title: The integration of business intelligence and knowledge management

abstract: Enterprise executives understand that timely, accurate knowledge can mean improved business performance. Two technologies have been central in improving the quantitative and qualitative value of the knowledge available to decision makers: business intelligence and knowledge management. Business intelligence has applied the functionality, scalability, and reliability of modern database management systems to build ever-larger data warehouses, and to utilize data mining techniques to extract business advantage from the vast amount of available enterprise data. Knowledge management technologies, while less mature than business intelligence technologies, are now capable of combining today's content management systems and the Web with vastly improved searching and text mining capabilities to derive more value from the explosion of textual information. We believe that these systems will blend over time, borrowing techniques from each other and inspiring new approaches that can analyze data and text together, seamlessly. We call this blended technology BIKM. In this paper, we describe some of the current business problems that require analysis of both text and data, and some of the technical challenges posed by these problems. We describe a particular approach based on an OLAP (on-line analytical processing) model enhanced with text analysis, and describe two tools that we have developed to explore this approach--eClassifier performs text analysis, and Sapient integrates data and text through an OLAP-style interaction model. Finally, we discuss some new research that we are pursuing to enhance this approach.


A Genetic Solution for the Cutting Stock Problem


title: Setting an attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels

abstract: Convolutional neural networks have emerged as the leading method for the classification and segmentation of images. In some cases, it is desirable to focus the attention of the net on a specific region in the image; one such case is the recognition of the contents of transparent vessels, where the vessel region in the image is already known. This work presents a valve filter approach for focusing the attention of the net on a region of interest (ROI). In this approach, the ROI is inserted into the net as a binary map. The net uses a different set of convolution filters for the ROI and background image regions, resulting in a different set of features being extracted from each region. More accurately, for each filter used on the image, a corresponding valve filter exists that acts on the ROI map and determines the regions in which the corresponding image filter will be used. This valve filter effectively acts as a valve that inhibits specific features in different image regions according to the ROI map. In addition, a new data set for images of materials in glassware vessels in a chemistry laboratory setting is presented. This data set contains a thousand images with pixel-wise annotation according to categories ranging from filled and empty to the exact phase of the material inside the vessel. The results of the valve filter approach and fully convolutional neural nets (FCN) with no ROI input are compared based on this data set.


SVM Classifiers – Concepts and Applications to Character Recognition


title: Multi-Agent Cooperation and the Emergence of (Natural) Language

abstract: The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are in- terested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communi- cation. We study this learning in the context of referential games. In these games, a sender and a receiver see a pair of images. The sender is told one of them is the target and is allowed to send a message to the receiver, while the receiver must rely on it to identify the target. Thus, the agents develop their own language interactively out of the need to communicate. We show that two networks with simple configurations are able to learn to coordinate in the referential game. We further explore whether the “word meanings” induced in the game reflect intuitive semantic properties of the objects depicted in the image, and we present a simple strategy for grounding the agents’ code into natural language, a necessary step in developing machines that should eventually be able to communicate with humans.


title: The Seven Sins of Consumer Psychology

abstract: Consumer psychology faces serious issues of internal and external relevance. Most of these issues originate in seven fundamental problems with the way consumer psychologists plan and conduct their research—problems that could be called “the seven sins of consumer psychology.” These seven “sins” are (1) a narrow conception of the scope of consumer behavior research; (2) adoption of a narrow set of theoretical lenses; (3) adherence to a narrow epistemology of consumer research; (4) an almost exclusive emphasis on psychological processes as opposed to psychological content; (5) a strong tendency to overgeneralize from finite empirical results, both as authors and as reviewers; (6) a predisposition to design studies based on methodological convenience rather than on substantive considerations; and (7) a pervasive confusion between “theories of studies” and studies of theories. Addressing these problems (“atoning for these sins”) would greatly enhance the relevance of the field. However, this may require a substantial rebalancing of the field's incentives to reward actual research impact rather than sheer number of publications in major journals.


title: Probing the surface-to-bulk transition: A closed-form constant-scaling algorithm for computing subsurface Green functions

abstract: A closed-form algorithm for computing subsurface Green functions—the blocks of a material’s Green function between the surface and the bulk—is presented, where we assume the system satisfies a common principal-layer approximation. By exploiting the block tridiagonal and nearly block Toeplitz structure of the Hamiltonian and overlap matrices, this method scales independently of the system size (constant scaling), allowing studies of large systems. As a proof-of-concept example, we investigate the decay of surface effects in an armchair graphene nanoribbon, demonstrating the persistence of surface effects hundreds of atomic layers (∼0.5 μm) away from a surface. We finally compare the surface-to-bulk transitions of finite and semi-infinite systems, finding that finite systems exhibit amplified surface effects.


title: Mining Version Control System for Automatically Generating Commit Comment

abstract: Commit comments increasingly receive attention as an important complementary component in code change comprehension. To address the comment scarcity issue, a variety of automatic approaches for commit comment generation have been intensively proposed. However, most of these approaches mechanically outline a superficial level summary of the changed software entities, the change intent behind the code changes is lost (e.g., the existing approaches cannot generate such comment: "fixing null pointer exception"). Considering the comments written by developers often describe the intent behind the code change, we propose a method to automatically generate commit comment by reusing the existing comments in version control system. Specifically, for an input commit, we apply syntax, semantic, pre-syntax, and pre-semantic similarities to discover the similar commits from half a million commits, and recommend the reusable comments to the input commit from the ones of the similar commits. We evaluate our approach on 7 projects. The results show that 9.1% of the generated comments are good, 27.7% of the generated comments need minor fix, and 63.2% are bad, and we also analyze the reasons that make a comment available or unavailable.


title: Proactive Coping and its Relation to the Five-Factor Model of Personality

abstract: Research on personality's role in coping is inconclusive. Proactive coping ability is one's tendency to expect and prepare for life's challenges (Schwarzer & Taubert, 2002). This type of coping provides a refreshing conceptualization of coping that allows an examination of personality's role in coping that transcends the current situational versus dispositional coping conundrum. Participants (N = 49) took the Proactive Coping Inventory (Greenglass, Schwarzer, & Taubert, 1999) and their results were correlated with all domains and facets of the Five-Factor Model (FFM; Costa & McCrae, 1995). Results showed strong correlations between a total score (which encompassed 6 proactive coping scales), and Extraversion, Agreeableness, Conscientiousness, and Neuroticism, as well as between several underlying domain facets. Results also showed strong correlations between specific proactive coping subscales and several domains and facets of the FFM. Implications for the influence of innate personality factors in one's ability to cope are discussed. An individual's methods of coping with adversity are important aspects of their overall adaptation. Although characteristic ways of coping likely reflect learned experiences and situational factors to some degree, it is also likely that innate dispositions contribute to specific coping styles and overall ability to cope. Thus, there may be systematic relationships between enduring personality traits and coping ability. To show the theoretical importance of such a relationship, an account of empirical data that highlights the fundamental role of personality will develop a rationale for the hypothesized influence of personality on overall adaptation, and reasons why personality is likely to affect coping ability. Personality


title: Integration of Spectral Histogram and Level Set for Coastline Detection in SAR Images

abstract: This study proposed a novel algorithm for coastline detection in single-polarization synthetic aperture radar (SAR) images based on the local spectral histogram (LSH) and the level set method (LSM). The proposed algorithm includes two main steps. In the processing step, a rough land/sea segmentation is done by utilizing a texture-based segmentation using LSH. In the postprocessing step, the region-based LSM is used to refine the previous segmentation and extract the coastline accurately. The level set (LS) function is initialized using the results of LSH segmentation. This prevents the development of spurious contours, becoming trapped in the local minimum, eliminates the manual support, and also speeds up the LS evolution. A hierarchical LS regularization is proposed using two Gaussian kernels, which is compatible with noisy images and able to detect very narrow regions. The proposed algorithm is able to detect a coastline at the full resolution of the input SAR image and is also robust to noise. A criterion to quantify the accuracy of the results was also proposed. The experimental results for a number of real high-resolution single-polarization SAR images demonstrate that the proposed method is robust to noise and efficient for the coastline detection in different coastal and sea environments.


title: A 110–250V 2MHz isolated DC-DC converter with integrated high-speed synchronous three-level gate drive

abstract: This paper presents techniques for high-voltage isolated converters to achieve high power efficiency at high switching frequencies. To minimize the converter switching loss, an isolated quasi-square-wave zero-voltage switching three-level half-bridge architecture is adopted. An integrated synchronous three-level gate driver is proposed to ensure reliability of all eGaN power FETs and provide fast propagation delays for high-frequency converter operation. Thanks to the auto-reconfigurability of the proposed V SW -based dead-time controller in the gate driver, ZVS of all power FETs with minimal body diode conduction loss can be achieved in a wide input range (110V–250V) to guarantee the reliability of the proposed converter. Implemented in a 0.7μm 700V CMOS-LDMOS process, the proposed gate driver achieves ≤ 18ns propagation delays and enables a 250V 45W isolated three-level converter to achieve the peak power efficiencies of 94.2% and 89.1% at 1MHz and 2MHz, respectively.


title: Optic Nerve Hypoplasia Syndrome: A Review of the Epidemiology and Clinical Associations

abstract: Background: Optic nerve hypoplasia (ONH) has developed into a leading cause of congenital blindness. The frequently associated features of hypopituitarism and absent septum pellucidum were felt to have embryonic linkage as “septo-optic dysplasia” or “de Morsier’s syndrome.” More recent studies have suggested these associations are independent of one another. This review provides an assessment of the historical and recent evidence linking neuroradiologic, endocrinologic and developmental morbidity in patients with ONH. The prenatal risk factors, heritability, and genetic mutations associated with ONH are described. Results: Recognition of the critical association of ONH with hypopituitarism should be attributed to William Hoyt, not Georges de Morsier. De Morsier never described a case of ONH or recognized its association with hypopituitarism or missing septum pellucidum. Hypopituitarism is caused by hypothalamic dysfunction. This, and other more recently identified associations with ONH, such as developmental delay and autism, are independent of septum pellucidum development. Other common neuroradiographic associations such as corpus callosum hypoplasia, gyrus dysplasia, and cortical heterotopia may have prognostic significance. The predominant prenatal risk factors for ONH are primiparity and young maternal age. Presumed risk factors such as prenatal exposure to drugs and alcohol are not supported by scrutiny of the literature. Heritability and identified gene mutations in cases of ONH are rare. Conclusion: Children with ONH require monitoring for many systemic, developmental, and even life-threatening problems independent of the severity of ONH and presence of brain malformations including abnormalities of the septum pellucidum. “Septo-optic dysplasia” and “de Morsier’s syndrome” are historically inaccurate and clinically misleading terms.


title: DevOps culture and its impact on cloud delivery and software development

abstract: DevOps presents a significant broader perspective in delivering software solutions. The perception of DevOps being a mere tool has been formulated into a change in culture, paradigms in automation, clarity in measuring risks and facilitating ease of sharing. DevOps has been responsible for companies to move from a monolithic pace of software releases to an agile mindset. This paper discovers the significance of DevOps and tries to explore the process of transformation from a legacy method and onto the impact of the change on overall architecture and software scale out. The transformation from a legacy mindset to agile mindset requires a formidable change to the organizational setup. The journey of adoption of such a culture requires various degrees of effort and techniques. In this paper, we cover the concepts, adoption strategies and the impact of such adoption strategies.


Cyber-Security , a new challenge for the aviation and automotive industries


title: Cusps Enable Line Attractors for Neural Computation

abstract: Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse-gating in feedforward neuronal networks. In this context, because of their neutral stability along a linear manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, non-linear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and thus, dynamically control the processing of graded information.


title: Mirror or Megaphone?: How relationships between narcissism and social networking site use differ on Facebook and Twitter

abstract: As research on the connection between narcissism and social networking site (SNS) use grows, definitions of SNS and measurements of their use continue to vary, leading to conflicting results. To improve understanding of the relationship between narcissism and SNS use, as well as the implications of differences in definition and measurement, we examine two ways of measuring Facebook and Twitter use by testing the hypothesis that SNS use is positively associated with narcissism. We also explore the relation between these types of SNS use and different components of narcissism within college students and general adult samples. Our findings suggest that for college students, posting on Twitter is associated with the Superiority component of narcissistic personality while Facebook posting is associated with the Exhibitionism component. Conversely, adults high in Superiority post on Facebook more rather than Twitter. For adults, Facebook and Twitter are both used more by those focused on their own appearances but not as a means of showing off, as is the case with college students. Given these differences, it is essential for future studies of SNS use and personality traits to distinguish between different types of SNS, different populations, and different types of use.


title: Causal independence for probability assessment and inference using Bayesian networks

abstract: A Bayesian network is a probabilistic representation for uncertain relationships, which has proven to be useful for modeling real-world problems. When there are many potential causes of a given effect, however, both probability assessment and inference using a Bayesian network can be difficult. In this paper, we describe causal independence, a collection of conditional independence assertions and functional relationships that are often appropriate to apply to the representation of the uncertain interactions between causes and effect. We show how the use of causal independence in a Bayesian network can greatly simplify probability assessment as well as probabilistic inference.


title: Effectiveness of Chinese massage therapy (Tui Na) for chronic low back pain: study protocol for a randomized controlled trial

abstract: Low back pain is a common, disabling musculoskeletal disorder in both developing and developed countries. Although often recommended, the potential efficacy of massage therapy in general, and Chinese massage (tuina) in particular, for relief of chronic low back pain (CLBP) has not been fully established due to inadequate sample sizes, low methodological quality, and subclinical dosing regimens of trials to date. Thus, the purpose of this randomized controlled trial (RCT) is to evaluate the comparative effectiveness of tuina massage therapy versus conventional analgesics for CLBP. The present study is a single center, two-arm, open-label RCT. A total of 150 eligible CLBP patients will be randomly assigned to either a tuina treatment group or a conventional drug control group in a 1:1 ratio. Patients in the tuina group receive a 20 minutes, 4-step treatment protocol which includes both structural and relaxation massage, administered in 20 sessions over a period of 4 weeks. Patients in the conventional drug control group are instructed to take a specific daily dose of ibuprofen. The primary outcome measure is the change from baseline back pain and function, measured by Roland-Morris Disability Questionnaire, at two months. Secondary outcome measures include the visual analogue scale, Japanese orthopedic association score (JOAS), and McGill pain questionnaire. The design and methodological rigor of this trial will allow for collection of valuable data to evaluate the efficacy of a specific tuina protocol for treating CLBP. This trial will therefore contribute to providing a solid foundation for clinical treatment of CLBP, as well as future research in massage therapy. This trial was registered with ClinicalTrials.gov of the National Institute of Health on 22 October 2013 (http://NCT01973010).


title: Numerical Study on Identification of Time Varying Parameters of Vibration Systems

abstract: An on-line least squares algorithm has previously been successfully applied to linear vibration systems in order to identify time varying parameters. In this article the limitations of the approach and the factors affecting the identification are further examined. The existence of the nonlinear term is determined by means of the time varying characteristics of the estimated linear parameters using the linear model and the data from a time invariant nonlinear system. The identification of the time varying linear parameters is also examined in accordance with the linear model by using the data with nonlinear elements.


title: Party Identity in Political Cognition

abstract: Despite the long-standing and prominent place of partisan identification (PID) in many aggregate- and individual-level models of voting behavior and political cognition in the United States, several basic features of this attachment remain poorly understood and significant controversies persist. The research presented here seeks to build upon the recent conceptualization of PID as a social identity (Green, Palmquist & Schickler, 2002; Greene, 1999, 2000, 2004; Huddy, Mason & Aaroe, 2010) in order to increase our knowledge of the ways in which it may function as such and to expand our understanding of partisan intensity and PID's biasing effects. This conceptualization is one that has been put to surprisingly limited use in political science. This work draws upon new data I have generated during my time as a doctoral student, making use of survey experimental paradigms and a new implicit measure. I call upon foundational and cutting-edge concepts and methods from social psychology in addressing several active research programs in political behavior. The first essay presents the most direct evidence to date regarding the presence of an affective identity component of PID (the way in which Campbell, Converse, Miller & Stokes (1960) conceived of the attachment), which sheds light on partisan intensity and measurement of it. Using data from a survey fielded among subjects in the Project Implicit research pool, it introduces a novel measure of implicit PID that directly measures the identity component as it is defined in balanced identity theory (Greenwald, Banaji, Rudman, Farnham, Nosek & Mellott, 2002), and compares it to standard PID measures. Among other things, the findings offer some confirmation that the traditional two-item, seven-point PID measure largely captures respondent identity levels. This is arguably the strongest evidence to date that the measure does, for the most part, what it was designed to do. I also find that Republican partisans, in the current political environment, are significantly stronger partisan identifiers than their Democratic counterparts. The second essay brings new data from embedded survey experiments to bear, assessing, in the case of political party, the presence of the kind of group-based bias often associated with social identities. The manipulation and measure are designed to avoid the confounders present in prior studies that have allowed some to question the biasing effect of PID. Consistent evidence suggestive of group-based bias emerges. These findings establish a new benchmark in this research program by demonstrating, at a micro level, the extent to which partisans are susceptible to a set of standard mechanisms for rationalization, information dismissal and motivated processing. Beyond adding evidence to the debate regarding perceptual bias, though, this paradigm allows for more nuanced analysis of the nature of that bias and heterogeneity in its expression. The final essay uses the notion of rooting interest to link this perceptual bias with a social identity model of PID. A manipulation was used to vary the relative salience of an individual's personal and collective self-concepts (Ambady, Paik, Steele, Owen-Smith & Mitchell, 2004), the interplay between which is at the heart of psychological conceptualizations of identity. The effects of this manipulation on the level of bias observed suggest that the strength of rooting interest may vary somewhat, but that the nature of the variation depends upon the political saturation of the context and differs between Republicans and Democrats in the current political environment. The results 1) demonstrate that manipulation of self-concept salience and variations in background politicization can alter the magnitude of bias; 2) provide evidence that this bias is pronounced even in less politicized contexts and when the personal self-concept is made more salient; and 3) suggest that bias is asymmetric across the two parties, with Republicans showing a higher baseline level, but some propensity to have their bias level manipulated downward, and Democrats starting at a lower point, but with the potential to be manipulated upward. Taken together, these new data (from both the experimental and measurement work) demonstrate two important points that were not as apparent in prior studies using other methods. To begin with, it appears that partisans of various intensities (strong Democrat versus strong Republican, for instance) should not be thought of or analyzed as mirror images of each other. Furthermore, it appears that a meaningful Identity Gap may exist between Republicans and Democrats in the current political moment. These emergent findings suggest future areas of inquiry, ways in which we might reexamine prior findings, and new potential research programs.


title: Bioacoustics-Based Human-Body-Mediated Communication

abstract: An acoustics-based method can utilize the human body as a communication channel to propagate information across different devices. The proposed system can propagate acoustic signals under 20 kHz within or between human bodies and even between the human body and the environment. The web extra at https://youtu.be/6Vo3gm5oJnM illustrates human-body-mediated communication concepts discussed in the article.


title: On the Origin of Reverse Transcriptase-Using CRISPR-Cas Systems and Their Hyperdiverse, Enigmatic Spacer Repertoires

abstract: Cas1 integrase is the key enzyme of the clustered regularly interspaced short palindromic repeat (CRISPR)-Cas adaptation module that mediates acquisition of spacers derived from foreign DNA by CRISPR arrays. In diverse bacteria, the cas1 gene is fused (or adjacent) to a gene encoding a reverse transcriptase (RT) related to group II intron RTs. An RT-Cas1 fusion protein has been recently shown to enable acquisition of CRISPR spacers from RNA. Phylogenetic analysis of the CRISPR-associated RTs demonstrates monophyly of the RT-Cas1 fusion, and coevolution of the RT and Cas1 domains. Nearly all such RTs are present within type III CRISPR-Cas loci, but their phylogeny does not parallel the CRISPR-Cas type classification, indicating that RT-Cas1 is an autonomous functional module that is disseminated by horizontal gene transfer and can function with diverse type III systems. To compare the sequence pools sampled by RT-Cas1-associated and RT-lacking CRISPR-Cas systems, we obtained samples of a commercially grown cyanobacterium-Arthrospira platensis Sequencing of the CRISPR arrays uncovered a highly diverse population of spacers. Spacer diversity was particularly striking for the RT-Cas1-containing type III-B system, where no saturation was evident even with millions of sequences analyzed. In contrast, analysis of the RT-lacking type III-D system yielded a highly diverse pool but reached a point where fewer novel spacers were recovered as sequencing depth was increased. Matches could be identified for a small fraction of the non-RT-Cas1-associated spacers, and for only a single RT-Cas1-associated spacer. Thus, the principal source(s) of the spacers, particularly the hypervariable spacer repertoire of the RT-associated arrays, remains unknown.IMPORTANCE While the majority of CRISPR-Cas immune systems adapt to foreign genetic elements by capturing segments of invasive DNA, some systems carry reverse transcriptases (RTs) that enable adaptation to RNA molecules. From analysis of available bacterial sequence data, we find evidence that RT-based RNA adaptation machinery has been able to join with CRISPR-Cas immune systems in many, diverse bacterial species. To investigate whether the abilities to adapt to DNA and RNA molecules are utilized for defense against distinct classes of invaders in nature, we sequenced CRISPR arrays from samples of commercial-scale open-air cultures of Arthrospira platensis, a cyanobacterium that contains both RT-lacking and RT-containing CRISPR-Cas systems. We uncovered a diverse pool of naturally occurring immune memories, with the RT-lacking locus acquiring a number of segments matching known viral or bacterial genes, while the RT-containing locus has acquired spacers from a distinct sequence pool for which the source remains enigmatic.


title: Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM

abstract: The combination of a direct electron-detection camera that can count individual electrons and an algorithm for correcting for beam-induced motion in cryo-EM will facilitate determination of three-dimensional structures of smaller, lower-symmetry macromolecular complexes to higher resolution than previously possible.


title: Learning from Ambiguously Labeled Face Images

abstract: Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled faces. We further extend MCar to incorporate the labeling constraints among instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvements on several ambiguously labeled datasets.


title: Using Software Non-Functional Assessment Process to Complement Function Points for Software Maintenance

abstract: Context: Most widely used cost models use source lines of code (SLOC) as the software size input measure, due to its quantifiability and high correlation with effort. Estimating the SLOC of a project is very difficult in early stages of the software lifecycle, especially for software maintenance tasks. Depending on the reuse model being used, one would need to size the existing code that needs modifications and the size of the changes being made in SLOC. Functional size measures, such as Function Points (FPs) and the Software Non-functional Assessment Process (SNAP), have been developed to improve the ability to estimate project size early in the lifecycle for both development and maintenance projects. While FPs represent software size by functions; SNAP complements FPs by sizing non-functional requirements, such as data operations and interface design. Goal: SNAP complements Function Points by sizing non-functional requirements, such as data operations and interface design. Through an empirical analysis, the authors want to determine whether SNAP might be an effective software size measure individually or in conjunction with FPs to improve effort estimation accuracy. Method: The empirical analysis will be run on Unified Code Count (UCC)'s dataset, a software tool maintained by University of Southern California (USC). Results: The analyses found that separating projects adding new functions from those modifying existing functions resulted in improved estimation models using SNAP. The effort estimation model for projects modifying functions in UCC had high prediction accuracy statistics, but less impressive results for projects adding existing functions to UCC. The effort estimation accuracy were satisfactory when using SNAP in conjunction with FPs for both groups of projects. Conclusions: SNAP, indeed, complements FPs in terms of the requirements that are considered and sized. Both size metrics should be treated as individual metrics, but can be used together for acceptably accurate cost models in UCC's development environment.


title: Profiling eicosanoids and phospholipids using LC-MS/MS: principles and recent applications.

abstract: Eicosanoids are potent lipid mediators involved in numerous physiological and pathophysiological processes. Precursors are polyunsaturated fatty acids liberated from membrane phospholipids. Thus, profiling and quantification of these molecules has gained a lot of attention during last years. Eicosanoids and phospholipids are commonly profiled by LC-MS/MSbecause this technique allows accurate quantification within acceptable run-times. This article therefore focuses on liquid chromatography and the ESI-MS/MS analysis of proinflammatory lipid mediators, particularly arachidonic acid (C20:4) derived eicosanoids and their precursors phospholipids. Recent analytical developments for quantification of these compounds are highlighted and analytical challenges are discussed. Furthermore, applications such as the use of these molecules as biomarkers are presented.


title: Visual reverse engineering of binary and data files

abstract: The analysis of computer files poses a difficult problem for security researchers seeking to detect and analyze malicious content, software developers stress testing file formats for their products, and for other researchers seeking to understand the behavior and structure of undocumented file formats. Traditional tools, including hex editors, disassemblers and debuggers, while powerful, constrain analysis to primarily text based approaches. In this paper, we present design principles for file analysis which support meaningful investigation when there is little or no knowledge of the underlying file format, but are flexible enough to allow integration of additional semantic information, when available. We also present results from the implementation of a visual reverse engineering system based on our analysis. We validate the efficacy of both our analysis and our system with case studies depicting analysis use cases where a hex editor would be of limited value. Our results indicate that visual approaches help analysts rapidly identify files, analyze unfamiliar file structures, and gain insights that inform and complement the current suite of tools currently in use.


title: The log-structured merge-tree (LSM-tree)

abstract: High-performance transaction system applications typically insert rows in a History table to provide an activity trace; at the same time the transaction system generates log records for purposes of system recovery. Both types of generated information can benefit from efficient indexing. An example in a well-known setting is the TPC-A benchmark application, modified to support efficient queries on the history for account activity for specific accounts. This requires an index by account-id on the fast-growing History table. Unfortunately, standard disk-based index structures such as the B-tree will effectively double the I/O cost of the transaction to maintain an index such as this in real time, increasing the total system cost up to fifty percent. Clearly a method for maintaining a real-time index at low cost is desirable. The log-structured mergetree (LSM-tree) is a disk-based data structure designed to provide low-cost indexing for a file experiencing a high rate of record inserts (and deletes) over an extended period. The LSM-tree uses an algorithm that defers and batches index changes, cascading the changes from a memory-based component through one or more disk components in an efficient manner reminiscent of merge sort. During this process all index values are continuously accessible to retrievals (aside from very short locking periods), either through the memory component or one of the disk components. The algorithm has greatly reduced disk arm movements compared to a traditional access methods such as B-trees, and will improve cost-performance in domains where disk arm costs for inserts with traditional access methods overwhelm storage media costs. The LSM-tree approach also generalizes to operations other than insert and delete. However, indexed finds requiring immediate response will lose I/O efficiency in some cases, so the LSM-tree is most useful in applications where index inserts are more common than finds that retrieve the entries. This seems to be a common property for history tables and log files, for example. The conclusions of Sect. 6 compare the hybrid use of memory and disk components in the LSM-tree access method with the commonly understood advantage of the hybrid method to buffer disk pages in memory.


title: The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF

abstract: The change of two orders of magnitude in the 'new DCF' of NIST's SRE'10, relative to the 'old DCF' evaluation criterion, posed a difficult challenge for participants and evaluator alike. Initially, participants were at a loss as to how to calibrate their systems, while the evaluator underestimated the required number of evaluation trials. After the fact, it is now obvious that both calibration and evaluation require very large sets of trials. This poses the challenges of (i) how to decide what number of trials is enough, and (ii) how to process such large data sets with reasonable memory and CPU requirements. After SRE'10, at the BOSARIS Workshop, we built solutions to these problems into the freely available BOSARIS Toolkit. This paper explains the principles and algorithms behind this toolkit. The main contributions of the toolkit are: 1. The Normalized Bayes Error-Rate Plot, which analyses likelihood- ratio calibration over a wide range of DCF operating points. These plots also help in judging the adequacy of the sizes of calibration and evaluation databases. 2. Efficient algorithms to compute DCF and minDCF for large score files, over the range of operating points required by these plots. 3. A new score file format, which facilitates working with very large trial lists. 4. A faster logistic regression optimizer for fusion and calibration. 5. A principled way to define EER (equal error rate), which is of practical interest when the absolute error count is small.


title: The Influence of Contour on Similarity Perception of Star Glyphs

abstract: We conducted three experiments to investigate the effects of contours on the detection of data similarity with star glyph variations. A star glyph is a small, compact, data graphic that represents a multi-dimensional data point. Star glyphs are often used in small-multiple settings, to represent data points in tables, on maps, or as overlays on other types of data graphics. In these settings, an important task is the visual comparison of the data points encoded in the star glyph, for example to find other similar data points or outliers. We hypothesized that for data comparisons, the overall shape of a star glyph---enhanced through contour lines--- would aid the viewer in making accurate similarity judgments. To test this hypothesis, we conducted three experiments. In our first experiment, we explored how the use of contours influenced how visualization experts and trained novices chose glyphs with similar data values. Our results showed that glyphs without contours make the detection of data similarity easier. Given these results, we conducted a second study to understand intuitive notions of similarity. Star glyphs without contours most intuitively supported the detection of data similarity. In a third experiment, we tested the effect of star glyph reference structures (i.e., tickmarks and gridlines) on the detection of similarity. Surprisingly, our results show that adding reference structures does improve the correctness of similarity judgments for star glyphs with contours, but not for the standard star glyph. As a result of these experiments, we conclude that the simple star glyph without contours performs best under several criteria, reinforcing its practice and popularity in the literature. Contours seem to enhance the detection of other types of similarity, e.g., shape similarity and are distracting when data similarity has to be judged. Based on these findings we provide design considerations regarding the use of contours and reference structures on star glyphs.


title: Feature Pyramid Network for Multi-class Land Segmentation

abstract: Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leaderboard score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.


title: Playful training with augmented reality games: case studies towards reality-oriented system design

abstract: In this paper, we propose a reality-oriented augmentation approach to support training activities. The approach aims at adding new value and playful features to traditional training environments with keeping their original look-and-feel. For example, a game monitoring service enables to automatically record game events so that players can review a gaming process and strategy for soul-searching, or replay most impressive scenes to share the experience with others after the game finishes. Even several services are running on background, digital devices and services are seamlessly integrated to the game environment in unobtrusive way so that players can concentrate on training as usual. The concept can be applied to both traditional games (e.g., poker and the game of Go) and non-gaming activities (e.g., calligraphy and drumming). We developed four case studies on the concept: Augmented Reality Go, EmoPoker, Augmented Calligraphy and AR Drum Kit. We discuss design issues in the reality-oriented augmentation process based on user study results.


title: Three-mobile-beacon assisted weighted centroid localization method in wireless sensor networks

abstract: Localization is one of the key technologies in wireless sensor networks, and the mobile beacon assisted localization method is promising to reduce the cost. The methods with only one beacon introduce collinearity problem which degrades performance. This paper proposes a weighted centroid localization method using three mobile beacons. These beacons preserve a special formation while traversing the network deployment area, and broadcast their positions periodically. The sensor nodes to be localized estimate the distances to these beacons, and utilize weighted centroid localization scheme to calculate its position. Simulation shows that this method is superior to trilateration and weighted centroid algorithm with single mobile beacon.


title: MIMO-OFDM for wireless communications: signal detection with enhanced channel estimation

abstract: Multiple transmit and receive antennas can be used to form multiple-input multiple-output (MIMO) channels to increase the capacity by a factor of the minimum number of transmit and receive antennas. In this paper, orthogonal frequency division multiplexing (OFDM) for MIMO channels (MIMO-OFDM) is considered for wideband transmission to mitigate intersymbol interference and enhance system capacity. The MIMO-OFDM system uses two independent space-time codes for two sets of two transmit antennas. At the receiver, the independent space-time codes are decoded using prewhitening, followed by minimum-Euclidean-distance decoding based on successive interference cancellation. Computer simulation shows that for four-input and four-output systems transmitting data at 4 Mb/s over a 1.25 MHz channel, the required signal-to-noise ratios (SNRs) for 10% and 1% word error rates (WER) are 10.5 dB and 13.8 dB, respectively, when each codeword contains 500 information bits and the channel's Doppler frequency is 40 Hz (corresponding normalized frequency: 0.9%). Increasing the number of the receive antennas improves the system performance. When the number or receive antennas is increased from four to eight, the required SNRs for 10% and 1% WER are reduced to 4 dB and 6 dB, respectively. Therefore, MIMO-OFDM is a promising technique for highly spectrally efficient wideband transmission.


title: i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery

abstract: Purpose: Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. Current techniques lack in accuracy and/or line-of-sight constraints (conventional tracking systems) or expose the patient to prohibitive ionizing radiation (intra-operative CT). A possible solution is to capture the instrument with a c-arm at irregular intervals and recover the pose from the image. Methods: i3PosNet infers the position and orientation of instruments from images using a pose estimation network. Said framework considers localized patches and outputs pseudo-landmarks. The pose is reconstructed from pseudo-landmarks by geometric considerations. Results: We show i3PosNet reaches errors less than 0.05mm. It outperforms conventional image registration-based approaches reducing average and maximum errors by at least two thirds. i3PosNet trained on synthetic images generalizes to real x-rays without any further adaptation. Conclusion: The translation of Deep Learning based methods to surgical applications is difficult, because large representative datasets for training and testing are not available. This work empirically shows sub-millimeter pose estimation trained solely based on synthetic training data.


title: Multi-task learning for smile detection, emotion recognition and gender classification

abstract: Facial expression analysis plays a key role in analyzing emotions and human behaviors. Smile detection, emotion recognition and gender classification are special tasks in facial expression analysis with various potential applications. In this paper, we propose an effective architecture of Convolutional Neural Network (CNN) which can jointly learn representations for three tasks: smile detection, emotion recognition and gender classification. In addition, this model can be trained from multiple sources of data with different kinds of task-specific class labels. The extensive experiments show that our model achieves superior accuracy over recent state-of-the-art techniques in all of three tasks on popular benchmarks. We also show that the joint learning helps the tasks with less data considerably benefit from other tasks with richer data.


title: A History of Metaheuristics

abstract: This chapter describes the history of metaheuristics in five distinct periods, starting long before the first use of the term and ending a long time in the future.


title: Intelligent Library Systems: Artificial Intelligence Technology and Library Automation Systems

abstract: Artificial Intelligence (AI) encompasses the following general areas of research: (1) automatic programming, (2) computer vision, (3) expert systems, (4) intelligent computer-assisted instruction, (5) natural language processing, (6) planning and decision support, (7) robotics, and (8) speech recognition. Intelligent library systems utilize artificial intelligence technologies to provide knowledge-based services to library patrons and staff. ::: This paper examines certain key aspects of AI that determine its potential utility as a tool for building library systems. It discusses the barriers that inhibit the development of intelligent library systems, and it suggests possible strategies for making progress in this important area. While all of the areas of AI research indicated previously may have some eventual application in the development of library systems, this paper primarily focuses on a few that the author judges to be of most immediate significance--expert systems, intelligent computer-assisted instruction, and natural language applications. This paper does not discuss the use of AI knowledge-bases in libraries as subject-oriented library materials.


title: CPAR: Classification based on Predictive Association Rules

abstract: Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as [7, 6], achieves higher classification accuracy than traditional classification approaches such as C4.5. However, the approach also suffers from two major deficiencies: (1) it generates a very large number of association rules, which leads to high processing overhead; and (2) its confidence-based rule evaluation measure may lead to overfitting. In comparison with associative classification, traditional rule-based classifiers, such as C4.5, FOIL and RIPPER, are substantially faster but their accuracy, in most cases, may not be as high. In this paper, we propose a new classification approach, CPAR (Classification based on Predictive Association Rules), which combines the advantages of both associative classification and traditional rule-based classification. Instead of generating a large number of candidate rules as in associative classification, CPAR adopts a greedy algorithm to generate rules directly from training data. Moreover, CPAR generates and tests more rules than traditional rule-based classifiers to avoid missing important rules. To avoid overfitting, CPAR uses expected accuracy to evaluate each rule and uses the best k rules in prediction.


title: Identifying Justifications in Written Dialogs By Classifying Text as Argumentative

abstract: In written dialog, discourse participants need to justify claims they make, to convince the reader the claim is true and/or relevant to the discourse. This paper presents a new task (with an associated corpus), namely detecting such justifications. We investigate the nature of such justifications, and observe that the justifications themselves often contain discourse structure. We therefore develop a method to detect the existence of certain types of discourse relations, which helps us classify whether a segment is a justification or not. Our task is novel, and our work is novel in that it uses a large set of connectives (which we call indicators), and in that it uses a large set of discourse relations, without choosing among them.


title: Dependence of self diffusivity on P and T in molten NaAlSi2O6: Comparison of laboratory and molecular dynamics experiments

abstract: We have used molecular dynamics (MD) simulation to investigate ionic self-diffusivity in NaAlSi2O6 melts and glasses at pressures of 3–58 GPa and temperatures of 2500–5000 K, to evaluate the effectiveness of a two-body potential in reproducing pressure and temperature dependence of tracer self-diffusivities. For each species, self-diffusivity increased with increasing temperature; with increasing pressure up to ∼15 GPa, the self-diffusivity of O, Si, and Al increased, while mobility of Na decreased. An Arrhenian fit to self-diffusivity data collected from simulations at temperatures above the kinetic glass transition (up to 15 GPa) gives: Species Ea Va log D* (kJ/mol) (×106m³/mol) (m²/s) Na 81±14 3.5±0.5 −6.8±0.17 Al 306±31 −5.6±1.1 −6.5±0.35 Si 294±23 −6.1±0.9 −6.9±0.26 O 302±23 −5.9±0.8 −6.6±0.26 for activation energy (Ea) and activation volume (Va). The MD-derived Ea,o and Va,o agree with laboratory results of 275±10 kJ/mol and −6.2±0.6 cm³/mol for Ea,o and Va,o respectively [Shimizu and Kushiro, 1984] measured in experiments at 1673 to 1923 K and 0.5 to 2 GPa. Agreement with the pre-exponential factor (D*) is poorer, with MD result of ∼10−6.6 for O and laboratory value ∼10−4.7. From ∼15 GPa–25 GPa, Va is ∼0 m³/mol for O, Si, and Al; these species have a positive Va for pressures above 25 GPa (Va,o is ∼ +1 cm³/mol). For pressures ∼15 GPa the majority of the network cations are in 5- or 6- fold coordination, and more than 20% of the O is in 3-fold coordination. With increasing pressure, 4-fold Si and Al decrease monotonically, while 6-fold Si and Al increase monotonically. Pentahedral Si and Al maximize at nearly the same pressure as O self-diffusivity.


A flexible model supporting the specification and enforcement of role-based authorization in workflow management systems


title: A structured approach to query recommendation with social annotation data

abstract: Query recommendation has been recognized as an important mean to help users search and also improve the usability of search engines. Existing approaches mainly focus on helping users refine their search queries and the recommendations typically stick to users' search intent, named search interests in this paper. However, users may also have some vague or delitescent interests which they are unaware of until they are faced with one, named exploratory interests. These interests may be provoked within a search session when users read a web page from search results or even follow links on the page. By considering exploratory interests in query recommendation, we attract more user clicks on recommendations. This type of query recommendation has not been explicitly addressed in previous work. In this paper, we propose to recommend queries in a structured way for better satisfying both search and exploratory interests of users. Specifically, we construct a query relation graph from query logs and social annotation data which capture two types of interests respectively. Based on the query relation graph, we employ hitting time to rank possible recommendations, leverage a modularity based approach to group top recommendations into clusters, and label each cluster with social tags. Empirical experimental results indicate that our structured approach to query recommendation with social annotation data can better satisfy users' interests and significantly enhance users' click behavior on recommendations.


Assignment Converting 2 D to 3 D : A Survey


Online Controlled Experiments and A / B Tests


title: Comparison of CNN models for application in crop health assessment with participatory sensing

abstract: Timely and robust diagnosis of plant diseases and nutrient deficiencies play a major role in management of crop yield. Automation is a low cost alternative to human experts and can help to detect early onset of crop diseases which aids faster decision making and in giving recommendations to farmers to curb yield loss. We have developed a smart-phone based participatory sensing application for agriculture which is used by farmers to scout their fields for events of interest, especially those related to crop health. Recently, deep convolutional neural networks (CNN) have emerged as a prominent technique in computer vision related challenges and such deep-learning based models could prove as an important tool to do just-in-time assessment of crop health. With a view to building state-of-the-art diagnostic capabilities on the phone, we present analysis of CNN models in terms of accuracy, memory, and inference time. Effects of change in hyperparameters have been evaluated in terms of accuracy. The trained model gives 99.7% classification accuracy with satisfactory inference time and model size which assures the application of CNN architectures for real-time crop state diagnosis on a large scale with limited hardware capabilities.


title: Persistent Authenticated Dictionaries and Their Applications

abstract: We introduce the notion of persistent authenticated dictionaries, that is, dictionaries where the user can make queries of the type "was element e in set S at time t?" and get authenticated answers. Applications include credential and certificate validation checking in the past (as in digital signatures for electronic contracts), digital receipts, and electronic tickets. We present two data structures that can efficiently support an infrastructure for persistent authenticated dictionaries, and we compare their performance.


title: Effects of Mindfulness-Based Stress Reduction (MBSR) on Emotion Regulation in Social Anxiety Disorder

abstract: Mindfulness-based stress reduction (MBSR) is an established program shown to reduce symptoms of stress, anxiety, and depression. MBSR is believed to alter emotional responding by modifying cognitive– affective processes. Given that social anxiety disorder (SAD) is characterized by emotional and attentional biases as well as distorted negative self-beliefs, we examined MBSR-related changes in the brain–behavior indices of emotional reactivity and regulation of negative self-beliefs in patients with SAD. Sixteen patients underwent functional MRI while reacting to negative self-beliefs and while regulating negative emotions using 2 types of attention deployment emotion regulation—breath-focused attention and distraction-focused attention. Post-MBSR, 14 patients completed neuroimaging assessments. Compared with baseline, MBSR completers showed improvement in anxiety and depression symptoms and self-esteem. During the breath-focused attention task (but not the distraction-focused attention task), they also showed (a) decreased negative emotion experience, (b) reduced amygdala activity, and (c) increased activity in brain regions implicated in attentional deployment. MBSR training in patients with SAD may reduce emotional reactivity while enhancing emotion regulation. These changes might facilitate reduction in SAD-related avoidance behaviors, clinical symptoms, and automatic emotional reactivity to negative self-beliefs in adults with SAD.


title: Real Estate Image Classification

abstract: Posting pictures is a necessary part of advertising a home for sale. Agents typically sort through dozens of images from which to pick the most complimentary ones. This is a manual effort involving annotating images accompanied by descriptions (bedroom, bathroom, attic, etc.). When volumes are small, manual annotation is not a problem, but there is a point where this becomes too burdensome and ultimately infeasible. Here, we propose an approach based on computer vision methodology to radically increase the efficiency of such tasks. We present a high-confidence image classification framework, whose inputs are images and outputs are labels. The core of the classification algorithm is long short term memory (LSTM), and fully connected neural networks, along with a substantial preprocessing using 'contrast-limited adaptive histogram equalization (CLAHE) for image enhancement. Since, there is no standard benchmark containing a comprehensive dataset of well-annotated real estate images, we introduce Real Estate Image (REI) database for evaluating the image classification algorithms. Therein we demonstrate empirics based on our proposed framework on the new REI dataset, as well as on the SUN dataset.


title: Storyboard augmentation of process model grammars for stakeholder communication

abstract: Process models are often used to visualize and communicate workflows to involved stakeholders. Unfortunately, process modeling notations can be complex and need specific knowledge to be understood. Storyboards, as a visual language to illustrate workflows as sequences of images, provide natural visualization features that allow for better communication, to provide insight to people from non-process modeling expert domains. This paper proposes a visualization approach using a 3D virtual world environment to visualize storyboards for business process models. A prototype was built to present its applicability via generating output with examples of five major process model patterns and two non-trivial use cases. Illustrative results for the approach show the promise of using a 3D virtual world to visualize complex process models in an unambiguous and intuitive manner.


title: WeChecker: efficient and precise detection of privilege escalation vulnerabilities in Android apps

abstract: Due to the rapid increase of Android apps and their wide usage to handle personal data, a precise and large-scaling checker is in need to validate the apps' permission flow before they are listed on the market. Several tools have been proposed to detect sensitive data leaks in Android apps. But these tools are not applicable to large-scale analysis since they fail to deal with the arbitrary execution orders of different event handlers smartly. Event handlers are invoked by the framework based on the system state, therefore we cannot pre-determine their order of execution. Besides, since all exported components can be invoked by an external app, the execution orders of these components are also arbitrary. A naive way to simulate these two types of arbitrary execution orders yields a permutation of all event handlers in an app. The time complexity is O(n!) where n is the number of event handlers in an app. This leads to a high analysis overhead when n is big. To give an illustration, CHEX [10] found 50.73 entry points of 44 unique class types in an app on average. In this paper we propose an improved static taint analysis to deal with the challenge brought by the arbitrary execution orders without sacrificing the high precision. Our analysis does not need to make permutations and achieves a polynomial time complexity. We also propose to unify the array and map access with object reference by propagating access paths to reduce the number of false positives due to field-insensitivity and over approximation of array access and map access. We implement a tool, WeChecker, to detect privilege escalation vulnerabilities [7] in Android apps. WeChecker achieves 96% precision and 96% recall in the state-of-the-art test suite DriodBench (for compairson, the precision and recall of FlowDroid [1] are 86% and 93%, respectively). The evaluation of WeChecker on real apps shows that it is efficient (average analysis time of each app: 29.985s) and fits for large-scale checking.


title: Microfabricated W-band traveling wave tubes

abstract: Two research programs are in progress to develop W-band travelling wave tubes (TWTs). One uses a folded waveguide (fwg) slow-wave circuit, and the other uses a novel, planar meander line circuit. Both devices are currently being assembled. The predicted performance, fabrication methods and challenges, and measured data to date will be presented.


An adaptable vertical partitioning method in distributed systems


Botta: An Arabic Dialect Chatbot


title: Structural Compression of Convolutional Neural Networks

abstract: Convolutional neural networks (CNNs) have state-of-the-art performance on many problems in machine vision. However, networks with superior performance often have millions of weights so that it is difficult or impossible to use CNNs on computationally limited devices or to humanly interpret them. A myriad of CNN compression approaches have been proposed and they involve pruning and compressing the weights and filters. In this article, we introduce a greedy structural compression scheme that prunes filters in a trained CNN. We define a filter importance index equal to the classification accuracy reduction (CAR) of the network after pruning that filter (similarly defined as RAR for regression). We then iteratively prune filters based on the CAR index. This algorithm achieves substantially higher classification accuracy in AlexNet compared to other structural compression schemes that prune filters. Pruning half of the filters in the first or second layer of AlexNet, our CAR algorithm achieves 26% and 20% higher classification accuracies respectively, compared to the best benchmark filter pruning scheme. Our CAR algorithm, combined with further weight pruning and compressing, reduces the size of first or second convolutional layer in AlexNet by a factor of 42, while achieving close to original classification accuracy through retraining (or fine-tuning) network. Finally, we demonstrate the interpretability of CAR-compressed CNNs by showing that our algorithm prunes filters with visually redundant functionalities. In fact, out of top 20 CAR-pruned filters in AlexNet, 17 of them in the first layer and 14 of them in the second layer are color-selective filters as opposed to shape-selective filters. To our knowledge, this is the first reported result on the connection between compression and interpretability of CNNs.


title: A probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring

abstract: Chronic diseases are major causes of deaths in Australia and throughout the world. This necessitates the need for a self-care, preventive, predictive and protective assisted living system where a patient can be monitored continuously using wearable and wireless sensors. In real-time home monitoring system, various biological signals of a patient are obtained continuously using a mobile device (smart phone or tablet) and sent to the cloud to discover patient-specific abnormalities. The objective of this work is to develop a probabilistic model that identifies the future clinical abnormalities of a patient using recent and past values of multiple vital signs (e.g. heart rate, blood pressure, respiratory rate). Chronic patients living alone in home die of various diseases for the lack of an efficient automated system having prior prediction ability in the irregularities of vital signs. In this paper, Hidden Markov Model (HMM) is adopted to predict different clinical onsets using the temporal behaviours of six biosignals. The HMM models are trained and evaluated using continuous monitoring data of more than 1000 patients collected from the MIMIC-II database of MIT physiobank archive. The best models are selected using expectation maximisation (EM) algorithm and used in personalized remote monitoring system to forecast the most probable forthcoming clinical states of a continuously monitored patient. The scalable power of cloud computing is utilized for fast learning of various clinical events from large samples. The results obtained from the innovative home-based monitoring application show a new approach of detecting clinical anomalies using multi-parameter trends.


title: Augmented Reality Scenarios for Guitar Learning

abstract: Abstract In this paper, an experimental self-teaching system capable of superimposing audio-visual information to support the process of learning to play the guitar is proposed. Different learning scenarios have been carefully designed according to diverse levels of experience and understanding and are presented in a simple way. Learners can select between representative numbers of scenarios and physically interact with the audio-visual information in a natural way. Audio-visual information can be placed anywhere on a physical space and multiple sound sources can be mixed to experiment with compositions and compilations. To assess the effectiveness of the system some initial evaluation is conducted. Finally conclusions and future work of the system are summarized. Categories: augmented reality, information visualisation, human-computer interaction, learning. 1 Introduction In the past years, online or CD-ROM books tend to replace the traditional paper based music books. E-learning has been on the focus of an enormous amount of research, both from academic and research communities. However, just a few experimental approaches have been developed trying to enable a complex computer interaction through the physical use of a musical instrument and digital information for the sake of learning. Augmented Reality is a technology that supplements the real world with virtual information that appears to coexist in the same space as the real world. Digital information does not have to be restricted only to vision but it can vary to other types of sensory information including hearing, touch and smell [AB


title: Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

abstract: This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.


title: Toward cloud computing QoS architecture: analysis of cloud systems and cloud services

abstract: Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service U+0028 QoS U+0029 by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.


Deception: the role of consequences


title: Electrooculography Based iOS Controller for Individuals with Quadriplegia or Neurodegenerative Disease

abstract: As the use of tablet computers and cell phones has become a standard medium of access to information, entertainment, and communication around the world, the reliance on having access to such devices has increased tremendously. For individuals with quadriplegia or neurodegenerative diseases, the access to these mobile devices is greatly hindered due to their inherent touchscreen design. Assistive technology solutions available to such patients today require families of patients to invest thousands of dollars in standalone tablet systems. There are few known options for allowing such patients to connect to their existing tablets or smartphones, which already have access to apps that can assist them in communication and daily activities. For this reason, we present in this paper a low-cost commercial off the shelf (COTS) assistive communication device to allow individuals with such conditions to access iOS based devices through electrooculography signals captured from their eye movements. Signals are captured through electrodes placed on the users face around the eyes. These signals are filtered, amplified, and processed to detect key eye movements mapped to perform control outputs sent to the iOS device. The communication capabilities are tested through the administration of a typing test to measure characters typed per minute (cpm). Testing of the device includes timed trials of directed tasks carried out by both healthy subjects and patients with ALS (PALS). It was determined that a user can type an average of 3.25 ~ 6.11 cpm using the device with an average accuracy of 89%. This could be significantly improved using a better suited keyboard application on the phone.


title: A practical analysis of EAP authentication methods

abstract: In the context of a growing concern for data security, network access control has become an important part of every network security system. The purpose of this paper is to provide a comparison between different EAP Methods that can be used with IEEE 802.1x, Port Based Network Access Control, standard as a means of protecting a computer network against unauthorized access. Three of the most common methods, EAP-MD5, EAP-TLS and PEAP, are compared with regard to time and packet performance both between each other and against a default situation, with no access control in place. The factors considered, authentication and reauthentication time, packet loss during reconnection and throughput are measured in a specially designed test environment using test equipment capable of accurate measurement of time in the range of milliseconds and constant high rate traffic generation.


title: Review of benefits and challenges of vehicle-to-grid technology

abstract: Plug-in electric vehicles (PEVs) can behave either as loads or as distributed energy sources in a concept known as vehicle-to-grid (V2G). The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural and technical obstacles. Although V2G operation can reduce the lifetime of PEVs, it is projected to be more economical for vehicle owners and grid operators. This paper reviews these benefits and challenges of V2G technology for both individual vehicles and vehicle fleets.


title: CRF framework for supervised preference aggregation

abstract: We develop a flexible Conditional Random Field framework for supervised preference aggregation, which combines preferences from multiple experts over items to form a distribution over rankings. The distribution is based on an energy comprised of unary and pairwise potentials allowing us to effectively capture correlations between both items and experts. We describe procedures for learning in this modelnand demonstrate that inference can be done much more efficiently thannin analogous models. Experiments on benchmark tasks demonstrate significant performance gains over existing rank aggregation methods.


title: Self-assembled plasmonic nanoparticle clusters

abstract: The self-assembly of colloids is an alternative to top-down processing that enables the fabrication of nanostructures. We show that self-assembled clusters of metal-dielectric spheres are the basis for nanophotonic structures. By tailoring the number and position of spheres in close-packed clusters, plasmon modes exhibiting strong magnetic and Fano-like resonances emerge. The use of identical spheres simplifies cluster assembly and facilitates the fabrication of highly symmetric structures. Dielectric spacers are used to tailor the interparticle spacing in these clusters to be approximately 2 nanometers. These types of chemically synthesized nanoparticle clusters can be generalized to other two- and three-dimensional structures and can serve as building blocks for new metamaterials.


title: An Automated Social Graph De-anonymization Technique

abstract: Copyright © 2014 ACM. We present a generic and automated approach to re-identifying nodes in anonymized social networks which enables novel anonymization techniques to be quickly evaluated. It uses machine learning (decision forests) to matching pairs of nodes in disparate anonymized sub-graphs. The technique uncovers artefacts and invariants of any black-box anonymization scheme from a small set of examples. Despite a high degree of automation, classification succeeds with significant true positive rates even when small false positive rates are sought. Our evaluation uses publicly available real world datasets to study the performance of our approach against realworld anonymization strategies, namely the schemes used to protect datasets of The Data for Development (D4D) Challenge. We show that the technique is effective even when only small numbers of samples are used for training. Further, since it detects weaknesses in the black-box anonymization scheme it can re-identify nodes in one social network when trained on another.


title: Supervised prediction of drug–target interactions using bipartite local models

abstract: Motivation:In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug–target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug–target interactions. ::: ::: Results: We propose a novel supervised inference method to predict unknown drug–target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug–target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug–target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug–target interactions. ::: ::: Availability: An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/. ::: ::: Contact: kevbleakley@gmail.com ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: Comparing Data Mining Techniques for Mining Patents

abstract: Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents. Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents. Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents.


title: CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing

abstract: Cloud computing is the future generation of computational services delivered over the Internet. As cloud infrastructure expands, resource management in such a large heterogeneous and distributed environment is a challenging task. In a cloud environment, uncertainty and dispersion of resources encounters problems of allocation of resources. Unfortunately, existing resource management techniques, frameworks and mechanisms are insufficient to handle these environments, applications and resource behaviors. To provide an efficient performance and to execute workloads, there is a need of quality of service (QoS) based autonomic resource management approach which manages resources automatically and provides reliable, secure and cost efficient cloud services. In this paper, we present an intelligent QoS-aware autonomic resource management approach named as CHOPPER (Configuring, Healing, Optimizing and Protecting Policy for Efficient Resource management). CHOPPER offers self-configuration of applications and resources, self-healing by handling sudden failures, self-protection against security attacks and self-optimization for maximum resource utilization. We have evaluated the performance of the proposed approach in a real cloud environment and the experimental results show that the proposed approach performs better in terms of cost, execution time, SLA violation, resource contention and also provides security against attacks.


title: Image emotional classification: static vs. dynamic

abstract: Grouping images into emotional categories is an important and challenging problem in content-based image retrieval. In this paper, we propose an approach to classify art paintings into emotional categories (dynamic vs. static). The key points are feature selection and classification algorithm. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (weighted line direction-length vector) is proposed, which includes both orientation and length information of lines in an image, Then classification is performed by SVM (support vector machine) and images can be classified into dynamic vs. static. Experimental results demonstrate the effectiveness and superiority of our approach.


title: An efficient 3D face recognition approach using local geometrical signatures

abstract: This paper presents a computationally efficient 3D face recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the face. Kernel Principal Component Analysis (KPCA) is then used to extract the mid-level features from the extracted ARSs to improve the discriminative power. The mid-level features are then concatenated into a single feature vector and fed into a Support Vector Machine (SVM) to perform face recognition. The proposed approach addresses the expression variation problem by using facial scans with various expressions of different individuals for training. We conducted a number of experiments on the Face Recognition Grand Challenge (FRGC v2.0) and the 3D track of Shape Retrieval Contest (SHREC 2008) datasets, and a superior recognition performance has been achieved. Our experimental results show that the proposed system achieves very high Verification Rates (VRs) of 97.8% and 88.5% at a 0.1% False Acceptance Rate (FAR) for the "neutral vs. nonneutral" experiments on the FRGC v2.0 and the SHREC 2008 datasets respectively, and 96.7% for the ROC III experiment of the FRGC v2.0 dataset. Our experiments also demonstrate the computational efficiency of the proposed approach. HighlightsNovel facial Angular Radial Signatures (ARSs) are proposed for 3D face recognition.The Signatures are extracted from the semi-rigid facial regions.A two-stage mapping-based classification strategy is used to perform face recognition.ARSs combined with machine learning techniques can handle expression variations.State-of-the-art performance on two public datasets with high efficiency is achieved.


title: High performance dynamic lock-free hash tables and list-based sets

abstract: Lock-free (non-blocking) shared data structures promise more robust performance and reliability than conventional lock-based implementations. However, all prior lock-free algorithms for sets and hash tables suffer from serious drawbacks that prevent or limit their use in practice. These drawbacks include size inflexibility, dependence on atomic primitives not supported on any current processor architecture, and dependence on highly-inefficient or blocking memory management techniques.Building on the results of prior researchers, this paper presents the first CAS-based lock-free list-based set algorithm that is compatible with all lock-free memory management methods. We use it as a building block of an algorithm for lock-free hash tables. In addition to being lock-free, the new algorithm is dynamic, linearizable, and space-efficient.Our experimental results show that the new algorithm outperforms the best known lock-free as well as lock-based hash table implementations by significant margins, and indicate that it is the algorithm of choice for implementing shared hash tables.


title: Change in Penile Length in Children: Preliminary Study

abstract: Purpose: Studies of penile length in children have been rarely conducted. In Korea, great improvements in height and weight have been observed because of economic development over the past 25 years. We investigated the current status of penile length in Korean children and compared the results with those of a previous Korean study conducted in 1987. Materials and Methods: The subjects in this study were 233 boys aged 1 to 158 months, each of whom had been brought to outpatient clinics between April and October 2011. Penile length was measured according to the stretched penile length (SPL) technique; testicular size was measured (in ml) by using orchidometry. A comparison of penile lengths between the current study and the 1987 study was made by using Student’s t-test. Results: SPL increased significantly by 0.7 to 1.1 cm in most age groups (p＜0.05). Current anthropometric measures of Korean children such as height, body weight, and testicular size have increased compared with those from 1987. Conclusions: Penile length has increased significantly over the last quarter century. Therefore, it is suggested that novel reference values for penile length in prepubertal Korean children be determined in studies with a larger community-based population in order to diagnose and treat size-related penile disorders.


title: Rice quality analysis based on physical attributes using image processing technique

abstract: This review paper is for quality control of rice which is most important crop for human as well as in food market using image processing techniques and computer vision. Basic problem in Indian food industry that performs quality check manually by human inspectors which is non-reliable, costly and time consuming.


title: Walls Have Ears: Traffic-based Side-channel Attack in Video Streaming

abstract: Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic Adaptive Streaming over HTTP (DASH) becomes the de facto standard of video streaming and it is adopted by Youtube, Netflix, etc. Despite of the popularity, network traffic during video streaming shows identifiable pattern which brings threat to user privacy. In this paper, we propose a video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bitrate trend remains relatively stable because of the widely used Variable Bit-Rate (VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, we propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. We evaluate our attack method in different scenarios for various video content, segment lengths and quality levels. The experimental results show that the identification accuracy can reach up to 90 % using only three-minute continuous network traffic eavesdropping.


title: New Advanced SiC Ceramics Based on Natural Biomaterials

abstract: On the basis of several years experiments in investigation of hetero-modulus material structures and using natural biomaterials and high purity quartz powders the authors successfully developed new high porosity low density SiC ceramic cellulars and foams. For the development of new silicon-carbide and carbon-silicon-carbide (C/SiC) cellular ceramic composites and foams the author used high purity SiO2 powders mined in Fehervarcsurgo (Hungary) and a biomaterial reagents made from renewable vegetable under trade-name IG-R1. These low density high porosity silicon-carbides probably can be successfully applied in development of light weight ceramic reinforced metal alloy composites in the future. The structure and X-ray diffraction (XRD) analysis of used raw materials and the achieved by authors new SiC and C/SiC ceramic composites and foams are described and shown in present work.


title: Pornography, Sex Crime, and Paraphilia

abstract: The current paper reviews research findings concerning the association of pornography with sexual violence and paraphilic interests. Little clarity concerning the causal impact of pornography on sexual aggression or child-oriented sexual behavior has been achieved in the scientific literature. Laboratory experimentation demonstrates that violent pornography may contribute to antiwoman aggression, but the artificiality and constraints of the experimental setting severely limit generalization of these findings to real-world situations, and observational studies in natural settings consistently find no association or an inverse association of pornography with sexual aggression. In addition, although pedophiles often use child pornography, the causal impact of child pornography on child sexual offending is not conclusive. The current analysis considers the confluence of predisposing factors and pornography use as issues requiring clinical judgment in the reduction of sexual aggression and management of paraphilic interest in children.


title: Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

abstract: EEG-based Brain-computer interfaces (BCI) are facing grant challenges in their real-world applications. The technical difficulties in developing truly wearable multi-modal BCI systems that are capable of making reliable real-time prediction of users’ cognitive states under dynamic real-life situations may appear at times almost insurmountable. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report our attempt to develop a pervasive on-line BCI system by employing state-of-art technologies such as multi-tier fog and cloud computing, semantic Linked Data search and adaptive prediction/classification models. To verify our approach, we implement a pilot system using wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end fog servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end cloud servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch and the UCSD Movement Disorder Center to use our system in real-life personal stress and in-home Parkinson’s disease patient monitoring experiments. We shall proceed to develop a necessary BCI ontology and add automatic semantic annotation and progressive model refinement capability to our system.


title: Comparison Of Laser Scanning And Photogrammetry And Their Use For Digital Recording Of Cultural Monument Case Study: Byzantine Land Walls-Istanbul

abstract: Abstract. 3D modeling of cultural monuments is very crucial issue for preparing restoration projects. However, it has challenges such as data acquisition, preparation and processing. 3D modeling of objects can be time consuming and may include some difficulties due to the complexity of the structures. 3D terrestrial laser (TLS) scanning technique is one of the reliable and advantageous methods for 3D reconstruction of monuments. This technique is commonly acknowledged due to its accuracy, speed and flexibility. But the suitability and capability of this technique depends on proper usage, and good survey planning. Magnificent developments in highresolution digital sensor technologies leaded to manufacturing of new camera systems. Parallel to these innovations, development of computer systems and image processing techniques made enable to obtain multiple image-based 3D object models. In the presented study, TLS method has been compared to conventional photogrammetric and image-based dense matching methods. Automatic dense point creation has been realized by our developed algorithm and PIXEL-PHOTO software which generates 3D point clouds from stereo images. The reliability and encountered problems during point cloud measurement process have been discussed. The study area has been chosen as historical Byzantine Land Walls of Istanbul, which constitute a remarkable area defining the ancient city’s historical peninsula.


title: Intelligent obstacle avoidance control strategy for wheeled mobile robot

abstract: This paper presents intelligent strategies for a wheeled mobile robot to avoid obstacle and move to target location. The obstacle detection for the wheeled mobile robot is carried out by ultrasonic sensors. There are two models in this study, short-distance obstacle avoidance model and target-driven obstacle avoidance model. In short-distance obstacle avoidance model, the wheeled mobile robot utilizes signals of the ultrasonic sensors to avoid obstacle. In target-driven obstacle avoidance model, fuzzy theory with sensor signals is used to control the speed of the wheeled mobile robot and make it move to target location. In this study, software simulations are made on the MATLAB platform. Both obstacle avoidance models are successfully performed.


title: Job Recommendation from Semantic Similarity of LinkedIn Users' Skills

abstract: Until recently job seeking has been a tricky, tedious and time consuming process, because people looking for a new position had to collect information from many different sources. Job recommendation systems have been proposed in order to automate and simplify this task, also increasing its effectiveness. However, current approaches rely on scarce manually collected data that often do not completely reveal people skills. Our work aims to find out relationships between jobs and people skills making use of data from LinkedIn usersâ?? public profiles. Semantic associations arise by applying Latent Semantic Analysis (LSA). We use the mined semantics to obtain a hierarchical clustering of job positions and to build a job recommendation system. The outcome proves the effectiveness of our method in recommending job positions. Anyway, we argue that our approach is definitely general, because the extracted semantics could be worthy not only for job recommendation systems but also for recruiting systems. Furthermore, we point out that both the hierarchical clustering and the recommendation system do not require parameters to be tuned.