human bodies using deep generative models. Our method can reconstruct a complete and textured 3D model of a person wearing clothes from a single input picture. Inspired by the visual hull algorithm, our implicit representation uses 2D silhouettes and 3D joints of a body pose to describe the immense shape complexity and variations of clothed people. Given a segmented 2D silhouette of a person and its inferred 3D joints from the input picture, we first synthesize consistent silhouettes from novel view points around the subject. The synthesized silhouettes, which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction. We then infer the texture of the subject’s back view using the frontal image and segmentation mask as input to a conditional generative adversarial network. Our experiments demonstrate that our silhouette-based model is an effective representation and the appearance of the back view can be predicted reliably using an image-to-image translation network. While classic methods based on parametric models often fail for single-view images of subjects with challenging clothing, our approach can still produce successful results, which are comparable to those obtained from multi-view input.


Celeb-500K: A Large Training Dataset for Face Recognition

In this paper, we propose a large training dataset named Celeb-500K for face recognition, which contains 50M images from 500K persons. To better facilitate academic research, we clean Celeb-500K to obtain Celeb-500K-2R, which contains 25M aligned face images from 365K persons. Based on the developed dataset, we achieve state-of-the-art face recognition performance and reveal two important observations on face recognition study. First, metric learning methods have limited performance gain when the training dataset contains a large number of identities. Second, in order to develop an efficient training dataset, the number of identities is more important than the average image number of each identity from the perspective of face recognition performance. Extensive experimental results show the superiority of Celeb-500K and provide a strong support to the two observations.


Random Projection and Hashing

Random Projection is another class of methods used for low-rank matrix approximation. A random projection algorithm projects datapoints from a high-dimensional space R n onto a lower-dimensional subspace R r (r ⌧ n) using a random matrix S 2 R r⇥n. The key idea of random mapping comes from the Johnson-Lindenstrauss lemma[7] (we will explain later in detail) that says " if points in a vector space are projected onto a randomly selected subspace of suitably high dimension, then the distances between points are approximately preserved ". Random projection methods are computationally efficient and sufficiently accurate in practice for dimen-sionality reduction of high-dimensional datasets. Moreover, since complexity of many geometric algorithms depends significantly on the dimension, applying random projection as pre-processing is a common task in many data mining applications. As opposed to column sampling methods, that need to access data for approximating the low-rank subspace, random projections are data oblivious, as their computation involves only a random matrix S. We start with the basic concepts and definitions required to understand random projections techniques. Definition 1 (Column Space). Consider a matrix A 2 R n⇥d (n > d). Notice that as one ranges over all vectors x 2 R d , Ax ranges over all linear combinations of the columns of A and therefore defines a d-dimensional subspace of R n , which we refer to as the column space of A and denote it by C(A). Definition 2 (` 2-Subspace Embedding). A matrix S 2 R r⇥n provides a subspace embedding for C(A) if kSAxk 2 2 = (1 ± ")kAxk 2 2 , 8x 2 R d. Such matrix S provides a low distortion embedding, and is called a (1 ± ") ` 2-subspace embedding. Using an`2-subspace embedding, one can work with SA 2 R r⇥d instead of A 2 R n⇥d. Typically r ⌧ n, so we are working with a smaller matrix that reduces the time/space complexity of many algorithms. However note that definitely r needs to be larger than d if we are talking about the whole subspace R d [11]. Note that subspace embedding does not depend on a particular basis for C(A), that means if we have a matrix U that is an orthonormal basis for C(A), then Ux gives the same subspace as Ax. Therefore if S is an embedding for A, it will be an embedding for U too. Let's consider …


Exploring the relationship between technology acceptance model and usability test

In the past, most studies used the technology acceptance model (TAM) to survey the subjective perception of users in using information technology. The usability test was also used to assess the ease of use of user interfaces. This study introduces a conceptual framework to explore the relationship between user’s beliefs of TAM and usability testing attributes. Usability testing was conducted on an eCampus learning system with a mobile device. TAM data was collected from the participants for analyzing a possible relationship. The findings of this study reveal that TAM results contradict the usability test results in certain areas. The focus of our proposed research model is supported from the causality between perceived ease of use and usability; however, the correlation between perceived usefulness and usability remains unclear.


Non-invasive optical detection of hand gestures

In this paper we present a novel type of sensing technology for hand and finger gesture recognition that utilizes light in the invisible spectrum to detect changes in position and form of body tissue like tendons and muscles. The proposed system can be easily integrated with existing wearable devices. Our approach not only enables gesture recognition but it could potentially double to perform a variety of health related monitoring tasks (e.g. heart rate, stress).


Micro bubble manipulation towards single cell handling tool

We report a series of micro air bubble manipulations (transporting, merging and eliminating of air bubbles) in two-dimensional microchannels filled with a water solution. Air bubbles (~500 nano liters in volume) are driven by electrowetting-on-dielectric (EWOD) principle. By sequentially energizing an array of electrodes covered with dielectric layers, air bubbles can be transported along a programmed path, merged into a single larger one, and eliminated out of a bulk water solution. This bubble manipulation technique can be applied to efficiently handle micro objects. As a proof of concept, we successfully demonstrate in a millimeter scale that a fish egg and a sesame husk are attached to an air bubble and then driven by the bubble in a programmed way. The moving air bubble can push and pull the attached objects of which surface is hydrophilic or hydrophobic. This handling technique will bring a novel, versatile, cost-effective, high throughput tool, enabling us to efficiently transport, trap, and isolate individual micro bio-entities such as cells, functional particles and possibly molecules


Learning and Collective Knowledge Construction With Social Media: A Process-Oriented Perspective

Social media are increasingly being used for educational purposes. The first part of this article briefly reviews literature that reports on educational applications of social media tools. The second part discusses theories that may provide a basis for analyzing the processes that are relevant for individual learning and collective knowledge construction. We argue that a systems-theoretical constructivist approach is appropriate to examine the processes of educational social media use, namely, self-organization, the internalization of information, the externalization of knowledge, and the interplay of externalization and internalization providing the basis of a co-evolution of cognitive and social systems. In the third part we present research findings that illustrate and support this systems-theoretical framework. Concluding, we discuss the implications for educational design and for future research on learning and collective knowledge construction with social media.


Low Drop-Out Voltage Regulators: Capacitor-less Architecture Comparison

Demand for system-on-chip solutions has increased the interest in low drop-out (LDO) voltage regulators which do not require a bulky off-chip capacitor to achieve stability, also called capacitor-less LDO (CL-LDO) regulators. Several architectures have been proposed; however comparing these reported architectures proves difficult, as each has a distinct process technology and specifications. This paper compares CL-LDOs in a unified matter. We designed, fabricated, and tested five illustrative CL-LDO regulator topologies under common design conditions using 0.6?m CMOS technology. We compare the architectures in terms of (1) line/load regulation, (2) power supply rejection, (3) line/load transient, (4) total on-chip compensation capacitance, (5) noise, and (6) quiescent power consumption. Insights on what optimal topology to choose to meet particular LDO specifications are provided.


Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots

Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction [1]. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework [2], the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning [1] or reinforcement learning [3]. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.


Intrusion-Tolerant Autonomous Driving

Fully autonomous driving is one if not the killer application for the upcoming decade of real-time systems. However, in the presence of increasingly sophisticated attacks by highly skilled and well equipped adversarial teams, autonomous driving must not only guarantee timeliness and hence safety. It must also consider the dependability of the software concerning these properties while the system is facing attacks. For distributed systems, fault-and-intrusion tolerance toolboxes already offer a few solutions to tolerate partial compromise of the system behind a majority of healthy components operating in consensus. In this paper, we present a concept of an intrusion-tolerant architecture for autonomous driving. In such a scenario, predictability and recovery challenges arise from the inclusion of increasingly more complex software on increasingly less predictable hardware. We highlight how an intrusion tolerant design can help solve these issues by allowing timeliness to emerge from a majority of complex components being fast enough, often enough while preserving safety under attack through pre-computed fail safes.


Modern FMCW radar - techniques and applications

The range of practical applications for FMCW radars has increased significantly in the last decade or so. This paper renews some of the recent applications and also some of the techniques which are now being applied to improve the versatility and performance of FMCW radars. Significant system developments include the use of moving-target detection techniques, the development of the automotive radar market, and the building of prototypes of active phased arrays using this modulation. Significant technical developments include improved ways of generating the sweep signal, using direct digital synthesis, or quieter oscillators and an improved understanding of the significance of the term 'Low Probability of Intercept.'.


A Literature Review on the Design of Spherical Rolling Robots

There are many advantages to the use of spherical robot designs. The current status of the design of spherical rolling robots is reviewed.


SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism — a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.


CIDEr: Consensus-based image description evaluation

Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.


Mind's eye: A recurrent visual representation for image caption generation

In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans 21.0% of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.


Enhanced Simulated Annealing for Globally Minimizing Functions of Many-Continuous Variables

A new global optimization algorithm for functions of many continuous variables is presented, derived from the basic Simulated annealing method. Our main contribution lies in dealing with high-dimensionality minimization problems, which are often difficult to solve by all known minimization methods with or without gradient. In this article we take a special interest in the variables discretization issue. We also develop and implement several complementary stopping criteria. The original Metropolis iterative random search, which takes place in a Euclidean space Rn, is replaced by another similar exploration, performed within a succession of Euclidean spaces Rp, with p <<n. This Enhanced Simulated Annealing (ESA) algorithm was validated first on classical highly multimodal functions of 2 to 100 variables. We obtained significant reductions in the number of function evaluations compared to six other global optimization algorithms, selected according to previously published computational results for the same set of test functions. In most cases, ESA was able to closely approximate known global optima. The reduced ESA computational cost helped us to refine further the obtained global results, through the use of some local search. We have used this new minimizing procedure to solve complex circuit design problems, for which the objective function evaluation can be exceedingly costly.


Microbial Prevalence and Antimicrobial Resistance in Children and Adolescents with Chronic Rhinosinusitis in South Indian Population

Objective: Chronic rhinosinusitis (CRS) is a common multifactorial upper respiratory disease with a key role of microbes in worsening of disease and its associated co-morbidities. Further, significant region specific variation in patient demographics and antibiotic resistance of causative bacteria are reported to pose difficulty in diagnosis and treatment. In India, studies on the etiology and antibiotic resistance in chronic rhinosinusitis are very meager, especially in children. The present study aimed to determine the prevalence of common causative microbes and their antibiotic resistance in children and adolescents with chronic rhinosinusitis in South Indian population. Subjects and methods: The present study was conducted on 89 children and 99 adolescents with chronic rhinosinusitis who visited MAA ENT Institute, Hyderabad, South India. The study samples were collected under the nasal endoscopic guidance from the middle meatus at first visit and sinuses at surgery. Conventional and VITEK-2 methods were used for identification and antibiotic sensitivity of the microbes. Chi-square test and multinomial logistic regression was applied to determine statistical differences between the variable using PASW v. 18.0 software (SPSS Inc., Chicago, IL). Results: The male-female ratio was 2:1 with an average children age of 8.9 ± 3.65 years and 16.1 ± 1.23 years in adolescents. The risk for adenoids was seen in 49.2 % of children (OR; 2.6: 95% CI: 1.63-4.06) while allergic fungal sinusitis (18.1%, OR: 2.7; 95% CI: 1.12-6.57) and nasal polyps (26.6%, OR: 2.3; 95% CI: 1.07-4.86) was commonly seen in adolescents. About 26.6% of adolescents with fungal positivity also showed bacterial infection. Aspergillus flavus (68%) was the most common fungi identified. Bacterial culture rate was positive in 46.8% of the total subjects of which Streptococcus aureus was the most common bacteria (59.1%) followed by Streptococcus pnuemoniae (21.2%), Klebsiella sp. (11.4%), Pseudomonas aeruginosa (11.4%) and β hemolytic streptococci (1.1%). No Methicillin-resistant Staphylococcus aureus strains could be identified. Streptococcus pneumonia (63.2%) was commonly identified in younger children and Pseudomonas aeruginosa (80%) was mostly seen in adolescents. The frequency of bacterial positivity in adolescents with CRS when compared to CRS children was high and varied between different associated co-morbidities. High antibiotic resistance in Staphylococcus aureus was seen towards gentamicin (73%) and co-trimoxazole (64%), Streptococcus pnuemoniae to gentamicin (58%), cotrimoxazole (68%) and meropenem (32%), Pseudomonas aeruginosa to co-trimoxazole (100%), cefatoximine (60%) and cefatazidime (50%) while Klebsiella sp. to gentamicin (80%) and co-trimoxazole (60%). Streptococcus aureus showed high sensitivity to cefatoximine (95.8%) and Streptococcus pnuemoniae for ofloxacin (100%), ciprofloxacin (89.5%) and cefazolin (89.5%). Pseudomonas aeruginosa showed high sensitivity for amikacin (100%) and ciprofloxacin (80%) and Klebsiella sp. for amikacin (100%) Conclusion: Significant regional specific variation in bacterial etiology that differed with age, severity and comorbidities was observed in children and adolescents with chronic rhinosinusitis. High antimicrobial resistance in the cultures of chronic rhinosinusitis patients at their first visit and also at sinus surgery warrants urgent need for early initiation of personalized interventions for better management of the infectious disease.


Since its introduction nearly 20 years ago, the Byzantine Generals Problem has been the subject of many papers having the scrutiny of the fault tolerance community. Numerous Byzantine tolerant algorithms and architectures have been proposed. However, this problem is not yet sufficiently understood by those who design, build, and maintain systems with high dependability requirements. Today, there are still many misconceptions relating to Byzantine failure, what makes a system vulnerable, and indeed the very nature and reality of Byzantine faults. This paper revisits the Byzantine problem from a practitioner’s perspective. It has the intention to provide the reader with a working appreciation of the Byzantine failure from a practical as well as a theoretical perspective. A discussion of typical failure properties and the difficulties in preventing the associated failure propagation is presented. These are illustrated with real Byzantine failure observations. Finally, various architectural solutions to the Byzantine problem are presented. 1 What You Thought Could Never Happen In English, the phrase “one in a million” is popularly used to describe the highly improbable. The ratio itself is difficult to comprehend. The easiest way to give it reason is to equate it to real-world expectations. For example, the probability of winning the U.K. National Lottery is around one in fourteen million; the probability of getting struck by lightning in the U.S. is around one in six hundred thousand [1]. It is not safe to rely on intuition for reasoning about unfathomably small probabilities (for example, the 1-in-1,000,000,000 maximum failure probability for critical aerospace systems1). It is problematic in two ways: (1) real-world parallels are beyond typical human experience and comprehension; (2) faults that are not recognized, such as Byzantine faults, are incorrectly assumed to occur with zero or very low probability. The lack of recognition causes additional issues in that it allows the manifestation of such faults to pass unnoticed or be otherwise misclassified, reinforcing the misconception of low probability of occurrence. 1 Usually written as a failure rate of 10/hr 236 K. Driscoll et al. The lack of recognition leads to repeating the “Legionnaire’s Disease” phenomenon. After its “discovery” in 1976, a search of medical records found that the disease had seldom occurred for many decades. The lack of shared knowledge and the disease’s rarity made each occurrence appear to be unique. Only after 1976 was it realized that all these “unique” occurrences had a common cause. Similarly, an observation of a Byzantine failure will not be recognized as being an instance of a known class of failure by those who are not intimately familiar with Byzantine failures. The intent of this paper is to redress this situation. Drawing from the authors’ experiences with Byzantine failures in real-world systems, this paper shows that Byzantine problems are real, have nasty properties, and are likely to increase in frequency with emerging technology trends. Some of the myths with respect to the containment of Byzantine faults are dispelled and suitable mitigation strategies and architectures are discussed. 2 The Byzantine Army Is Growing The microprocessor revolution has seen electronic and software technologies proliferate into almost every domain of life, including an increasing responsibility in safety-critical domains. High assurance processes (e.g. DO-254 [2]) have been matured to manage the development of high integrity systems. The cost of using high assurance processes is relatively high in comparison to equivalent levels of functionality in commercial counterparts. In recent years, there has been a push to adopt commercial off-the-shelf (COTS) technology into high integrity systems. The timing of the COTS push is alarming, considering the decreasing reliability and dependability trends emerging within the COTS integrated circuit (IC) arena. With increasing clock frequencies, decreasing process geometries, and decreasing power supply voltages, studies [3] conclude that the dependability of modern ICs is decreasing. This development reverses the historical trend of increasing IC dependability. Bounding the failure behaviors of emerging COTS ICs will become increasingly more difficult. The expected lifetime of ICs is also decreasing, as modes of “silicon wear-out” (i.e. time-dependent dielectric breakdown, hot carrier aging, and electro-migration) become more significant. The anticipated working life of current ICs may be on the order of 5-10 years. Although viable in the commercial arena, this is a world away from the requirements for high integrity systems, which have traditionally had deployment lifetimes ranging over multiple decades. Strategies to mitigate the problems are in development [4]. It is safe to assume that the anticipated rate of IC failure will increase and the modes of failure will become ever more difficult to characterize. Another significant trend is the move towards more distributed, safety-critical processing system topologies. These distributed architectures appear to be favored for the emerging automotive “by-wire” control systems [5], where a new breed of safety critical communications protocols and associated COTS are being developed [6]. Such technologies, if developed in compliance with the traditional high assurance processes, show promise to answer the cost challenges of high integrity applications. However, it is imperative that such technologies are developed with full knowledge of all possible failure modes. Distributed control systems, by their very nature, require Byzantine Fault Tolerance, from Theory to Reality 237 consensus among their constituent elements. The required consensus might be lowlevel (e.g. in the form of mutual synchronization) or at a higher level (e.g. in the form of some coordinated system action such as controlling a braking force). Addressing Byzantine faults that can disrupt consensus is thus a crucial system requirement. We expect Byzantine faults to be of increasing concern given the two major trends described in this section: (1) Byzantine faults are more likely to occur due to trends in device physics; (2) systems are becoming more vulnerable due to increasing emphasis on safety-critical distributed topologies. It is therefore imperative that Byzantine faults and failure mechanisms become widely understood and that the design of safety-critical systems includes mitigation strategies. 3 Parables from the Classical Byzantine Age The initial definition of Byzantine failure was given in a landmark paper by Lamport et al [7]. They present the scenario of a group of Byzantine Generals whose divisions surround an enemy camp. After observing the enemy, the Generals must communicate amongst themselves and come to consensus on a plan of action— whether to attack or retreat. If they all attack, they win; if none attack, they live to fight another day. If only some of the generals attack, then the generals will die. The generals communicate via messages. The problem is that one or more of the generals may be traitors who send inconsistent messages to disrupt the loyal generals from reaching consensus. The original paper discusses the problem in the context of oral messages and written signed messages that are attributed different properties. Oral messages are characterized as follows: A1. Every message that is sent is delivered correctly (messages are not lost). A2. The receiver of a message knows who sent it. A3. The absence of a message can be detected. For messages with these properties, it has been proven that consensus cannot be achieved with three generals, if one of the generals is assumed to be a traitor. A solution is presented in which each of four general exchanges information with his peers and a majority vote makes selections over all of the data exchanged. This solution is generalized to accommodate multiple traitors, concluding: to tolerate m traitorous generals, requires 3 m + 1 generals utilizing m + 1 rounds of information exchange. Written, signed messages assume all of the properties (A1-A3) of the oral messages, and are further characterized by the properties below: A4. A loyal general’s signature cannot be forged. A5. Anyone can verify the authenticity of a signature. Assuming the signed message properties above, it is shown that consensus is possible with just three generals, using a simple majority voting function. The solution is further generalized to address multiple fault scenarios, concluding: to tolerate m traitorous generals requires 2m + 1 loyal generals and m + 1 rounds of information exchange. The initial proofs presented in the paper assume that all generals communicate directly with one another. The assumptions are later relaxed to address topologies of 238 K. Driscoll et al. less connectivity. It is proven that for oral messages, consensus is possible if the generals are connected in a p regular graph, where p > 3m 1. For signed (authenticated) messages, it is proven that consensus is possible if the loyal generals are connected to each other. However, this solution requires additional rounds of information exchange Since its initial presentation, nearly two decades ago, the Byzantine Generals problem has been the subject of intense academic study, leading to the development and formal validation of numerous Byzantine-tolerant algorithms and architectures. As stated previously, industry’s recognition and treatment of the problem has been far less formal and rigorous. A reason for this might be the anthropomorphic tone and presentation of the problem definition. Although the authors warned against adopting too literal an interpretation, much of the related literature that has followed the original text has reinforced the “traitorous” anthropomorphic failure model. Such treatment has resulted in the work being ignored by a large segment of the commu


Real-time underwater 3D scene reconstruction using commercial depth sensor

This paper presents preliminary work to utilize a commercial time of flight depth camera for real-time 3D scene reconstruction of underwater objects. Typical RGB stereo camera imaging for 3D capturing suffers from blur and haziness due to turbidity of water in addition to critical dependence on light, either from natural or artificial sources. We propose a method for repurposing the low-cost Microsoft Kinect™ Time of Flight camera for underwater environment enabling dense depth data acquisition that can be processed in real time. Our motivation is the ease of use and low cost of the device for high quality real-time scene reconstruction as compared to multi view stereo cameras, albeit at a smaller range. Preliminary results of depth data acquisition and surface reconstruction in underwater environment are also presented. The novelty of our work is the utilization of the Kinect depth camera for real-time 3D mesh reconstruction and our main objective is to develop an economical and compact solution for underwater 3D mapping.


1 Keyword extraction : a review of methods and approaches

Paper presents a survey of methods and approaches for keyword extraction task. In addition to the systematization of methods, the paper gathers a comprehensive review of existing research. Related work on keyword extraction is elaborated for supervised and unsupervised methods, with special emphasis on graphbased methods as well as Croatian keyword extraction. Selectivity-based keyword extraction method is proposed as a new unsupervised graph-based keyword extraction method which extracts nodes from a complex network as keyword candidates. The paper provides guidelines for future research and development of new graph-based approaches for keyword extraction.


Sentiment classification for Turkish Twitter feeds using LDA

Today, people are able to express their opinions and ideas on any subject using personal blogs or a variety of social media environments. Contents shared by users with each passing day increases the data size on the web but this situation is a complication for meaningful information extraction. Therefore, sentiment analysis studies aimed at automatically obtaining meaningful information from social media content showed a significant increase in recent years. In these studies, automatically analyzing the content of messages and identifying sentiment such as positive, neutral, negative etc. are intended. In this study, a system has been proposed for analyzing sentiment of Turkish Twitter feeds. In addition, sentiment classification which is a leading problem for sentiment analysis studies has been performed using topic modeling and the effect of modeling on results are examined. The topic model has been created using LDA (Latent Dirichlet Allocation) algorithm. Experimental results obtained with different feature extraction model and classifiers. The results show that topic modeling is more successful compared to the methods used in the previous studies. Also, sentiment classification based on topic model knowledge has been found to increase sentiment analysis success by %26 compared to our previous work.


A conceptual framework for serious games and its validation

FACULTY OF ENGINEERING, SCIENCES AND MATHEMATICS SCHOOL OF ELECTRONICS AND COMPUTER SCIENCE Doctor of Philosophy A CONCEPTUAL FRAMEWORK FOR SERIOUS GAMES AND ITS VALIDATION


Cytokine removal with high cut-off membrane: review of literature.

During the last decade, blood purification therapies have been proposed as an effective therapy to control the cytokines dysregulation in systemic inflammatory syndromes. Among them, the treatment with high cut-off membranes is characterized by larger pore size and more effective clearance for middle molecular weight molecules (cytokines). In this paper, we performed a thoughtful review of the literature on HCO being used for blood purification indications in all systemic inflammation syndromes. Clinical and experimental studies show that the use of high effluent flows in a pure diffusive treatment effectively removes serum cytokines with a safe profile in albumin clearance. In clinical studies, the removal of these inflammatory mediators is associated with a significant improvement in hemodynamic condition, oxygenation indices, and organ dysfunction.


Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation <ce:italic>in vitro</ce:italic>

BACKGROUND
The neocortex is the most common target of subdural electrotherapy and noninvasive brain stimulation modalities, including transcranial magnetic stimulation (TMS) and transcranial current simulation (TCS). Specific neuronal elements targeted by cortical stimulation are considered to underlie therapeutic effects, but the exact cell type(s) affected by these methods remains poorly understood.

OBJECTIVE
We determined whether neuronal morphology or cell type predicted responses to subthreshold and suprathreshold uniform electric fields.

METHODS
We characterized the effects of subthreshold and suprathreshold electrical stimulation on identified cortical neurons in vitro. Uniform electric fields were applied to rat motor cortex brain slices, while recording from interneurons and pyramidal cells across cortical layers, using a whole-cell patch clamp. Neuron morphology was reconstructed after intracellular dialysis of biocytin. Based solely on volume-weighted morphology, we developed a parsimonious model of neuronal soma polarization by subthreshold electric fields.

RESULTS
We found that neuronal morphology correlated with somatic subthreshold polarization. Based on neuronal morphology, we predict layer V pyramidal neuronal soma to be individually the most sensitive to polarization by optimally oriented subthreshold fields. Suprathreshold electric field action potential threshold was shown to reflect both direct cell polarization and synaptic (network) activation. Layer V/VI neuron absolute electric field action potential thresholds were lower than layer II/III pyramidal neurons and interneurons. Compared with somatic current injection, electric fields promoted burst firing and modulated action potential firing times.

CONCLUSIONS
We present experimental data indicating that cortical neuron morphology relative to electric fields and cortical cell type are factors in determining sensitivity to sub- and supra-threshold brain stimulation.


Predicting Price Changes in Ethereum

The market capitalization of publicly traded cryptocurrencies is currently above $230 billion (Bovaird, 2017). Bitcoin, the most valuable cryptocurrency, serves primarily as a digital store of value (Van Alstyne, 2014), and its price predictability has been well-studied (Hegazy and Mumford, 2016). However, Ethereum has the second-highest market capitalization and supports much more functionality than Bitcoin. While its price predictability is sparsely covered in published literature, the technology’s additional functionality may cause Ether’s price predictability to differ significantly from that of Bitcoin. These characteristics are outlined in the following subsection; the underlying details of Bitcoin (Nakamoto, 2008) and Ethereum (Buterin, 2013) are elided, as they are described in depth in the cited papers.


Binary malware image classification using machine learning with local binary pattern

Malware classification is a critical part in the cyber-security. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malware classification methodology based on its binary image and extracting local binary pattern (LBP) features is proposed. First, malware images are reorganized into 3 by 3 grids which is mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature. Performance comparison results among different classifiers with different image descriptors such as GIST, a spatial envelop, and the LBP demonstrate that our proposed approach outperforms others.


The return of AdaBoost.MH: multi-class Hamming trees

Within the framework of ADABOOST.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem toK binary one-againstall classifications. The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length K and label-independent scalar classifier. At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions. The algorithm retains the conceptual elegance, power, and computational efficiency of binary ADABOOST. In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLOGITBOOST, and it is significantly better than other known implementations of ADABOOST.MH.


Formalizing agility, part 2: how an agile organization embraced the CMMI

Most large IT organizations need the best of both worlds - working software that supports ever-changing business needs, and a process for delivering software that is predictable, trainable, and auditable. Organizations with agile software teams often receive timely, cost-effective solutions of sufficient quality. Organizations with formal processes often benefit from industry-recognized certifications and robust process improvement mechanisms. Rarely does a single, large IT organization achieve both. DTE Energy has such a combination: its lightweight yet CMMI-compatible methodology is now used within its 600+ person IT organization to deliver and support working software. Its small teams embrace core agile principles as they provide "just enough" solutions that satisfy maturing business needs. Yet they passed two SCAMPI appraisals toward formal CMMI Level III accreditation, scheduled for mid-2006. This report extends the Agile 2005 experience report on DTE Energy's agile IT organization's journey toward CMMI staged maturity accreditation. This report briefly recaps their seven years of agile experience, presents governance mechanisms and change management techniques, and highlights their four-release, one-year plan for CMMI Level III accreditation. Finally, this report offers suggestions on embracing a formal process framework that are applicable to any organization


Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.


Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model

The ABSTRACT is to be in fully-justified italicized text, at the top of the left-hand column, below the author and affiliation information. Use the word “Abstract” as the title, in 12-point Times, boldface type, centered relative to the column, initially capitalized. The abstract is to be in 10point, single-spaced type. Leave two blank lines after the Abstract, then begin the main text. Look at previous CVPR abstracts to get a feel for style and length.


Signal Characteristics on Sensor Data Compression in IoT -An Investigation

In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.


Resolving constraint conflicts

In this paper, we define constraint conflicts and examine properties that may aid in guiding their resolution. A constraint conflict is an inconsistency between the access control policy and the constraints specified to limit that policy. For example, a policy that permits a high integrity subject to access low integrity data is in conflict with a Biba integrity constraint. Constraint conflicts differ from typical policy conflicts in that constraints are never supposed to be violated. That is, a conflict with a constraint results in a policy compilation error, whereas policy conflicts are resolved at runtime. As we have found in the past, when constraint conflicts occur in a specification a variety of resolutions are both possible and practical. In this paper, we detail some key formal properties of constraint conflicts and show how these are useful in guiding conflict resolution. We use the SELinux example policy for Linux 2.4.19 as the source of our constraint conflicts and resolution examples. The formal properties are used to guide the selection of resolutions and provide a basis for a resolution language that we apply to resolve conflicts in the SELinux example policy.


Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning

Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter optimization method that can find optimal hyperparameters for a given sequence using an action-prediction network leveraged on Continuous Deep Q-Learning. Since the common state-spaces for object tracking tasks are significantly more complex than the ones in traditional control problems, existing Continuous Deep Q-Learning algorithms cannot be directly applied. To overcome this challenge, we introduce an efficient heuristic to accelerate the convergence behavior. We evaluate our method on several tracking benchmarks and demonstrate its superior performance1.


A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.


Co-operative downloading in vehicular ad-hoc wireless networks

Increasing need for people to be "connected"; while at the same time remain as mobile as ever poses several interesting issues in wireless networks. It is conceivable in the near-future that wireless "hotspots" experience flash crowds-like traffic arrival pattern. A common phenomena in the Internet today characterized by sudden and unpredicted increase in popularity of on-line content. In this paper, we propose SPAWN, a cooperative strategy for content delivery and sharing in future vehicular networks. We study the issues involved in using such a strategy from the standpoint of vehicular ad-hoc networks. In particular, we show that not only content server but also wireless access network load reduction is critical. We propose a "communication efficient" swarming protocol which uses a gossip mechanism that leverages the inherent broadcast nature of the wireless medium, and a piece-selection strategy that takes proximity into account in decisions to exchange pieces. We show through simulation that gossip incorporates location-awareness into peer selection, while incurring low messaging overhead, and consequently enhancing the swarming protocol performance. We develop an analytical model to characterize the performance of SPAWN.


Internet Traffic Measurement

This tutorial article discusses the role of network traffic measurement in the design, testing, and evaluation of Internet protocols and applications. The article begins with some background information on Internet traffic measurement, and then proceeds to discuss the “tools of the trade”, including examples of both hardwarebased and software-based approaches to network traffic measurement. The article concludes with a summary of the main observations from the past fifteen years of network measurement research, along with pointers to the relevant literature for more information.


Runtime Verification of Ethereum Smart Contracts

The notion of smart contracts in distributed ledger systems have been hailed as a safe way of enforcing contracts between participating parties. However, unlike legal contracts, which talk about ideal behaviour and consequences of not adhering to such behaviour, smart contracts are by their very nature executable code, giving explicit instructions on how to achieve compliance. Executable specification languages, particularly Turing complete ones, are notoriously known for the difficulty of ensuring correctness, and recent incidents which led to huge financial losses due to bugs in smart contracts, have highlighted this issue. In this paper we show how standard techniques from runtime verification can be used in the domain of smart contracts, including a novel stake-based instrumentation technique which ensures that the violating party provides insurance for correct behaviour. The techniques we describe have been partially implemented in a proof-of-concept tool ContractLarva, which we discuss in this paper.


DBpedia - A crystallization point for the Web of Data

The DBpedia project is a community effort to extract structured information from Wikipedia and to make this information accessible on the Web. The resulting DBpedia knowledge base currently describes over 2.6 million entities. For each of these entities, DBpedia defines a globally unique identifier that can be dereferenced over the Web into a rich RDF description of the entity, including human-readable definitions in 30 languages, relationships to other resources, classifications in four concept hierarchies, various facts as well as data-level links to other Web data sources describing the entity. Over the last year, an increasing number of data publishers have begun to set data-level links to DBpedia resources, making DBpedia a central interlinking hub for the emerging Web of data. Currently, the Web of interlinked data sources around DBpedia provides approximately 4.7 billion pieces of information and covers domains such as geographic information, people, companies, films, music, genes, drugs, books, and scientific publications. This article describes the extraction of the DBpedia knowledge base, the current status of interlinking DBpedia with other data sources on the Web, and gives an overview of applications that facilitate the Web of Data around DBpedia.


LUBM: A benchmark for OWL knowledge base systems

We describe our method for benchmarking Semantic Web knowledge base systems with respect to use in large OWL applications. We present the Lehigh University Benchmark (LUBM) as an example of how to design such benchmarks. The LUBM features an on-tology for the university domain, synthetic OWL data scalable to an arbitrary size, fourteen extensional queries representing a variety of properties, and several performance metrics. The LUBM can be used to evaluate systems with different reasoning capabilities and storage mechanisms. We demonstrate this with an evaluation of two memory-based systems and two systems with persistent storage.


Automatically refining the wikipedia infobox ontology

The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia's infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia's infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.


Exploiting Wikipedia as External Knowledge for Named Entity Recognition

We explore the use of Wikipedia as external knowledge to improve named entity recognition (NER). Our method retrieves the corresponding Wikipedia entry for each candidate word sequence and extracts a category label from the first sentence of the entry, which can be thought of as a definition part. These category labels are used as features in a CRF-based NE tagger. We demonstrate using the CoNLL 2003 dataset that the Wikipedia category labels extracted by such a simple method actually improve the accuracy of NER.


Field-Robot-Based Agriculture : “ RemoteFarming . 1 ” and “ BoniRob-Apps

Based on the autonomous field robot BoniRob, which was developed for plant phenotyping, the authors have created an enhanced platform BoniRob (V2). The design of BoniRob (V2) considers two main improvements: At first the robustness for continuous outdoor use is increased and secondly the robot now makes available a reusable platform that can serve for multiple agricultural purposes. The combination of the platform BoniRob (V2) and application modules (“BoniRob-Apps”) is comparable to the classical combination of a tractor and an implement. In order to demonstrate this new concept, the authors have implemented three BoniRob-Apps, which can be integrated in BoniRob (V2) via defined mechanical, electrical and logical interfaces. The research project RemoteFarming.1 goes even beyond the accomplishment of a specific task by a robot and demonstrates the integration of the BoniRob (V2) platform into agricultural processes. For that purpose innovative agricultural engineering is combined with webbased communication technologies. The target is to develop a robotic mechanical weed control system which integrates a human user as remote worker in the process. The robotic weed control system is used for intra row weed treatment in carrots at BBCH-scales 10 to 20 in organic farming. This task is today conducted by hand. Introduction In recent years a variety of field robot prototypes have been proposed. They cover several applications, such as scouting and sampling, precise variable rate application of pesticides and fertilizers as well as autonomous weeding [1][2]. However, until now there is no commercial application of autonomous field operation in agriculture available. This is partly due to open issues regarding safety and legal requirements of autonomous operations [3].Another problem of the presented prototypes for autonomous field robots is their conception as single-purpose robots, despite there being several applications with market potential [6]. As the single-purpose concept leads typically would lead high costs due to a low workload over the year (specific tasks are required over only a short period of time), the systems are not economically feasible yet. To overcome this barrier a new concept for a multipurpose field robot is shown here. Multipurpose field robot platform BoniRob (V2) The BoniRob (V2) platform has been designed as an improved version of the BoniRob crop scout, which has been announced by the authors [5]. The BoniRob (V2) is shown in Fig 1. Fig.1: BoniRob (V2) during a RemoteFarming.1 field trial It was reengineered with focus on robustness and reusability. This manifests in increases of power supply by the electrical generator, continuous and peak torque at the wheel drives as well as chassis clearance (Table 1). Table 1: Technical figures of BoniRob (V2) BoniRob crop scout BoniRob (V2) platform Power of electric generator 2.0 kW 2.6 kW Battery’s capacity 110 Ah 170 Ah Continuous drive torque (per wheel) 55 Nm 70 Nm Peak drive torque (per wheel) 110 Nm 240 Nm As it can be seen in Fig.1, the height adjustment of the entire robot body was replaced by vertically fixed arms for each wheel, which can be rotated in the horizontal plane for adjustment of track width and centration of the robot body over the row to be treated. The hydraulic height adjustment was replaced by a manual, mechanical height-adjustment module frame. Furthermore, all other hydraulic drives were replaced by electrical systems. The BoniRob-App concept Using the multipurpose field robot platform BoniRob (V2) as a carrier, supplier and base for multiple BoniRob-Apps can be compared to the traditional combination of a single tractor with multiple implements. BoniRob-Apps can be integrated into the platform using defined mechanical, electrical and logical interfaces. The module frame of 0.77 m x 0.75 m in the centre of the robot allows mechanically attaching the BoniRob-App (Fig. 2). The mounting height can be adjusted in a range of 0.4 m. The electrical connection is set up using a custom plug. This provides energy supply to the BoniRob-App and different voltage levels (230V AC, 24 V DC, 12 V DC, 5 V DC). Moreover, it allows linking the emergency stop infrastructure of platform and App, such that even the App can stop the entire robot in case of emergency. Also, the custom plug sets up a Gigabit-Ethernet connection to the host-computer on BoniRob (V2). The entire communication infrastructure on BoniRob (V2) and between platform and the Apps is designed using Ethernet and ROS [4]. A sequence controller with 2 levels opens the way for distributed drive control of the robot. In the row the App (level 2 sequence controller) can access the drive control. On the headland the platform (level 1 sequence controller) holds the access to the drive control and runs the turn procedure of the robot. This Fig. 2: Multiple BoniRob-Apps can be used in the same platform allows switching the access to the drive depending on the state including letting the App control the drive train of the robot in properly defined situations. Again, this can be compared to a tractor-implement combination. In recent years in the field of Tractor Implement Management advances have been made towards control of the tractor by the implement in specific situations, because the implement and its sensors are closer to the process and more specifically adapted to it. This also shows that BoniRob-Apps are more than just tools to be used with the BoniRob (V2) platform but smart combinations of mechanical and electrical systems together with processand application-specific intelligence. BoniRob-Apps: Demonstrating examples In order to demonstrate the new concept, the authors implemented three BoniRob-Apps:


Energy Minimization Using Multiple Supply Voltages

We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both non-pipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level to each operation in a data ow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using four supply voltage levels on a number of standard benchmarks, an average energy saving of 53% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using one fixed supply voltage level.


Modeling Commonsense Reasoning via Analogical Chaining: A Preliminary Report

Understanding the nature of commonsense reasoning is one of the deepest questions of cognitive science. Prior work has proposed analogy as a mechanism for commonsense reasoning, with prior simulations focusing on reasoning about continuous behavior of physical systems. This paper examines how analogy might be used in commonsense more broadly. The two contributions are (1) the idea of common sense units, intermediate-sized collections of facts extracted from experience (including cultural experience) which improves analogical retrieval and simplifies inferencing, and (2) analogical chaining, where multiple rounds of analogical retrieval and mapping are used to rapidly construct explanations and predictions. We illustrate these ideas via an implemented computational model, tested on examples from an independently-developed test of commonsense reasoning.


A key-management scheme for distributed sensor networks

Distributed Sensor Networks (DSNs) are ad-hoc mobile networks that include sensor nodes with limited computation and communication capabilities. DSNs are dynamic in the sense that they allow addition and deletion of sensor nodes after deployment to grow the network or replace failing and unreliable nodes. DSNs may be deployed in hostile areas where communication is monitored and nodes are subject to capture and surreptitious use by an adversary. Hence DSNs require cryptographic protection of communications, sensor-capture detection, key revocation and sensor disabling. In this paper, we present a key-management scheme designed to satisfy both operational and security requirements of DSNs. The scheme includes selective distribution and revocation of keys to sensor nodes as well as node re-keying without substantial computation and communication capabilities. It relies on probabilistic key sharing among the nodes of a random graph and uses simple protocols for shared-key discovery and path-key establishment, and for key revocation, re-keying, and incremental addition of nodes. The security and network connectivity characteristics supported by the key-management scheme are discussed and simulation experiments presented.


A New Approach to Track Multiple Vehicles With the Combination of Robust Detection and Two Classifiers

It plays an important role to accurately track multiple vehicles in intelligent transportation, especially in intelligent vehicles. Due to complicated traffic environments it is difficult to track multiple vehicles accurately and robustly, especially when there are occlusions among vehicles. To alleviate these problems, a new approach is proposed to track multiple vehicles with the combination of robust detection and two classifiers. An improved ViBe algorithm is proposed for robust and accurate detection of multiple vehicles. It uses the gray-scale spatial information to build dictionary of pixel life length to make ghost shadows and object’s residual shadows quickly blended into the samples of the background. The improved algorithm takes good post-processing method to restrain dynamic noise. In this paper, we also design a method using two classifiers to further attack the problem of failure to track vehicles with occlusions and interference. It classifies tracking rectangles with confidence values between two thresholds through combining local binary pattern with support vector machine (SVM) classifier and then using a convolutional neural network (CNN) classifier for the second time to remove the interference areas between vehicles and other moving objects. The two classifiers method has both time efficiency advantage of SVM and high accuracy advantage of CNN. Comparing with several existing methods, the qualitative and quantitative analysis of our experiment results showed that the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.


Digital Watermarking and Steganography

Sharing, disseminating, and presenting data in digital format is not just a fad, but it is becoming part of our life. Without careful planning, digitized resources could easily be misused, especially those that are shared across the Internet. Examples of such misuse include use without the owner’s permission, and modification of a digitized resource to fake ownership. One way to prevent such behaviors is to employ some form of copyright protection technique, such as digital watermarks. Digital watermarks refer to the data embedded into a digital source (e.g., images, text, audio, or video recording). They are similar to watermarks in printed materials as a message inserted into the host media typically becomes an integral part of the media. Apart from traditional watermarks in printed forms, digital watermarks may also be invisible, may be in the forms other than graphics, and may be digitally removed.


An evaluation of bipartitioning techniques

Logic partitioning is an important issue in VLSI CAD, and has been an area of active research for at least the last 25 years. Numerous approaches have been developed and many different techniques have been combined for a wide range of applications. In this paper, we examine many of the existing techniques for logic bipartitioning and present a methodology for determining the best mix of approaches. The result is a novel bipartitioning algorithm that includes both new and pre-existing techniques. Our algorithm produces results that are at least 16% better than the state-of-the-art while also being efficient in run-time.