title: Muscle Growth and Poultry Meat Quality Issues

abstract: Over the past 50 years the worldwide growing demand of poultry meat has resulted in pressure on breeders, nutritionists and growers to increase the growth rate of birds, feed efficiency, size of breast muscle and reduction in abdominal fatness. Moreover, the shift toward further processed products has emphasized the necessity for higher standards in poultry meat to improve sensory characteristics and functional properties. It is believed that genetic progress has put more stress on the growing bird and it has resulted in histological and biochemical modifications of the muscle tissue by impairing some meat quality traits. The most current poultry meat quality concerns are associated with deep pectoral muscle disease and white striping which impair product appearance, and increased occurrence of problems related with the meat's poor ability to hold water during processing and storage (PSE-like condition) as well as poor toughness and cohesiveness related to immaturity of intramuscular connective tissue. This paper is aimed at making a general statement of recent studies focusing on the relationship between muscle growth and meat quality issues in poultry.


title: PROSPECTS OF ACCESSING DNA BANKS FOR THE ISOLATION OF GENES ENCODING BIOLOGICALLY ACTIVE PROTEINS

abstract: Summary - The feasibility of utilizing genes from DNA banks is examined. A summary of the efforts to clone genes of phenylpropanoid metabolism is presented. These genes have been cloned from five major phenylpropanoid pathways: central phenylpropanoid, lignin branch, flavonoid branch, isoflavonoid branch and the stilbene branch pathway. Genes have been cloned from the following alkaloid classes: monoterpene indole, indole and isoquinoline, isoquinoline, and tropane. Two other gene classes are reviewed: genes that encode bioactive proteins and regulatory proteins. The major limitation for utilization of DNA from banks does not appear to be technology, but the lack of detailed basic knowledge concerning the biochemistry, bioactivity, and pharmacology of plant chemicals.


title: Universal construction based on 3D printing electric motors: Steps towards self-replicating robots to transform space exploration

abstract: Through a recent confluence of technological capacities, self-replicating robots have become a potentially nearterm rather than speculative technology. In a practical sense, self-replicating robots can never become obsolete — the first self- replicating robots will spawn all future generations of robots, subject to deliberate upgrading and/or evolutionary change. Furthermore, this technology promises to revolutionise space exploration by bypassing the apparently-insurmountable problem of high launch costs. We present recent efforts in 3D printing the key robotic components required for any such self- replicating machine.


title: Ontology Engineering in Yorùbá Cultural Heritage Domain

abstract: In this paper, we describe an engineering process of building an ontology for Yoruba Cultural Heritage (YCH). The aim of this work is to design and implement an ontology representing knowledge embedded in YCH as a digital (documentation) resource. Building an indigenous knowledge management system requires a formal representation of YCH concepts and their relationships. Even so, YCH resources are endangered in the face of globalisation, westernisation and inter-ethnic interaction with the available few locked up in memory institutions. A combination of observation, consultation, documented materials and prototyping was used for the knowledge elicitation from domain experts. Formal Concept Analysis approach was adopted for the design aspect of the work. And, Protege was used for the implementation. The resulting ontology was jointly validated with both the domain experts and the ontology experts. The final ontology represents the documentation of the elicited knowledge of the YCH domain. In conclusion, the formal digital documentation resulting from the study is a useful software artefact for the development of a semantic application for Yoruba heritage domain.


title: Higher-order logical inference with compositional semantics

abstract: We present a higher-order inference system based on a formal compositional semantics and the wide-coverage CCG parser. We develop an improved method to bridge between the parser and semantic composition. The system is evaluated on the FraCaS test suite. In contrast to the widely held view that higher-order logic is unsuitable for efficient logical inferences, the results show that a system based on a reasonably-sized semantic lexicon and a manageable number of non-first-order axioms enables efficient logical inferences, including those concerned with generalized quantifiers and intensional operators, and outperforms the state-of-the-art firstorder inference system.


Cross correlation velocity measurement of multiphase flow


title: Acute Sexual Assault in the Pediatric and Adolescent Population.

abstract: Children and adolescents are at high risk for sexual assault. Early medical and mental health evaluation by professionals with advanced training in sexual victimization is imperative to assure appropriate assessment, forensic evidence collection, and follow-up. Moreover, continued research and outreach programs are needed for the development of preventative strategies that focus on this vulnerable population. In this review we highlight key concepts for assessment and include a discussion of risk factors, disclosure, sequelae, follow-up, and prevention.


title: Low-rank matrix completion by Riemannian optimization---extended version

abstract: The matrix completion problem consists of finding or approximating a low-rank matrix based on a few samples of this matrix. We propose a new algorithm for matrix completion that minimizes the least-square distance on the sampling set over the Riemannian manifold of fixed-rank matrices. The algorithm is an adaptation of classical non-linear conjugate gradients, developed within the framework of retraction-based optimization on manifolds. We describe all the necessary objects from differential geometry necessary to perform optimization over this low-rank matrix manifold, seen as a submanifold embedded in the space of matrices. In particular, we describe how metric projection can be used as retraction and how vector transport lets us obtain the conjugate search directions. Finally, we prove convergence of a regularized version of our algorithm under the assumption that the restricted isometry property holds for incoherent matrices throughout the iterations. The numerical experiments indicate that our approach scales very well for large-scale problems and compares favorably with the state-of-the-art, while outperforming most existing solvers.


title: Accurate Real-time Map Matching for Challenging Environments

abstract: We present the SnapNet system, which provides accurate real-time map matching for cellular-based trajectory traces. Such traces are characterized by input locations that are far from the actual road segment, errors on the order of kilometers, back-and-forth transitions, and highly sparse input data. SnapNet applies a series of filters to handle the noisy locations and an interpolation stage to address the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints in the estimation process and a number of heuristics to reduce the noise and provide real-time estimations. Evaluation of SnapNet using actual traces from different cities covering more than 400 km shows that it can achieve a precision and recall of more than 90% under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall, respectively, when compared to the traditional HMM map-matching algorithms. Moreover, SnapNet has a latency of 0.58 ms per location estimate.


title: Inverse agonism at adrenergic and opioid receptors: studies with wild type and constitutively active mutant receptors.

abstract: Ligands which display inverse agonism at G protein-coupled receptors do so by decreasing the intrinsic ability of a receptor to active the cellular G protein population in the absence of an agonist ligand. Expression of the murine delta opioid receptor in Rat-1 fibroblasts resulted in the inverse agonist ICI174864 being able to cause inhibition of basal high affinity GTPase activity and of the binding of [35S]GTP gamma S in membranes of a clone (D2) of these cells which expresses high levels of the receptor. These effects were blocked by co-addition of the neutral antagonist TIPP[psi], demonstrating a requirement for the delta opioid receptor, and by pertussis toxin pretreatment of the cells, showing them to be produced via a Gi-like G protein. The inverse agonist properties of ICI174864 could also be demonstrated in whole cells. Stimulation of forskolin-amplified adenylyl cyclase activity was produced by ICI174864 following [3H]adenine prelabelling of the cells. Constitutively activated mutants of receptors should provide a convenient means to detect inverse agonists. Incubation of cells either transiently or stably transfected with a constitutively activated mutant of the human beta 2-adrenoceptor with the beta 2-inverse agonists betaxolol or sotalol, which are both able to inhibit CAM beta 2-adrenoceptor-mediated basal adenylyl cyclase activity, resulted in a strong upregulation of levels of the receptor. In the stable cells lines this effect was prevented by co-incubation with neutral antagonists but could not be reproduced by an adenylyl cyclase P-site ligand which also inhibited basal adenylyl cyclase levels.


Smart mechatronic driver for surgical trajectory navigation


title: Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation

abstract: Nonnegative matrix factorization (NMF) algorithms often suffer from slow convergence speed due to the nonnegativity constraints, especially for large-scale problems. Low-rank approximation methods such as principle component analysis (PCA) are widely used in matrix factorizations to suppress noise, reduce computational complexity and memory requirements. However, they cannot be applied to NMF directly so far as they result in factors with mixed signs. In this paper, low-rank approximation is introduced to NMF (named lraNMF), which is not only able to reduce the computational complexity of NMF algorithms significantly, but also suppress bipolar noise. In fact, the new update rules are typically about times faster than traditional ones of NMF, here is the number of observations and is the low rank of latent factors. Therefore lraNMF is particularly efficient in the case where , which is the general case in NMF. The proposed update rules can also be incorporated into most existing NMF algorithms straightforwardly as long as they are based on Euclidean distance. Then the concept of lraNMF is generalized to the tensor field to perform a fast sequential nonnegative Tucker decomposition (NTD). By applying the proposed methods, the practicability of NMF/NTD is significantly improved. Simulations on synthetic and real data show the validity and efficiency of the proposed approaches.


title: Solving the Sports League Scheduling Problem with Tabu Search

abstract: In this paper we present a tabu approach for a version of the Sports League Scheduling Problem. The approach adopted is based on a formulation of the problem as a Constraint Satisfaction Problem (CSP). Tests were carried out on problem instances of up to 40 teams representing 780 integer variables with 780 values per variable. Experimental results show that this approach outperforms some existing methods and is one of the most promising methods for solving problems of this type.


title: Software Vulnerability Discovery Techniques: A Survey

abstract: Software vulnerabilities are the root cause of computer security problem. How people can quickly discover vulnerabilities existing in a certain software has always been the focus of information security field. This paper has done research on software vulnerability techniques, including static analysis, Fuzzing, penetration testing. Besides, the authors also take vulnerability discovery models as an example of software vulnerability analysis methods which go hand in hand with vulnerability discovery techniques. The ending part of the paper analyses the advantages and disadvantages of each technique introduced here and talks about the future direction of this field.


title: Champollion: A Robust Parallel Text Sentence Aligner

abstract: This paper describes Champollion, a lexicon-based sentence aligner designed for robust alignment of potential noisy parallel text. Champollion increases the robustness of the alignment by assigning greater weights to less frequent translated words. Experiments on a manually aligned Chinese – English parallel corpus show that Champollion achieves high precision and recall on noisy data. Champollion can be easily ported to new language pairs. It’s freely available to the public.


title: Pre-training of Recurrent Neural Networks via Linear Autoencoders

abstract: We propose a pre-training technique for recurrent neural networks based on linear autoencoder networks for sequences, i.e. linear dynamical systems modelling the target sequences. We start by giving a closed form solution for the definition of the optimal weights of a linear autoencoder given a training set of sequences. This solution, however, is computationally very demanding, so we suggest a procedure to get an approximate solution for a given number of hidden units. The weights obtained for the linear autoencoder are then used as initial weights for the input-to-hidden connections of a recurrent neural network, which is then trained on the desired task. Using four well known datasets of sequences of polyphonic music, we show that the proposed pre-training approach is highly effective, since it allows to largely improve the state of the art results on all the considered datasets.


title: THE IMPACT OF ERP ON SUPPLY CHAIN MANAGEMENT : EXPLORATORY FINDINGS FROM A EUROPEAN DELPHI STUDY

abstract: Abstract This article presents results from a Delphi study on the future impact of enterprise resource planning (ERP) systems on supply chain management (SCM). The Delphi study was conducted with 23 Dutch supply chain executives of European multi-nationals. Findings from this exploratory study were threefold. First, our executives have identified the following key SCM issues for the coming years: (1) further integration of activities between suppliers and customers across the entire supply chain; (2) on-going changes in supply chain needs and required flexibility from IT; (3) more mass customization of products and services leading to increasing assortments while decreasing cycle times and inventories; (4) the locus of the driver’s seat of the entire supply chain and (5) supply chains consisting of several independent enterprises. The second main finding is that the panel experts saw only a modest role for ERP in improving future supply chain effectiveness and a clear risk of ERP actually limiting progress in SCM. ERP was seen as offering a positive contribution to only four of the top 12 future supply chain issues: (1) more customization of products and services; (2) more standardized processes and information; (3) the need for worldwide IT systems; and (4) greater transparency of the marketplace. Implications for subsequent research and management practice are discussed. The following key limitations of current ERP systems in providing effective SCM support emerge as the third finding from this exploratory study: (1) their insufficient extended enterprise functionality in crossing organizational boundaries; (2) their inflexibility to ever-changing supply chain needs, (3) their lack of functionality beyond managing transactions, and (4) their closed and non-modular system architecture. These limitations stem from the fact that the first generation of ERP products has been designed to integrate the various operations of an individual firm. In modern SCM, however, the unit of analysis has become a network of organizations, rendering these ERP products inadequate in the new economy.


title: Canny Edge-Detection Based Vehicle Plate Recognition

abstract: Vehicle plate recognition is an effective image processing technique used to identify vehicles' plate numbers. There are several applications for this technique which expand through many fields and interest groups. Vehicle plate recognition may be used as a marketing tool, for purposes of traffic and border control, for law enforcement, and travel. Many methods have been proposed to facilitate this technique. This study proposes an edgedetection method to enable a Plate Recognition System through practical situations, such as various environmental or meteorological conditions. Image processing tools are used to scan the plate area, resize it, and convert it toward a gray scale prior to filtering the image in order to remove small objects. The obtained objects are identified such that the numbers object is recognized. The details of the obtained image are controlled through the standard deviation of the Gaussian filter (sigma).


title: Video summarization of surveillance cameras

abstract: The number of video surveillance cameras has increased by a large amount in recent years. There is therefore a need to process the captured videos such that human operators can quickly review the activities recorded by a camera over a long period of time. We propose in this paper an approach for producing video summaries, an abbreviated video preserving the important elements of interest. We introduce a dataset as well as three evaluation metrics for quantifying the performance of a video summary with respect to compression length, the amount of activity retained and the amount of activity that is packed into each frame of the summary.


title: Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals

abstract: Recent years have witnessed an increased interest in the application of persistent homology, a topological tool for data analysis, to machine learning problems. Persistent homology is known for its ability to numerically characterize the shapes of spaces induced by features or functions. On the other hand, deep neural networks have been shown effective in various tasks. To our best knowledge, however, existing neural network models seldom exploit shape information. In this paper, we investigate a way to use persistent homology in the framework of deep neural networks. Specifically, we propose to embed the so-called "persistence landscape," a rather new topological summary for data, into a convolutional neural network (CNN) for dealing with audio signals. Our evaluation on automatic music tagging, a multi-label classification task, shows that the resulting persistent convolutional neural network (PCNN) model can perform significantly better than state-of-the-art models in prediction accuracy. We also discuss the intuition behind the design of the proposed model, and offer insights into the features that it learns.


title: Karma: A System for Mapping Structured Sources into the Semantic Web ⋆

abstract: The Linked Data cloud contains large amounts of RDF data generated from databases. Much of this RDF data, generated using tools such as D2R, is expressed in terms of vocabularies automatically derived from the schema of the original database. The generated RDF would be significantly more useful if it were expressed in terms of commonly used vocabularies. Using today’s tools, it is labor-intensive to do this. For example, one can first use D2R to automatically generate RDF from a database and then use R2R to translate the automatically generated RDF into RDF expressed in a new vocabulary. The problem is that defining the R2R mappings is difficult and labor intensive because one needs to write the mapping rules in terms of SPARQL graph patterns. In this work, we present a semi-automatic approach for building mappings that translate data in structured sources to RDF expressed in terms of a vocabulary of the user’s choice. Our system, Karma, automatically derives these mappings, and provides an easy to use interface that enables users to control the automated process to guide the system to produce the desired mappings. In our evaluation, users need to interact with the system less than once per column (on average) in order to construct the desired mapping rules. The system then uses these mapping rules to generate semantically rich RDF for the data sources. We demonstrate Karma using a bioinformatics example and contrast it with other approaches used in that community. Bio2RDF [7] and Semantic MediaWiki Linked Data Extension (SMW-LDE) [2] are examples of efforts that integrate bioinformatics datasets by mapping them to a common vocabulary. We applied our approach to a scenario used in the SMW-LDE that integrate ABA, Uniprot, KEGG Pathway, PharmGKB and Linking Open Drug Data datasets using a


title: Ischio femoral impingement in an 11-year-old girl

abstract: Ischiofemoral impingement (IFI) is the entrapment of the quadratus femoris muscle (QFM) between the trochanter mi- nor of the femur and the ischium-hamstring tendon. Patients with IFI generally present with hip pain, which may radiate toward the knee. Although there is no specific diagnostic clini- cal test for this disorder, the presence of QFM edema/fatty replacement and narrowing of the ischiofemoral space and the quadratus femoris space on magnetic resonance imaging (MRI) are suggestive of IFI. The optimal treatment strategy of this syndrome remains obscure. Patients may benefit from a conservative treatment regimen that includes rest, activity restriction, nonsteroidal anti-inflammatory drugs, and reha- bilitation procedures, just as with other impingement syn- dromes. Herein we report an 11-year-old girl with IFI who was successfully treated conservatively. To our knowledge, our case is the youngest patient reported in the English literature. MRI remains an important tool in the diagnosis of IFI, and radiologists should be aware of the specific features of this entity.


title: Studying political microblogging: Twitter users in the 2010 Swedish election campaign

abstract: Among the many so-called microblogging services that allow their users to describe their current status in short posts, Twitter is probably among the most popular and well known. Since its launch i ...


title: Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy

abstract: Summary Background Strong evidence shows that physical inactivity increases the risk of many adverse health conditions, including major non-communicable diseases such as coronary heart disease, type 2 diabetes, and breast and colon cancers, and shortens life expectancy. Because much of the world's population is inactive, this link presents a major public health issue. We aimed to quantify the effect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level. Methods For our analysis of burden of disease, we calculated population attributable fractions (PAFs) associated with physical inactivity using conservative assumptions for each of the major non-communicable diseases, by country, to estimate how much disease could be averted if physical inactivity were eliminated. We used life-table analysis to estimate gains in life expectancy of the population. Findings Worldwide, we estimate that physical inactivity causes 6% (ranging from 3·2% in southeast Asia to 7·8% in the eastern Mediterranean region) of the burden of disease from coronary heart disease, 7% (3·9–9·6) of type 2 diabetes, 10% (5·6–14·1) of breast cancer, and 10% (5·7–13·8) of colon cancer. Inactivity causes 9% (range 5·1–12·5) of premature mortality, or more than 5·3 million of the 57 million deaths that occurred worldwide in 2008. If inactivity were not eliminated, but decreased instead by 10% or 25%, more than 533 000 and more than 1·3 million deaths, respectively, could be averted every year. We estimated that elimination of physical inactivity would increase the life expectancy of the world's population by 0·68 (range 0·41–0·95) years. Interpretation Physical inactivity has a major health effect worldwide. Decrease in or removal of this unhealthy behaviour could improve health substantially. Funding None.


title: ZVS phase shift full bridge converter with controlled leakage inductance of transformer

abstract: Generally additional leakage inductance and two clamp diodes are adopted into the conventional phase shift full bridge (PSFB) converter for reducing the voltage stress of secondary rectifier diodes and extending the range of zero voltage switching (ZVS) operation. However, the core and copper loss caused by additional leakage inductor can be high enough to decrease the whole efficiency of DC/DC converter. Therefore, a new ZVS PSFB converter with controlled leakage inductance of transformer is proposed. The proposed converter makes both the transformer and additional leakage inductor with same ferrite core by separated primary winding (SPW). Using this method, leakage inductance is controlled by the winding ratio of SPW. Moreover, by using this integrated magnetic component with single core, size and core loss can be greatly reduced and it results in the improvement of efficiency and power density of DC/DC converter. The operational principle and analysis of proposed converter are presented and verified by the 1.2kW prototype.


title: Managerial Modes of Influence and Counterproductivity in Organizations: A Longitudinal Business-Unit-Level Investigation

abstract: Boston College The authors studied the effect of 3 modes of managerial influence (managerial oversight, ethical leadership, and abusive supervision) on counterproductivity, which was conceptualized as a unit-level outcome that reflects the existence of a variety of intentional and unintentional harmful employee behaviors in the unit. Counterproductivity was represented by an objective measure of food loss in a longitudinal study of 265 restaurants. After prior food loss and alternative explanations (e.g., turnover, training, neighborhood income) were controlled for, results indicated that managerial oversight and abusive supervision significantly influenced counterproductivity in the following periods, whereas ethical leadership did not. Counterproductivity was also found to be negatively related to both restaurant profitability and customer satisfaction in the same period and to mediate indirect relationships between managerial influences and distal unit outcomes.


title: An analysis of involvement of HCI experts in distributed software development: practical issues

abstract: Traditionally, free and open source software (F/OSS) developers have focused more on the features of a specific application, most of the time ignoring the necessity of user-centric design. This has mainly stemmed from the fact that developers have little interaction with HCI studies, knowledge bases and reports. Moreover, the lack of user interface designers has resulted in a lack of awareness of this area. As a consequence, the user centric design phenomenon within F/OSS applications has been neglected. In this paper, we have mentioned various problems that would slow down a F/OSS project development towards a user-engineered software, and investigated the ways that HCI experts and developers interact with each other and researched bug reporting systems by means of eligibility to issue usability bugs. For the conclusion part, we have explored possible ways to achieve a user-centric design in a project with asynchronous interaction among geographically distributed developers.


title: The Use of Facebook for Educational Purposes in EFL Classrooms

abstract: The objective of this study is to analyze the possibility of blending conventional instruction with online instruction via a social networking website, Facebook, in EFL classrooms in order to motivate students and improve their English language learning. Thus, this paper seeks to examine specific ways in which EFL teachers can use Facebook as an educational tool, describing the benefits of this technological instrument and analyzing the potential pitfalls and challenges that it could create. Besides, it includes practical strategies that teachers can apply in order to overcome these pitfalls and get the most out of this social network.


title: Deeply learned attributes for crowded scene understanding

abstract: Crowded scene understanding is a fundamental problem in computer vision. In this study, we develop a multi-task deep model to jointly learn and combine appearance and motion features for crowd understanding. We propose crowd motion channels as the input of the deep model and the channel design is inspired by generic properties of crowd systems. To well demonstrate our deep model, we construct a new large-scale WWW Crowd dataset with 10, 000 videos from 8, 257 crowded scenes, and build an attribute set with 94 attributes on WWW. We further measure user study performance on WWW and compare this with the proposed deep models. Extensive experiments show that our deep models display significant performance improvements in cross-scene attribute recognition compared to strong crowd-related feature-based baselines, and the deeply learned features behave a superior performance in multi-task learning.


title: Fast sort on CPUs and GPUs: a case for bandwidth oblivious SIMD sort

abstract: Sort is a fundamental kernel used in many database operations. In-memory sorts are now feasible; sort performance is limited by compute flops and main memory bandwidth rather than I/O. In this paper, we present a competitive analysis of comparison and non-comparison based sorting algorithms on two modern architectures - the latest CPU and GPU architectures. We propose novel CPU radix sort and GPU merge sort implementations which are 2X faster than previously published results. We perform a fair comparison of the algorithms using these best performing implementations on both architectures. While radix sort is faster on current architectures, the gap narrows from CPU to GPU architectures. Merge sort performs better than radix sort for sorting keys of large sizes - such keys will be required to accommodate the increasing cardinality of future databases. We present analytical models for analyzing the performance of our implementations in terms of architectural features such as core count, SIMD and bandwidth. Our obtained performance results are successfully predicted by our models. Our analysis points to merge sort winning over radix sort on future architectures due to its efficient utilization of SIMD and low bandwidth utilization. We simulate a 64-core platform with varying SIMD widths under constant bandwidth per core constraints, and show that large data sizes of 240 (one trillion records), merge sort performance on large key sizes is up to 3X better than radix sort for large SIMD widths on future architectures. Therefore, merge sort should be the sorting method of choice for future databases.


title: Multiple feature extraction for early Parkinson risk assessment based on transcranial sonography image

abstract: Transcranial sonography (TCS) is a new tool for the diagnosis of Parkinson's disease (PD) at a very early state. The TCS image of mesencephalon shows a distinct hyperechogenic pattern in about 90% PD patients. This pattern is usually manually segmented and the substantia nigra (SN) region can be used as an early PD indicator. However this method is based on manual evaluation of examined images. The extraction of multiple features from TCS images characterizing the half mesencephalon morphology and structure can be used to validate the observer-independent PD indicator. We propose hybrid feature extraction methods which includes statistical, geometrical and texture features for the early PD risk assessment. These features are tested with support vector machines (SVMs). Furthermore five features are selected with the sequential feature selection methods. The results show that the correct rate of the classification with these five features is reaching 96%.


title: Combining evidence from multiple searches

abstract: At Virginia Tech and PRC Inc. investigations with TREC data have focused on developing and comparing mechanims for combining evidence related to a number of search schemes. Our work with the first CD-ROM has included various indexing, weighting, retrieval, combination, evaluation, and failure analysis efforts. Related work reported elsewhere in the proceedings by Paul Thompson discusses extensions undertaken by PRC Inc. and an evaluation of those results. Futur's work will develop our ideas further, try them out with additional data, and hoefully be evaluated in connection with other work on TREC and TIPSTER


title: Methodologies of targeting – Renaissance militarism attacking Christianity as ‘weakness’

abstract: Intellectuals in the post-Medieval West, striving for scholarly emancipation, developed methodologies to target so as to free themselves from the dominant Christian, largely Roman Catholic, intellectual tradition. Machiavelli was one such critic, calling for the repristination of pre-Christian classics. Such methodologies are never without an inherent quest for power. Machiavelli developed a Classicist, ‘heroic exemplar’ hermeneutic in order to extol the republicanist, manly, ferocious, imperialist virtues of Rome, vis-a-vis the divisive, meek, caring, justice-seeking, unpatriotic, Christian leaders of his day. He therefore initiated Modern ideological militaristic competitiveness with its mutually suspicious balance of powers practices. This article forms part of a series of articles on methodologies of targeting groups.


title: Automatic detection and quantification of brain midline shift using anatomical marker model

abstract: Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected anatomical markers to delineate the deformed midline and quantify the shift. For each anatomical marker, the detector generates five candidate points. Then the best candidate for each marker is selected based on the statistical distribution of features characterizing the spatial relationships among the markers. Experiments show that the proposed method outperforms previous methods, especially in the cases of large intra-cerebral hemorrhage and missing ventricles. A brain CT retrieval system is also developed based on the brain midline shift quantification results.


title: Voronoi-based trajectory optimization for UGV path planning

abstract: Optimal path planning in dynamic environments for an unmanned vehicle is a complex task of mobile robotics that requires an integrated approach. This paper describes a path planning algorithm, which allows to build a preliminary motion trajectory using global information about environment, and then dynamically adjust the path in real-time by varying objective function weights. We introduce a set of key parameters for path optimization and the algorithm implementation in MATLAB. The developed algorithm is suitable for fast and robust trajectory tuning to a dynamically changing environment and is capable to provide efficient planning for mobile robots.


title: Visual Exploration of Semantic Relationships in Neural Word Embeddings

abstract: Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.


Abstract Optimizing Intel EPIC/Itanium2 Architecture for Forth


title: Measuring large-scale distributed systems: case of BitTorrent Mainline DHT

abstract: Peer-to-peer networks have been quite thoroughly measured over the past years, however it is interesting to note that the BitTorrent Mainline DHT has received very little attention even though it is by far the largest of currently active overlay systems, as our results show. As Mainline DHT differs from other systems, existing measurement methodologies are not appropriate for studying it. In this paper we present an efficient methodology for estimating the number of active users in the network. We have identified an omission in previous methodologies used to measure the size of the network and our methodology corrects this. Our method is based on modeling crawling inaccuracies as a Bernoulli process. It guarantees a very accurate estimation and is able to provide the estimate in about 5 seconds. Through experiments in controlled situations, we demonstrate the accuracy of our method and show the causes of the inaccuracies in previous work, by reproducing the incorrect results. Besides accurate network size estimates, our methodology can be used to detect network anomalies, in particular Sybil attacks in the network. We also report on the results from our measurements which have been going on for almost 2.5 years and are the first long-term study of Mainline DHT.


title: The Albany Approach to Urban Youth Development.

abstract: This paper describes an approach to urban youth leadership development, the Albany Institutes for Urban Youth Leaders, which validates urban students' educational aspirations and fosters identification with academic and prosocial concerns. The program helps strengthen relationships between students' promotive characteristics and their involvement in school and community activities. Negative stereotypes abound in the descriptions of urban youths in the professional and public press. Urban students sometimes even find that their positive characteristics, such as high educational aspirations, are seen as at-risk characteristics because they are incongruent with negative stereotypes. This situation may contribute to the gap between educational aspirations and achievement among urban students of color. The Albany Institutes provide experiences that confirm the promotive characteristics of urban youth and their families and treat them as if they are gifted and talented. The curriculum and themes for each of the Institutes are the result of extensive community involvement. The Institutes themselves offer opportunities for students to participate in democratic decision making processes that promote their social responsibilities and enhance cognitive and moral development. The Institutes also offer prosocial peer group experiences. Evaluations of the Institutes indicate that students especially appreciate the teambuilding experiences and their relationships with mentors. (Contains 34 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document. arb-a4/7/ Edacatton/ opo-rts/ Sp ri,n4g, 2000 Schoo l. o f Ectaoctuo-ni ni/varsit-y at,4 lbany 1 PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. EAR? Copy AVAMAIELS The Albany Approach Page 1 [Running Head: The Albany Approach] The Albany Approach to Urban Youth Development Donald Biggs School of Education University at Albany Albany, NY Robert Colesante Department of Education Siena College Loudonville, NY Paper Presented at the American Educational Research Association Conference held at New Orleans, Louisiana. April, 2000.


title: SOFT-IoT: Self-Organizing FOG of Things

abstract: The Internet of Things (IoT) has advanced in different directions, including the development of new architectures, platforms and applications. This multidirectional progress has resulted in the creation of several parallel IoT ecosystems called verticals. Therefore, several approaches have proposed solutions for interoperability of these ecosystems through the delivery of services over virtual infrastructure (Cloud Computing). We introduce the paradigm of Fog of Things (FoT) and propose the design and development of a self-organizing platform called SOFT-IoT: Self-Organizing Fog of Things. SOFT-IoT implements the "Fog Computing" concept introduced by CISCO for the Internet of Things, where part of data processing capacity and service delivery operations are processed locally in "small servers", i.e., close to where data is collected. SOFT-IoT deals with protocols to facilitate the local computing processing and rely in more complex operations running in virtual entities avoiding the traditional approach of centralized cloud computing solutions. In this way, SOFT-IoT enables interoperability of local ecosystems in the fog and also at the cloud level, where other data is stored, processed and complex operations are resolved. In other words, in SOFT-IoT the data processing and delivery of services occurs locally in order to overcome with current infrastructure limitations and data processing alleviating those demands for heavy computational resources that can occur in remote servers, i.e., geographically away from where data is generated.


title: High Step-Up Active-Clamp Converter With Input-Current Doubler and Output-Voltage Doubler for Fuel Cell Power Systems

abstract: A high-efficiency high step-up DC-DC converter is proposed for fuel cell power systems. The proposed system consists of an input-current doubler, an output-voltage doubler, and an active-clamp circuit. The input-current doubler and the output-voltage doubler provide a much higher voltage conversion ratio without using a high turns ratio in the transformer and increase the overall efficiency. A series-resonant circuit of the output-voltage doubler removes the reverse-recovery problem of the rectifying diodes. The active-clamp circuit clamps the surge voltage of switches and recycles the energy stored in the leakage inductance of the transformer. The operation principle of the converter is analyzed and verified. A 1 kW prototype is implemented to show the performance of the proposed converter. The prototype achieved a European efficiency of 96% at an input voltage of 30 V.


A New Dataset for Fine-Grained Citation Field Extraction


title: Business process mining: An industrial application

abstract: Contemporary information systems (e.g., WfM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful process mining techniques and software vendors are incorporating these in their software, few of the more advanced process mining techniques have been tested on real-life processes. This paper describes the application of process mining in one of the provincial offices of the Dutch National Public Works Department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of process mining techniques, we analyzed the processing of invoices sent by the various subcontractors and suppliers from three different perspectives: (1) the process perspective, (2) the organizational perspective, and (3) the case perspective. For this purpose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of process mining in general and our algorithms and tools in particular.


title: Lexicalized Semi-incremental Dependency Parsing

abstract: Even leaving aside concerns of cognitive plausibility, incremental parsing is appealing for applications such as speech recognition and machine translation because it could allow for incorporating syntactic features into the decoding process without blowing up the search space. Yet, incremental parsing is often associated with greedy parsing decisions and intolerable loss of accuracy. Would the use of lexicalized grammars provide a new perspective on incremental parsing? In this paper we explore incremental left-to-right dependency parsing using a lexicalized grammatical formalism that works with lexical categories (supertags) and a small set of combinatory operators. A strictly incremental parser would conduct only a single pass over the input, use no lookahead and make only local decisions at every word. We show that such a parser suffers heavy loss of accuracy. Instead, we explore the utility of a two-pass approach that incrementally builds a dependency structure by first assigning a supertag to every input word and then selecting an incremental operator that allows assembling every supertag with the dependency structure built so-far to its left. We instantiate this idea in different models that allow a trade-off between aspects of full incrementality and performance, and explore the differences between these models empirically. Our exploration shows that a semi-incremental (two-pass), linear-time parser that employs fixed and limited look-ahead exhibits an appealing balance between the efficiency advantages of incrementality and the achieved accuracy. Surprisingly, taking local or global decisions matters very little for the accuracy of this linear-time parser. Such a parser fits seemlessly with the currently dominant finite-state decoders for machine translation.


title: Selecting requirement elicitation techniques for software projects

abstract: Software development process consists of many knowledge intensive processes, among which requirement elicitation process is perhaps the most critical for the success of the software system. Requirement elicitation process is intended to gain knowledge about user's requirement or need. Usually, the selection of requirement elicitation techniques is based on the company practice or on the personal experience. Moreover, there is a little guidance available on how to select elicitation techniques for a new software project. In this paper, we first provide a brief overview of the techniques available to support requirement elicitation process and identify their contextual applications. Next, we developed a framework to select elicitation techniques for a given software project based on the alignment of project's contextual information and the elicitation techniques. We demonstrate the applicability of the proposed framework by using illustrative examples and show how the framework uses the contextual knowledge of the software being develop to select useful requirement elicitation techniques.


title: Faster integer multiplication

abstract: For more than 35 years, the fastest known method for integer multiplication has been the Schonhage-Strassen algorithm running in time O(n log n log log n). Under certain restrictive conditions there is a corresponding Ω(n log n) lower bound. The prevailing conjecture has always been that the complexity of an optimal algorithm is Θ(n log n). We present a major step towards closing the gap from above by presenting an algorithm running in time n log n, 2O(log* n). The main result is for boolean circuits as well as for multitape Turing machines, but it has consequences to other models of computation as well.


title: Precision nitrogen management of wheat. A review

abstract: Conventional farming has led to extensive use of chemicals and, in turn, to negative environmental impacts such as soil erosion, groundwater pollution and atmosphere contamination. Farming systems should be more sustainable to reach economical and social profitability as well as environmental preservation. A possible solution is to adopt precision agriculture, a win–win option for sustaining food production without degrading the environment. Precision technologies are used for gathering information about spatial and temporal differences within the field in order to match inputs to site-specific field conditions. Here we review reports on the precision N management of wheat crop. The aims are to perform an investigation both on approaches and results of site-specific N management of wheat and to analyse performance and sustainability of this agricultural practice. In this context, we analysed literature of the last 10–15 years. The major conclusions are: (a) before making N management decisions, both the measurement and understanding of soil spatial variability and the wheat N status are needed. Complementary use of different sensors has improved soil properties assessment at relatively low cost; (b) results show the usefulness of airborne images, remote and proximal sensing for predicting crop N status by responsive in-season management approaches; (c) red edge and near-infrared bands can penetrate into higher vegetation fraction of the canopy. These narrowbands better estimated grain yield, crop N and water status, with R 2 higher than 0.70. In addition, different hyperspectral vegetation indices accounted for a high variability of 40–75 % of wheat N status; (d) various diagnostic tools and procedures have been developed in order to help wheat farmers for planning variable N rates. In-season adjustments in N fertilizer management can account for the specific climatic conditions and yield potential since less than 30 % of spatial variance could show temporal stability; (e) field studies in which sensor-based N management systems were compared with common farmer practices showed high increases in the N use efficiency of up to 368 %. These systems saved N fertilizers, from 10 % to about 80 % less N, and reduced residual N in the soil by 30–50 %, without either reducing yields or influencing grain quality; (f) precision N management based on real-time sensing and fertilization had the highest profitability of about $5–60 ha−1 compared to undifferentiated applications.


title: Large Scale Parallel Solution of Incompressible Flow Problems Using Uintah and Hypre

abstract: The Uintah Software framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids together with a novel asynchronous task-based approach with fully automated load balancing. As Uintah is often used to solve incompressible flow problems in combustion applications it is important to have a scalable linear solver. While there are many such solvers available, the scalability of those codes varies greatly. The hypre software offers a range of solvers and pre-conditioners for different types of grids. The weak scalability of Uintah and hypre is addressed for particular examples of both packages when applied to a number of incompressible flow problems. After careful software engineering to reduce startup costs, much better than expected weak scalability is seen for up to 100K cores on NSFs Kraken architecture and up to260K cpu cores, on DOEs new Titan machine. The scalability is found to depend in a crtitical way on the choice of algorithm used by hypre for a realistic application problem.


title: Characteristics of AMI using DLMS/COSEM and IEEE 802.15.4g multi-hop wireless communication

abstract: IEC 62056 for upper-layer protocols and IEEE 802.15.4g for communication infrastructure are promising means of advanced metering infrastructure (AMI) in Japan. However, since the characteristics of a communication system based on these combined technologies have yet to be identified, this paper gives the communication failure rates and latency acquired by calculations. In addition, the calculation results suggest some adequate AMI configurations, and show its extensibility in consideration of the usage environment.


Sentiment Analysis and Opinion Mining


title: Developing a Standard for Personal Health Devices based on 11073

abstract: This paper describes the process and outcome of the efforts to develop a new standard for Personal Health Data (PHD) based on the existing 11073 family of standards for medical devices. It identifies the requirements for a standard that is to be applied to small devices with limited resources of processor, memory and power and that will use short range wireless technology. It describes how existing components of 11073, such the Domain Information Model and nomenclature have been used and adapted to create the new standard.


Quantity and Quality Aware Artificial Bee Colony Algorithm for Clustering


Dissemination: Some Progress and Prospects


title: Cognitive and psychological flexibility after a traumatic brain injury and the implications for treatment in acceptance-based therapies: A conceptual review.

abstract: This paper provides a selective review of cognitive and psychological flexibility in the context of treatment for psychological distress after traumatic brain injury, with a focus on acceptance-based therapies. Cognitive flexibility is a component of executive function that is referred to mostly in the context of neuropsychological research and practice. Psychological flexibility, from a clinical psychology perspective, is linked to health and well-being and is an identified treatment outcome for therapies such as acceptance and commitment therapy (ACT). There are a number of overlaps between the constructs. They both manifest in the ability to change behaviour (either a thought or an action) in response to environmental change, with similarities in neural substrate and mental processes. Impairments in both show a strong association with psychopathology. People with a traumatic brain injury (TBI) often suffer impairments in their cognitive flexibility as a result of damage to areas controlling executive p...


title: Segmenting and Tagging Text with Neural Networks

abstract: Segmentation and tagging of text are important preprocessing steps for higher-level natural language processing tasks. In this thesis, we apply a sequence labelling framework based on neural networ ...


title: Usage of Model Driven Environment for the Classification of ECG features: A Systematic Review

abstract: Electrocardiography (ECG) constitutes a perfect and primary diagnostic tool for measuring the different morbidity conditions of the heart in the context of different heart diseases and arrhythmia. Various studies have proposed different techniques of classification of ECG features and defined the parametric structure of different features of ECG. This paper is primarily designed to provide a more accurate classification by inducting the concept of a model-driven environment (MDE). Such induction works on the basis of reusable factors that are fitted to the state-of-the-art parametric structure of the ECG features. Some issues and challenges related to the embedding process are highlighted in the form of research questions. The aim of this paper is to provide the solutions to these research questions. The literature review is completed in two phases. In the first phase, those articles are collected that have been published in IEEE Xplore, ACM Library, Science Direct, and Springer, from 2008 to 2017.The second phase as a part of the execution stage is completed at the three different levels of the rectification process by adhering to the Kitchen ham guideline. At the first level, articles are filtered according to title and abstract. At the second level, articles are filtered according to specific eligibility criteria, and at the last level, articles are selected based on the skills of different domain experts (authors) by checking the quality assessment parameters. The significance of MDE in the classification of different ECG features is reflected in their compatibility with the research questions. Furthermore, future directions are proposed that depict the significance of dependencies involvement in classification analysis of ECG. These future directions are identified based on the planning and execution of our operational investigation and our critical observation of the existing gaps between dependencies of features classification that is the major cause of cardiovascular diseases.


title: Can prosody be used to discover hierarchical structure in continuous speech

abstract: We tested whether adult listeners can simultaneously keep track of variations in pitch and syllable duration in order to segment continuous speech into phrases and group these phrases into sentences. The speech stream was constructed so that prosodic cues signaled hierarchical structures (i.e., phrases embedded within sentences) and non-adjacent relations (i.e., AxB rules within phrases), while transitional probabilities between syllables favored adjacent dependencies that straddled phrase and sentence boundaries. In Experiments 1 and 2, participants hierarchically segmented the stream and learned the grammar used to generate the phrases when prosodic cues were consistent with their native language. In Experiment 3, participants segmented the stream based on transitional probabilities when no prosodic cues were present and all syllables had the same pitch and duration. In Experiment 4, participants were able to exploit non-native prosody in order to learn hierarchical relations and non-adjacent dependencies. These results suggest that prosodic cues such as pitch declination and final lengthening provide a stronger basis for learning than transitional probabilities even when both are unfamiliar and not wholly consistent with native language informational structure.


title: Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

abstract: Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness1.


title: A first approach to argument-based recommender systems based on defeasible logic programming

abstract: Recommender systems have evolved in the last years as specialized tools to assist users in a plethora of computermediated tasks by providing guidelines or hints. Most recommender systems are aimed at facilitating access to relevant items, a situation particularly common when performing web-based tasks. At the same time, defeasible argumentation has evolved as a successful approach in AI to model commonsense qualitative reasoning, with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents a first approach towards the integration of web-based recommender systems with a defeasible argumentation framework. The final goal is to enhance practical reasoning capabilities of current recommender system technology by incorporating argument-based qualitative inference.


title: Twitter Trending Topic Classification

abstract: With the increasing popularity of microblogging sites, we are in the era of information explosion. As of June 2011, about 200 million tweets are being generated everyday. Although Twitter provides a list of most popular topics people tweet about known as Trending Topics in real time, it is often hard to understand what these trending topics are about. Therefore, it is important and necessary to classify these topics into general categories with high accuracy for better information retrieval. To address this problem, we classify Twitter Trending Topics into 18 general categories such as sports, politics, technology, etc. We experiment with 2 approaches for topic classification, (i) the well-known Bag-of-Words approach for text classification and (ii) network-based classification. In text-based classification method, we construct word vectors with trending topic definition and tweets, and the commonly used tf-idf weights are used to classify the topics using a Naive Bayes Multinomial classifier. In network-based classification method, we identify top 5 similar topics for a given topic based on the number of common influential users. The categories of the similar topics and the number of common influential users between the given topic and its similar topics are used to classify the given topic using a C5.0 decision tree learner. Experiments on a database of randomly selected 768 trending topics (over 18 classes) show that classification accuracy of up to 65% and 70% can be achieved using text-based and network-based classification modeling respectively.


title: A mixed bag of emotions: Model, predict, and transfer emotion distributions

abstract: This paper explores two new aspects of photos and human emotions. First, we show through psychovisual studies that different people have different emotional reactions to the same image, which is a strong and novel departure from previous work that only records and predicts a single dominant emotion for each image. Our studies also show that the same person may have multiple emotional reactions to one image. Predicting emotions in “distributions” instead of a single dominant emotion is important for many applications. Second, we show not only that we can often change the evoked emotion of an image by adjusting color tone and texture related features but also that we can choose in which “emotional direction” this change occurs by selecting a target image. In addition, we present a new database, Emotion6, containing distributions of emotions.


title: Investigating queries and search failures in academic search

abstract: Academic search concerns the retrieval and profiling of information objects in the domain of academic research. In this paper we reveal important observations of academic search queries, and provide an algorithmic solution to address a type of failure during search sessions: null queries. We start by providing a general characterization of academic search queries, by analyzing a large-scale transaction log of a leading academic search engine. Unlike previous small-scale analyses of academic search queries, we find important differences with query characteristics known from web search. E.g., in academic search there is a substantially bigger proportion of entity queries, and a heavier tail in query length distribution. We then focus on search failures and, in particular, on null queries that lead to an empty search engine result page, on null sessions that contain such null queries, and on users who are prone to issue null queries. In academic search approximately 1 in 10 queries is a null query, and 25% of the sessions contain a null query. They appear in different types of search sessions, and prevent users from achieving their search goal. To address the high rate of null queries in academic search, we consider the task of providing query suggestions. Specifically we focus on a highly frequent query type: non-boolean informational queries. To this end we need to overcome query sparsity and make effective use of session information.We find that using entities helps to surface more relevant query suggestions in the face of query sparsity. We also find that query suggestions should be conditioned on the type of session in which they are offered to be more effective. After casting the session classification problem as a multi-label classification problem, we generate session-conditional query suggestions based on predicted session type. We find that this session-conditional method leads to significant improvements over a generic query suggestion method. Personalization yields very little further improvements over session-conditional query suggestions.


title: Vision-based rain sensing with an in-vehicle camera

abstract: An intelligent wiper speed adjustment system can be found in most middle and upper class cars. A core piece of this gadget is the rain sensor on the windshield. With the upcoming number of cars being equipped with an in-vehicle camera for vision-based applications the call for integrating all sensors in the area of the rearview mirror into one device rises to reduce the number of parts and variants. In this paper, functionality of standard rain sensors and different vision-based approaches are explained and a novel rain sensing concept based on an automotive in-vehicle camera for Driver Assistance Systems (DAS) is developed to enhance applicability. Hereby, the region at the bottom of the field of view (FOV) of the imager is used to detect raindrops, while the upper part of the image is still usable for other vision-based applications. A simple algorithm is set up to keep the additional processing time low and to quantitatively gather the rain intensity. Mechanisms to avoid false activations of the wipers are introduced. First experimental experiences based on real scenarios show promising results.


title: Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory

abstract: Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.


title: Multiple Social Identities and Stereotype Threat: Imbalance, Accessibility, and Working Memory

abstract: In 4 experiments, the authors showed that concurrently making positive and negative self-relevant stereotypes available about performance in the same ability domain can eliminate stereotype threat effects. Replicating past work, the authors demonstrated that introducing negative stereotypes about women’s math performance activated participants’ female social identity and hurt their math performance (i.e., stereotype threat) by reducing working memory. Moving beyond past work, it was also demonstrated that concomitantly presenting a positive self-relevant stereotype (e.g., college students are good at math) increased the relative accessibility of females’ college student identity and inhibited their gender identity, eliminating attendant working memory deficits and contingent math performance decrements. Furthermore, subtle manipulations in questions presented in the demographic section of a math test eliminated stereotype threat effects that result from women reporting their gender before completing the test. This work identifies the motivated processes through which people’s social identities became active in situations in which self-relevant stereotypes about a stigmatized group membership and a nonstigmatized group membership were available. In addition, it demonstrates the downstream consequences of this pattern of activation on working memory and performance.


title: A Unified Sentence Space for Categorical Distributional-Compositional Semantics: Theory and Experiments

abstract: This short paper summarizes a faithful implementation of the categorical framework of Coecke et al. (2010), the aim of which is to provide compositionality in distributional models of lexical semantics. Based on Frobenius Algebras, our method enable us to (1) have a unifying meaning space for phrases and sentences of different structure and word vectors, (2) stay faithful to the linguistic types suggested by the underlying type-logic, and (3) perform the concrete computations in lower dimensions by reducing the space complexity. We experiment with two different parameters of the model and apply the setting to a verb disambiguation and a term/definition classification task with promising results.


title: Two-mode-interference switching in photonic crystal waveguides

abstract: A tunable photonic crystal switch based on two-mode-interference directional coupler is investigated. By modulating the central row of the rods of the coupler to change the real part of the refractive index, the bar-cross switch is obtained. Finite difference time-domain method (FDTD) is employed to predict the characterization of the switch, with crosstalk 30 dB.


title: XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing

abstract: Purpose ::: To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. ::: ::: Methods ::: Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. ::: ::: Results ::: XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. ::: ::: Conclusion ::: XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. Magn Reson Med 75:775–788, 2016. © 2015 Wiley Periodicals, Inc.


title: Spectral Estimation-Based OFDM Radar Algorithms for IEEE 802.11a Signals

abstract: Recently, OFDM radar has gained attention as new algorithms for range and Doppler estimation specific to OFDM signals have been developed. The major advantage of OFDM is that it is both well-suited for radar processing as well as being suitable for communication. In previous work, we have proposed parametrizations for OFDM signals which suit the radar problem well. However, the proposed algorithms are independent of the OFDM signal parametrization, and it is therefore interesting to see how well they work with other, more commonly available signals. For this paper, we examine a common type of signal-OFDM signals according to the IEEE 802.11a standard-and determine how they could be used in a radar application.


title: Learning Non-Stationary Dynamic Bayesian Networks

abstract: Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional DBN structure learning is that the data are generated by a stationary process, an assumption that is not true in many important settings. In this paper, we introduce a new class of graphical model called a non-stationary dynamic Bayesian network, in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. Non-stationary dynamic Bayesian networks represent a new framework for studying problems in which the structure of a network is evolving over time. Some examples of evolving networks are transcriptional regulatory networks during an organism's development, neural pathways during learning, and traffic patterns during the day. We define the non-stationary DBN model, present an MCMC sampling algorithm for learning the structure of the model from time-series data under different assumptions, and demonstrate the effectiveness of the algorithm on both simulated and biological data.


title: Deep Fundamental Matrix Estimation

abstract: We present an approach to robust estimation of fundamental matrices from noisy data contaminated by outliers. The problem is cast as a series of weighted homogeneous least-squares problems, where robust weights are estimated using deep networks. The presented formulation acts directly on putative correspondences and thus fits into standard 3D vision pipelines that perform feature extraction, matching, and model fitting. The approach can be trained end-to-end and yields computationally efficient robust estimators. Our experiments indicate that the presented approach is able to train robust estimators that outperform classic approaches on real data by a significant margin.


title: Time-optimal UAV trajectory planning for 3D urban structure coverage

abstract: In this paper, we study the time-optimal trajectory planning of a sensor attached to an unmanned aerial vehicle (UAV) to provide complete 3-dimensional coverage with applications to urban environments with 2.5-dimensional features. The basic approach is to approximate the features of interest with a set of non planar coverage surfaces and to design a motion plan that guarantees the coverage surface is swept completely with a conical-field-of-view sensor. We establish a lower bound on time for a UAV to achieve complete coverage and derive the analytical coverage plan whose duration is a constant times this lower bound. Our hardware-in-the-loop simulation results verify the effectiveness of the proposed algorithm.


title: Neural Decision Forests for Semantic Image Labelling

abstract: In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees. Recent advances of deep learning architectures demonstrate the power of embedding representation learning within the classifier--An idea that is intuitively supported by the hierarchical nature of the decision forest model where the input space is typically left unchanged during training and testing. We bridge this gap by introducing randomized Multi- Layer Perceptrons (rMLP) as new split nodes which are capable of learning non-linear, data-specific representations and taking advantage of them by finding optimal predictions for the emerging child nodes. To prevent overfitting, we i) randomly select the image data fed to the input layer, ii) automatically adapt the rMLP topology to meet the complexity of the data arriving at the node and iii) introduce an l1-norm based regularization that additionally sparsifies the network. The key findings in our experiments on three different semantic image labelling datasets are consistently improved results and significantly compressed trees compared to conventional classification trees.


title: From Facial Parts Responses to Face Detection: A Deep Learning Approach

abstract: In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method [23] by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.


title: Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps

abstract: We analyze StirTrace towards benchmarking face morphing forgeries and extending it by additional scaling functions for the face biometrics scenario. We benchmark a Benford's law based multi-compression-anomaly detection approach and acceptance rates of morphs for a face matcher to determine the impact of the processing on the quality of the forgeries. We use 2 different approaches for automatically creating 3940 images of morphed faces. Based on this data set, 86614 images are created using StirTrace. A manual selection of 183 high quality morphs is used to derive tendencies based on the subjective forgery quality. Our results show that the anomaly detection seems to be able to detect anomalies in the morphing regions, the multi-compression-anomaly detection performance after the processing can be differentiated into good (e.g. cropping), partially critical (e.g. rotation) and critical results (e.g. additive noise). The influence of the processing on the biometric matcher is marginal.


title: American Sign Language Translation through Sensory Glove; SignSpeak

abstract: To make a communication bridge, a highly accurate, cost effective and an independent glove was designed for deaf/mute people to enable them to communicate. The glove translates the sign language gestures into speech according to the American Sign Language Standard. The glove contained flex and contact sensors to detect the movements of the fingers and bending of the palm. In addition an accelerometer was built in the glove to measure the acceleration produced by the changing positions of the hand. Principal Component Analysis (PCA) was used to train the glove into recognizing various gestures, and later classify the gestures into alphabets in real time. The glove then established a Bluetooth link with an Android phone, which was used to display the received letters and words and convert the text into speech. The glove was found to have an accuracy of 92%.


title: A Survey on Multi-Task Learning

abstract: Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL. First, we classify different MTL algorithms into several categories: feature learning approach, low-rank approach, task clustering approach, task relation learning approach, dirty approach, multi-level approach and deep learning approach. In order to compare different approaches, we discuss the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, batch MTL models are difficult to handle this situation and online, parallel and distributed MTL models as well as feature hashing are reviewed to reveal the computational and storage advantages. Many real-world applications use MTL to boost their performance and we introduce some representative works. Finally, we present theoretical analyses and discuss several future directions for MTL.


title: Lifeline-based global load balancing

abstract: On shared-memory systems, Cilk-style work-stealing has been used to effectively parallelize irregular task-graph based applications such as Unbalanced Tree Search (UTS). There are two main difficulties in extending this approach to distributed memory. In the shared memory approach, thieves (nodes without work) constantly attempt to asynchronously steal work from randomly chosen victims until they find work. In distributed memory, thieves cannot autonomously steal work from a victim without disrupting its execution. When work is sparse, this results in performance degradation. In essence, a direct extension of traditional work-stealing to distributed memory violates the work-first principle underlying work-stealing. Further, thieves spend useless CPU cycles attacking victims that have no work, resulting in system inefficiencies in multi-programmed contexts. Second, it is non-trivial to detect active distributed termination (detect that programs at all nodes are looking for work, hence there is no work). This problem is well-studied and requires careful design for good performance. Unfortunately, in most existing languages/frameworks, application developers are forced to implement their own distributed termination detection. In this paper, we develop a simple set of ideas that allow work-stealing to be efficiently extended to distributed memory. First, we introduce lifeline graphs: low-degree, low-diameter, fully connected directed graphs. Such graphs can be constructed from k-dimensional hypercubes. When a node is unable to find work after w unsuccessful steals, it quiesces after informing the outgoing edges in its lifeline graph. Quiescent nodes do not disturb other nodes. A quiesced node is reactivated when work arrives from a lifeline and itself shares this work with those of its incoming lifelines that are activated. Termination occurs precisely when computation at all nodes has quiesced. In a language such as X10, such passive distributed termination can be detected automatically using the finish construct -- no application code is necessary. Our design is implemented in a few hundred lines of X10. On the binomial tree described in olivier:08}, the program achieve 87% efficiency on an Infiniband cluster of 1024 Power7 cores, with a peak throughput of 2.37 GNodes/sec. It achieves 87% efficiency on a Blue Gene/P with 2048 processors, and a peak throughput of 0.966 GNodes/s. All numbers are relative to single core sequential performance. This implementation has been refactored into a reusable global load balancing framework. Applications can use this framework to obtain global load balance with minimal code changes. In summary, we claim: (a) the first formulation of UTS that does not involve application level global termination detection, (b) the introduction of lifeline graphs to reduce failed steals (c) the demonstration of simple lifeline graphs based on k-hypercubes, (d) performance with superior efficiency (or the same efficiency but over a wider range) than published results on UTS. In particular, our framework can deliver the same or better performance as an unrestricted random work-stealing implementation, while reducing the number of attempted steals.


title: Modeling and control design for a computer-controlled brake system

abstract: The brake subsystem is one of the most significant parts of a vehicle with respect to safety. A computer-controlled brake system has the capability of acting faster than the human driver during emergencies, and therefore has the potential of improving safety of the vehicles used in the future intelligent transportation systems (ITSs). In this paper we consider the problem of modeling and control of a computer-controlled brake system. The brake model is developed using a series of experiments conducted on a test bench which contains the full scale brake subsystem of a Lincoln town car and a computer-controlled actuator designed by Ford Motor Company. The developed model has the form of a first-order nonlinear system with the system nonlinearities lumped in the model coefficients. The unknown model parameters are identified by applying curve fitting techniques to the experimental data. The major characteristics of the system such as static friction, dead zones, and hysteresis have been identified in terms of model parameters. The brake controller design makes use of a standard feedback linearization technique along with intuitive modifications to meet the closed-loop performance specifications. The simulation results show that the proposed controller guarantees no overshoot and zero steady-state error for step inputs. Test of the same controller using the experimental bench setup demonstrates its effectiveness in meeting the performance requirements.


title: Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics.

abstract: � Abstract—A method for estimating pulse wave velocity (PWV) using circulatory waveform signals derived from multiple photoplethysmograph (PPG) sensors is described. The method employs two wearable in-line PPG sensors placed at a known distance from one another at the ulnar and digital artery. A technique for calibrating the measured pulse wave velocity to arterial blood pressure using hydrostatic pressure variation is presented. Additionally, a framework is described for estimating local arterial dynamics using PPG waveforms and multi-channel blind system ID. Initial results implementing the method on data derived from a human subject at different arterial pressures is presented. Results show that the method is capable of measuring the changes in arterial PWV that result from fluctuations in mean arterial pressure.


title: An Improved Impostors Method for Authorship Verification

abstract: Authorship verification has gained a lot of attention during the last years mainly due to the focus of PAN@CLEF shared tasks. A verification method called Impostors, based on a set of external (impostor) documents and a random subspace ensemble, is one of the most successful approaches. Variations of this method gained top-performing positions in recent PAN evaluation campaigns. In this paper, we propose a modification of the Impostors method that focuses on both appropriate selection of impostor documents and enhanced comparison of impostor documents with the documents under investigation. Our approach achieves competitive performance on PAN corpora, outperforming previous versions of the Impostors method.


title: Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision

abstract: This paper presents a lightweight stereo vision-based driving lane detection and classification system to achieve the ego-car’s lateral positioning and forward collision warning to aid advanced driver assistance systems (ADAS). For lane detection, we design a self-adaptive traffic lanes model in Hough Space with a maximum likelihood angle and dynamic pole detection region of interests (ROIs), which is robust to road bumpiness, lane structure changing while the ego-car’s driving and interferential markings on the ground. What’s more, this model can be improved with geographic information system or electronic map to achieve more accurate results. Besides, the 3-D information acquired by stereo matching is used to generate an obstacle mask to reduce irrelevant objects’ interfere and detect forward collision distance. For lane classification, a convolutional neural network is trained by using manually labeled ROI from KITTI data set to classify the left/right-side line of host lane so that we can provide significant information for lane changing strategy making in ADAS. Quantitative experimental evaluation shows good true positive rate on lane detection and classification with a real-time (15Hz) working speed. Experimental results also demonstrate a certain level of system robustness on variation of the environment.


title: Stress causing Arrhythmia Detection from ECG Signal using HMM

abstract: Electrocardiogram (ECG) is an electrical recording of the heart and is used to measure the rate and regularity ofheartbeats.The cardiac arrhythmias are identified and diagnosed by analyzing the ECG signals. In this paper, the human stress assessment is the major issues taken to identify arrhythmia, where thefeature extraction is done using Discrete Wavelet Transform (DWT) technique for the purpose of analyzing the signals. The DWT technique is used to denoise the ECG signal by removing the corresponding wavelet coefficients and also used to retrieve relevant information from the ECG input signal. The classification of the stress causing arrhythmia from ECG signal is performed by the Hidden Markov Model.


title: Automatic Photo Orientation Detection with Convolutional Neural Networks

abstract: We apply convolutional neural networks (CNN) to the problem of image orientation detection in the context of determining the correct orientation (from 0, 90, 180, and 270 degrees) of a consumer photo. The problem is especially important for digitazing analog photographs. We substantially improve on the published state of the art in terms of the performance on one of the standard datasets, and test our system on a more difficult large dataset of consumer photos. We use Guided Backpropagation to obtain insights into how our CNN detects photo orientation, and to explain its mistakes.


title: Design Considerations of Charge Pump for Antenna Switch Controller With SOI CMOS Technology

abstract: An enhanced charge pump for the antenna switch controller using the silicon-on-insulator (SOI) CMOS technology is presented in this brief. The pseudo cross-coupled technique is proposed to reduce parasitic capacitances at charging/discharging nodes through charge transferring paths, which improves the current drive capability and provides better accuracy of the output voltage. Furthermore, the codesign between the gate control voltages of power MOS transistors and the clock drive signals of pumping capacitors has been investigated to eliminate the reversion loss and reduce the ripple voltage. The pseudo cross-coupled charge pump has been fabricated in the 0.18- $\mu\text{m}$ SOI CMOS technology with an area of 0.065 mm2. According to the comparison results of the conventional and enhanced charge pumps, the start-up time and the recovery time are typically shortened by 71.4% and 21.7%, owing to the improvement of the current drive capability, and the ripple voltage at no-load condition is greatly reduced by 46.1%.


title: Introducing the use of depth data for fall detection

abstract: Current emergency systems for elderly contain at least one sensor (button or accelerometer), which has to be worn or pressed in case of emergency. If elderly fall and loose their consciousness, they are not able to press the button anymore. Therefore, autonomous systems to detect falls without wearing any devices are needed. This paper presents three different non-invasive technologies: the use of audio, 2D sensors (cameras) and introduces a new technology for fall detection: the Kinect as 3D depth sensor. Our fall detection algorithms using the Kinect are evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living. The evaluation results are compared with State-of-the-Art approaches using 2D sensors or microphones.


title: Context Aware Computing for The Internet of Things: A Survey

abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.


title: A Review Of Methods For Encoding Neural Network Topologies In Evolutionary Computation.

abstract: This paper describes various methods used to encode artificial neural networks to chromosomes to be used in evolutionary computation. The target of this review is to cover the main techniques of network encoding and make it easier to choose one when implementing a custom evolutionary algorithm for finding the network topology. Most of the encoding methods are mentioned in the context of neural networks; however all of them could be generalized to automata networks or even oriented graphs. We present direct and indirect encoding methods, and given examples of their genotypes. We also describe the possibilities of applying genetic operators of mutation and crossover to genotypes encoded by these methods. Also, the dependencies of using special evolutionary algorithms with some of the encodings were considered.


title: Fashion World Map: Understanding Cities Through Streetwear Fashion

abstract: Fashion is an integral part of life. Streets as a social center for people's interaction become the most important public stage to showcase the fashion culture of a metropolitan area. In this paper, therefore, we propose a novel framework based on deep neural networks (DNN) for depicting the street fashion of a city by automatically discovering fashion items (e.g., jackets) in a particular look that are most iconic for the city, directly from a large collection of geo-tagged street fashion photos. To obtain a reasonable collection of iconic items, our task is formulated as the prize-collecting Steiner tree (PCST) problem, whereby a visually intuitive summary of the world's iconic street fashion can be created. To the best of our knowledge, this is the first work devoted to investigate the world's fashion landscape in modern times through the visual analytics of big social data. It shows how the visual impression of local fashion cultures across the world can be depicted, modeled, analyzed, compared, and exploited. In the experiments, our approach achieves the best performance (43.19%) on our large collected GSFashion dataset (170K photos), with an average of two times higher than all the other algorithms (FII: 20.13%, AP: 18.76%, DC: 17.90%), in terms of the users' agreement ratio on the discovered iconic fashion items of a city. The potential of our proposed framework for advanced sociological understanding is also demonstrated via practical applications.