title: Rya: a scalable RDF triple store for the clouds

abstract: Resource Description Framework (RDF) was designed with the initial goal of developing metadata for the Internet. While the Internet is a conglomeration of many interconnected networks and computers, most of today's best RDF storage solutions are confined to a single node. Working on a single node has significant scalability issues, especially considering the magnitude of modern day data. In this paper we introduce a scalable RDF data management system that uses Accumulo, a Google Bigtable variant. We introduce storage methods, indexing schemes, and query processing techniques that scale to billions of triples across multiple nodes, while providing fast and easy access to the data through conventional query mechanisms such as SPARQL. Our performance evaluation shows that in most cases, our system outperforms existing distributed RDF solutions, even systems much more complex than ours.


title: Interactions between surface water and groundwater in Chabagou Catchment using hydrogen and oxygen isotopes

abstract: Loess Plateau is of great significance to the hydrology in the Yellow River Basin, and interactions between surface water and groundwater at Loess Plateau, especially at the Hilly and Gully Region, are important preconditions for water resources evaluation and ecological restoration. For this aim, Chabagou Catchment in the Hilly and Gully Region was chosen to study interactions between the two by integrating hydrogen and oxygen isotopes and hydrochemistry. In Chabagou Catchment, precipitation, surface water and groundwater were observed and sampled systematically; two trips of instantaneous sampling, including surface water and groundwater, were performed in June and August of 2005, with electrical conductivity (EC) and pH values tested in situ. The isotopic compositions and EC of surface water varied greatly and had different trends along the stream. The isotopic compositions of surface runoff concentrated along the mainstream. EC increased in June, but in August, it decreased from upper to middle reaches and then increased at the lower reaches. Variation of isotopic compositions in groundwater was small while it was large for EC. According to the isotopic and hydrochemical information of instantaneous sampling, relationships between surface water and groundwater has been identified. In dry season, low flow surface water is recharged by contact spring in fractured bedrock, and evaporates severely during flow. In rainy season, peak runoff recedes quickly and only groundwater adjacent to the mainstream or gullies can get recharge during flood; when pea runoff recedes, surface runoff mainly gets recharge from spring water and surface water held up by silt arresters at the upper reaches; increasing recharge of contact springs and runoff in branches reduces EC of surface water at the middle reaches; while at the lower reaches, recharge from spring and branches decreases and surface water gets severe evaporation along the mainstream. No matter in dry season or in rainy season when peak runoff recedes, surface water experiences serious evaporation, and only phreatic aquifers near mainstream can get recharge from surface runoff and experiences severe groundwater evaporation. Silt arresters can obviously hold up surface runoff and increase the adjacent groundwater and baseflow. The enhanced evaporation caused by silt arresters may concentrate the total dissolved solid of surface water at upper layers, while its influence on groundwater quality of groundwater is not apparent yet and calls for further study.


title: Health-related quality of life after short segment instrumentation of lumbar burst fractures.

abstract: Management of lumbar burst fractures remains controversial. Surgical reduction/stabilization is becoming more popular; however, the functional impact of operative intervention is not clear. The purpose of this study was to assess health-related quality of life and functional outcome after posterior fixation of lumbar burst fractures with either posterolateral or intrabody bone grafting. Twenty-four subjects were included. Radiographs and computed tomography scans were evaluated for deformity (kyphosis, vertebral compression, lateral angulation, lateral body height, and canal compromise) postoperatively, at 1 year, and at final follow-up (mean 3.2 years). Patients completed the SF 36 Health Survey and the Oswestry Low Back Pain Disability Questionnaire at final follow-up. Significant improvement was noted in midsagittal diameter compromise, vertebral compression, and kyphosis. The difference observed between the respondents mean scores on the SF 36 was not significantly different from those presented as the U.S. national average (p = 0.053). Data from the Oswestry questionnaire indicated a similarly high level of function. Overall, we found posterior spinal instrumentation to correlate with positive functional outcome based on both general health (SF 36) and joint-specific outcome scales (Oswestry). Posterior instrumentation provides sound canal decompression, kyphotic reduction, and maintains vertebral height with minimal transgression and long-term sequelae. In cases of severe initial deformity and neurologic compromise, intrabody bone grafting is most certainly indicated; the additional support provided by a posterolateral graft may also prove beneficial as an adjunct.


title: Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis

abstract: Long short-term memory recurrent neural networks (LSTM-RNNs) have been applied to various speech applications including acoustic modeling for statistical parametric speech synthesis. One of the concerns for applying them to text-to-speech applications is its effect on latency. To address this concern, this paper proposes a low-latency, streaming speech synthesis architecture using unidirectional LSTM-RNNs with a recurrent output layer. The use of unidirectional RNN architecture allows frame-synchronous streaming inference of output acoustic features given input linguistic features. The recurrent output layer further encourages smooth transition between acoustic features at consecutive frames. Experimental results in subjective listening tests show that the proposed architecture can synthesize natural sounding speech without requiring utterance-level batch processing.


title: Dual Linear Regression Based Classification for Face Cluster Recognition

abstract: We are dealing with the face cluster recognition problem where there are multiple images per subject in both gallery and probe sets. It is never guaranteed to have a clear spatio-temporal relation among the multiple images of each subject. Considering that the image vectors of each subject, either in gallery or in probe, span a subspace, an algorithm, Dual Linear Regression Classification (DLRC), for the face cluster recognition problem is developed where the distance between two subspaces is defined as the similarity value between a gallery subject and a probe subject. DLRC attempts to find a "virtual" face image located in the intersection of the subspaces spanning from both clusters of face images. The "distance" between the "virtual" face images reconstructed from both subspaces is then taken as the distance between these two subspaces. We further prove that such distance can be formulated under a single linear regression model where we indeed can find the "distance" without reconstructing the "virtual" face images. Extensive experimental evaluations demonstrated the effectiveness of DLRC algorithm compared to other algorithms.


title: Single-Feed Circularly Polarized Aperture-Coupled Stack Antenna With Dual-Mode Square Loop Radiator

abstract: The authors propose a compact single-feed stack antenna consisting of a square loop radiator with perturbation, an aperture-coupled structure, and a straight-strip feed line for circular polarization (CP) and unidirectional radiation applications. This perturbation applies both dual-mode and orthogonal-mode effects simultaneously in the square loop resonator to present bandwidth and CP characteristics. The stack antenna presents the desired axial ratio (AR) bands of 2.42 GHz with 3-dB bandwidth (BW) = 62 MHz (2.5%). The circular polarization is demonstrated with AR spectrum and orthogonal modes distributions. The proposed antenna is successfully simulated and measured with frequency responses, radiation patterns, and current distributions.


title: Social Competence Intervention for Youth with Asperger Syndrome and High-functioning Autism: An Initial Investigation

abstract: Individuals with high functioning autism (HFA) or Asperger Syndrome (AS) exhibit difficulties in the knowledge or correct performance of social skills. This subgroup’s social difficulties appear to be associated with deficits in three social cognition processes: theory of mind, emotion recognition and executive functioning. The current study outlines the development and initial administration of the group-based Social Competence Intervention (SCI), which targeted these deficits using cognitive behavioral principles. Across 27 students age 11–14 with a HFA/AS diagnosis, results indicated significant improvement on parent reports of social skills and executive functioning. Participants evidenced significant growth on direct assessments measuring facial expression recognition, theory of mind and problem solving. SCI appears promising, however, larger samples and application in naturalistic settings are warranted.


title: Mobile product image search by automatic query object extraction

abstract: Mobile product image search aims at identifying a product, or retrieving similar products from a database based on a photo captured from a mobile phone camera. Application of traditional image retrieval methods (e.g. bag-of-words) to mobile visual search has been shown to be effective in identifying duplicate/near-duplicate photos, near-planar and textured objects such as landmarks, books/cd covers. However, retrieving more general product categories is still a challenging research problem due to variations in viewpoint, illumination, scale, the existence of blur and background clutter in the query image, etc. In this paper, we propose a new approach that can simultaneously extract the product instance from the query, identify the instance, and retrieve visually similar product images. Based on the observation that good query segmentation helps improve retrieval accuracy and good search results provide good priors for segmentation, we formulate our approach in an iterative scheme to improve both query segmentation and retrieval accuracy. To this end, a weighted object mask voting algorithm is proposed based on a spatially-constrained model, which allows robust localization and segmentation of the query object, and achieves significantly better retrieval accuracy than previous methods. We show the effectiveness of our approach by applying it to a large, real-world product image dataset and a new object category dataset.


title: DCASE 2017 Challenge setup: Tasks, datasets and baseline system

abstract: DCASE 2017 Challenge consists of four tasks: acoustic scene classification , detection of rare sound events, sound event detection in real-life audio, and large-scale weakly supervised sound event detection for smart cars. This paper presents the setup of these tasks: task definition, dataset, experimental setup, and baseline system results on the development dataset. The baseline systems for all tasks rely on the same implementation using multilayer perceptron and log mel-energies, but differ in the structure of the output layer and the decision making process, as well as the evaluation of system output using task specific metrics.


title: SlashBurn: Graph Compression and Mining beyond Caveman Communities

abstract: Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the ‘cavemen graph’, and compress them. We show that the block-diagonal mental image of the ‘cavemen graph’ is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the SlashBurn method to recursively split a graph into hubs and spokes connected only by the hubs. We also propose techniques to select the hubs and give an ordering to the spokes, in addition to the basic SlashBurn. We give theoretical analysis of the proposed hub selection methods. Our view point has several advantages: (a) it avoids the ‘no good cuts’ problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Through experiments, we show that SlashBurn consistently outperforms other methods for all data sets, resulting in better compression and faster running time. Moreover, we show that SlashBurn with the appropriate spokes ordering can further improve compression while hardly sacrificing the running time.


title: Male secretory breast cancer: case in a 6-year-old boy with a peculiar gene duplication and review of the literature

abstract: Secretory breast cancer (SBC) is one of the rarest breast cancer (BC), representing the majority of BC in childhood. Nevertheless, it elicits a lot of interest both for the peculiar morphology and the characteristic genetic features. Currently, there is no consensus on optimal treatment strategy. Therefore, it is useful to report every case in order to establish treatment algorithms. We describe the case of a 6-year-old boy diagnosed with a SBC, with peculiar genomic and immunohistochemical features. Moreover, we carried out a review of the literature in order to analyze the present state of knowledge about this rare entity. To the best of our knowledge, there are only 120 cases published in literature, only 32 in males and only 2 younger than 6 years. Furthermore, this one had peculiar genomic and immunohistochemical features. Indeed, even if SBC expresses basal-cell markers, our patient had a triple-negative tumor expressing both basal and luminal cell markers. Furthermore, the boy’s genomic profile revealed not only positivity for the typical SBC’s translocation t(12;15), but also for a 3q28 duplication, found in his father (healthy) and paternal grandfather (with a previous BC). None were positive for BRCA mutation. This locus includes only one gene encoding for a growth factor recently linked to Early Infantile Epileptic Encephalopathy-47 and Idiopathic ventricular tachycardia. Even if the literature does not provide evidence of a pathogenic role it is not possible to exclude a cancer-predisposing activity. SBC is a rare type of BC, characterized by triple-negative features with an unexpectedly good prognosis. More data are needed to fully understand the behavior of this cancer and genomic profiling could be helpful in improving its diagnosis and management.


title: Realtime DDoS Defense Using COTS SDN Switches via Adaptive Correlation Analysis

abstract: Distributed denial-of-service (DDoS) defense is still a difficult problem though it has been extensively studied. The existing approaches are not capable of detecting various types of DDoS attacks. In particular, new emerging sophisticated DDoS attacks (e.g., Crossfire) constructed by low-rate and short-lived benign traffic are even more challenging to capture. Moreover, it is difficult to enforce realtime defense to throttle these detected attacks since the attack traffic can be concealed in benign traffic. Software defined networking (SDN) opens a new door to address these issues. In this paper, we propose Reinforcing Anti-DDoS Actions in Realtime (RADAR) to detect and throttle DDoS attacks via adaptive correlation analysis built upon unmodified commercial off-the-shelf SDN switches. It is a practical system to defend against a wide range of flooding-based DDoS attacks, e.g., link flooding (including Crossfire), SYN flooding, and UDP-based amplification attacks, while requiring neither modifications in SDN switches/protocols nor extra appliances. It accurately detects attacks by identifying attack features in suspicious flows, and locates attackers (or victims) to throttle the attack traffic by adaptive correlation analysis. We implement RADAR prototype using open source Floodlight controller, and evaluate its performance under various DDoS attacks by real hardware testbed based experiments. We observe that our scheme can successfully detect and effectively defend against various DDoS attacks with acceptable overhead.


title: Low power dissipation MOS ternary logic family

abstract: An MOS ternary-logic family comprising a set of inverters, NOR gates, and NAND gates is proposed. These gates are used to design basic ternary arithmetic and memory circuits. The circuits thus obtained are then used to synthesize complex ternary arithmetic circuits and shift registers. The ternary circuits developed are shown to have some significant advantages relative to other known ternary circuits; these include low power dissipation and reduced propagation delay and component count. For a given dynamic range, the complexity of the new ternary circuits is shown to be comparable to that of corresponding binary circuits. Nevertheless, the associated reduction in the wordlength in the case of the ternary circuits tends to alleviate to a large extent the pin limitation problem associated with VLSI implementation. The authors conclude with an implementation of the cyclic convolution, an application in which a significant advantage can be gained through the use of ternary digital hardware.


title: Automatic diagnosis and response to memory corruption vulnerabilities

abstract: Cyber attacks against networked computers have become relentless in recent years. The most common attack method is to exploit memory corruption vulnerabilities such as buffer overflow and format string bugs. This paper presents a technique to automatically identify both known and unknown memory corruption vulnerabilities. Based on the observation that a randomized program usually crashes upon a memory corruption attack, this technique uses the crash as a trigger to initiate an automatic diagnosis algorithm. The output of the diagnosis includes the instruction that is tricked to corrupt data, the call stack at the time of corruption, and the propagation history of corrupted data. These results provide useful information in fixing the vulnerabilities. Moreover, the diagnosis process also generates a signature of the attack using data/address values embedded in the malicious input message, and is used to block future attacks. Such a signature is further associated with the program execution state to reduce false positives without decreasing the detection rate. The proposed techniques enable the development of a decentralized self-diagnosing and self-protecting defense mechanism for networked computers. We report the implementation experience and experimental evaluation of a prototype system on Linux.


title: High-efficiency step-down converter via coupled inductor

abstract: In this study, a high-efficiency single-input multiple-output (SIMO) step-down converter is designed. The coupled-inductor-based SIMO step-down converter utilizes two power switches with the properties of voltage clamping for the middle-voltage switch, and soft switching for all power switches due to the appropriately choice of the corresponding device specifications. As a result, the leakage inductor energy of the coupled inductor can be recycled, and the voltage spikes on power switches can be alleviated. Moreover, the switching losses can be significantly decreased because of all power switches with zero-voltage-switching (ZVS) features. Therefore, the objectives of high-efficiency power conversion, high step-down ratio, and various output voltage with different levels can be obtained. The effectiveness of the proposed SIMO step-down converter is verified by experimental results of a converter prototype in practical applications.


title: A Hough transform-based voting framework for action recognition

abstract: We present a method to classify and localize human actions in video using a Hough transform voting framework. Random trees are trained to learn a mapping between densely-sampled feature patches and their corresponding votes in a spatio-temporal-action Hough space. The leaves of the trees form a discriminative multi-class codebook that share features between the action classes and vote for action centers in a probabilistic manner. Using low-level features such as gradients and optical flow, we demonstrate that Hough-voting can achieve state-of-the-art performance on several datasets covering a wide range of action-recognition scenarios.


title: Impact of Information Technology Management Practices on Customer Service

abstract: Recently,despite huge incentives and subsequent increases in investment in customer relationship management technology, many firms have not been able to increase their customer satisfaction index ratings. The purpose of this paper is to gauge whether IT management practices differ among firms where IT has a major role in transforming marketing, operations, or both, which give the firms advantage by affecting their customer service. Several research hypotheses are tested using data obtained from a survey of 213 IT-leaders in the financial services industry. The results clearly indicate that the IT-leader firms have a higher level of IT management sophistication and a higher role for their IT-leaders compared to IT-enabled customer focus, IT-enabled operations focus, and IT-laggard firms. This paper concludes with the implications for both researchers and practitioners.


title: A modified error function for imbalanced dataset classification problem

abstract: The objective of learning is to achieve the least error rate. In this paper we proposed a modified cost function as a means to properly measure error rate for imbalanced dataset. Most cost functions apply the same weights to all classes. However, it has been known that for imbalanced problem, the number of instances in the majority class is larger than the minority class. Therefore, the application of equal weight to all classes will significantly lead to improper classification boundary. That is, for most learning model, the minority class would be dominated by majority class which then causes a misclassification on the minority class. The objective of this paper is to find the appropriate parameters to improve MSE cost function based on overlap ratio and class distribution ratio. Back-propagation algorithm with the proposed modified cost function is used to solve two-class classification problem. UCI datasets are used for the experimentation. The results show that the modified MSE cost function provides a better result than the standard one, based on True-positive rate, G-Mean, and F-measurement.


title: Star image noise filtering based on regularization influence function diffusion model

abstract: As noise filtering of a star image has a high demand for reserving details of star edge,a new star map noise filtering method on a regularization influence function diffusion model was proposed based on Tukey diffusion model and modified Perona-Malik model.The boundary point set was extracted by a derivative operator and the map noise was processed by filtering with the space distribution characteristics of the original pixel and the noise pixel in the images.Moreover,the image edge was recovered by agiven boundary condition.Due to avoiding Variance Stabilization(VS)transform,it could process the Gaussian noise directly.Simulation experiments on a common image and a star map with Gaussian noise show that this method has good capability of noise filtering and can effectively reserve the edges of feature images.Compared with common diffusion function algorithm,the average error is reduced by 13.6%and the Peak Signal to Noise Ratio(PSNR)is improved by 6.1%.Filtering performance of the proposed method is better than that of common diffusion function method,especially suitable for noise filtering of star maps.


title: Genetic evaluation of suspected osteogenesis imperfecta (OI)

abstract: Disclaimer: This guideline is designed primarily as an educational resource for medical geneticists and other health care providers to help them provide quality medical genetic services. Adherence to this guideline does not necessarily assure a successful medical outcome. This guideline should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed to obtaining the same results. In determining the propriety of any specific procedure or test, the geneticist should apply his or her own professional judgment to the specific clinical circumstances presented by the individual patient or specimen. It may be prudent, however, to document in the patient's record the rationale for any significant deviation from this guideline.


title: Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation

abstract: The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences.


title: Physically based modeling and animation of fire

abstract: We present a physically based method for modeling and animating fire. Our method is suitable for both smooth (laminar) and turbulent flames, and it can be used to animate the burning of either solid or gas fuels. We use the incompressible Navier-Stokes equations to independently model both vaporized fuel and hot gaseous products. We develop a physically based model for the expansion that takes place when a vaporized fuel reacts to form hot gaseous products, and a related model for the similar expansion that takes place when a solid fuel is vaporized into a gaseous state. The hot gaseous products, smoke and soot rise under the influence of buoyancy and are rendered using a blackbody radiation model. We also model and render the blue core that results from radicals in the chemical reaction zone where fuel is converted into products. Our method allows the fire and smoke to interact with objects, and flammable objects can catch on fire.


title: Influentials, networks, and public opinion formation

abstract: A central idea in marketing and diffusion research is that influentials—a minority of individuals who influence an exceptional number of their peers—are important to the formation of public opinion. Here we examine this idea, which we call the “influentials hypothesis,” using a series of computer simulations of interpersonal influence processes. Under most conditions that we consider, we find that large cascades of influence are driven not by influentials but by a critical mass of easily influenced individuals. Although our results do not exclude the possibility that influentials can be important, they suggest that the influentials hypothesis requires more careful specification and testing than it has received.


title: Automatic Generation of XSS and SQL Injection Attacks with Goal-Directed Model Checking

abstract: Cross-site scripting (XSS) and SQL injection errors are two prominent examples of taint-based vulnerabilities that have been responsible for a large number of security breaches in recent years. This paper presents QED, a goal-directed model-checking system that automatically generates attacks exploiting taint-based vulnerabilities in large Java web applications. This is the first time where model checking has been used successfully on real-life Java programs to create attack sequences that consist of multiple HTTP requests. ::: ::: QED accepts any Java web application that is written to the standard servlet specification. The analyst specifies the vulnerability of interest in a specification that looks like a Java code fragment, along with a range of values for form parameters. QED then generates a goal-directed analysis from the specification to perform session-aware tests, optimizes to eliminate inputs that are not of interest, and feeds the remainder to a model checker. The checker will systematically explore the remaining state space and report example attacks if the vulnerability specification is matched. ::: ::: QED provides better results than traditional analyses because it does not generate any false positive warnings. It proves the existence of errors by providing an example attack and a program trace showing how the code is compromised. Past experience suggests this is important because it makes it easy for the application maintainer to recognize the errors and to make the necessary fixes. In addition, for a class of applications, QED can guarantee that it has found all the potential bugs in the program. We have run QED over 3 Java web applications totaling 130,000 lines of code. We found 10 SQL injections and 13 cross-site scripting errors.


A Spiking Neuron Model of Word Associations for the Remote Associates Test


title: Vectorizing Database Column Scans with Complex Predicates

abstract: The performance of the full table scan is critical for the overall performance of column-store database systems such as the SAP HANA database. Compressing the underlying column data format is both an advantage and a challenge, because it reduces the data volume involved in a scan on one hand and introduces the need for decompression during the scan on the other hand. In previous work [26] we have shown how to accelerate the column-scan with range predicates using SIMD instructions. In this paper, we present a framework for vectorized scans with more complex predicates. One important building block is the In-List predicate, where all rows whose values are contained in a given list of values are selected. While this seems to exhibit only little data parallelism on first sight, we show that a performant vectorized implementation is possible using the new Intel AVX2 instruction set. We also improve our previous algorithms by leveraging the increased vector-width. Finally in a detailed performance evaluation, we show the benefit of these optimizations and of the new instruction set: in almost all cases our scans needs less than one CPU cycle per row including scans with In-List predicate, leading to an overall throughput of 8 billion rows per second and more on a single core.


title: Time perception, phonological skills and executive function in children with dyslexia and/or ADHD symptoms

abstract: Dyslexia and attention-deficit/hyperactivity disorder (ADHD) are both common childhood disorders. It is widely accepted that the proximal cognitive cause of dyslexia is a phonological deficit (Vellutino, Fletcher, Snowling, & Scanlon, 2004) whereas the predominant account of ADHD sees it as arising from an impairment in executive functions that affects both cognitive and motivational systems (Barkley, 1997). The frequent comorbidity of reading and attention difficulties raises the issue of whether common causal mechanisms, at either the cognitive or biological levels, may be involved (Light, Pennington, Gilger, & DeFries, 1995; Stevenson et al., 2005). ::: ::: There are a number of competing explanations of the comorbidity between dyslexia and ADHD. In one of the first papers to address this issue, Pennington, Groisser, and Welch (1993) proposed that the symptoms of ADHD associated with dyslexia are a secondary consequence of reading problems (‘phenocopy’ hypothesis). However, later studies failed to support this hypothesis (e.g., Willcutt et al., 2001; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005), instead reporting that comorbid dyslexia+ADHD is associated with a combination of the cognitive impairments seen in dyslexia and ADHD alone (Adams & Snowling, 2001; Nigg, Hinshaw, Carte, & Treuting, 1998; Raberger & Wimmer, 2003; Rucklidge & Tannock, 2002; Willcutt et al., 2005). ::: ::: A different view of the aetiology of comorbid dyslexia+ADHD is that the condition arises from shared genetic risk factors that contribute to the development of separate cognitive impairments that underlie the two disorders. In this view, those same genetic risk factors (acting in concert with other genetic and environmental risk factors) may lead to the development of both underlying cognitive impairments, in turn producing the comorbid condition (‘shared aetiology’ hypothesis; de Jong, Oosterlaan, & Sergeant, 2006). Arguably, the pattern of findings just described (that dyslexia-only and ADHD-only are associated with different patterns of cognitive impairment but both forms of impairment co-occur in the comorbid condition) is consistent with this hypothesis. ::: ::: Finally, it has occasionally been argued that comorbid dyslexia+ADHD may reflect different causal mechanisms from those operating in either condition alone (‘cognitive subtype’ hypothesis, de Jong et al., 2006). Findings that children with dyslexia+ADHD show more severe impairments in phonological skills, inhibition (Willcutt et al., 2001), rapid automatised naming (Rucklidge & Tannock, 2002) and time estimation (McGee, Brodeur, Symons, Andrade, & Fahie, 2004) than children with either disorder alone have been interpreted as supporting this hypothesis. ::: ::: There is currently growing interest in the search for the ‘endophenotypes’ underlying developmental disorders (Skuse, 2001). Endophenotypes can be defined as heritable processes, intermediate between the genotype and the behavioural phenotype that reflect an underlying liability for a disorder (Almasy & Blangero, 2001), which may combine with additional risk factors to lead to a clinical diagnosis (Bishop, 2006). On the basis of findings from a family risk study of dyslexia, Snowling (2008) proposed that phonological deficits may be better conceptualised as an endophenotype of dyslexia rather than a marker of reading difficulties per se, because some individuals with phonological deficits can compensate for these to become competent readers. ::: ::: Castellanos and Tannock (2002) discuss a number of putative cognitive endophenotypes for ADHD, including deficits in inhibition, sustained attention, response variability, working memory and temporal processing. They suggest that temporal processing deficits may be causally related to deficits in time perception (see Toplak, Dockstader, & Tannock, 2006) and response variability (e.g., Kunsti & Stevenson, 2001) seen in ADHD. They also suggest that temporal processing deficits may underpin the deficits in time perception (Nicolson, Fawcett, & Dean, 1995) and phoneme awareness associated with dyslexia (although this remains controversial (Nittrouer, 1999; Marshall, Snowling, & Bailey, 2001)). According to this hypothesis, deficits in temporal processing may be an endophenotype of both ADHD and RD that could explain the frequent comorbidity between these disorders. ::: ::: The present study aimed to clarify the comorbidity between dyslexia and ADHD by investigating the possible cognitive endophenotypes of these disorders in children with dyslexia and/or ADHD symptoms (AS). Children’s performance on measures of phonological skills, executive function and time perception were examined. According to the ‘phenocopy’ hypothesis, children with dyslexia+AS will only exhibit the cognitive deficits associated with dyslexia since their AS arise as a secondary consequence of reading problems. In contrast, the ‘cognitive subtype’ hypothesis suggests that the impairments observed in dyslexia+AS should be more severe than those observed in children with either dyslexia or AS alone. The ‘shared aetiology’ hypothesis suggests that children with dyslexia-only and AS-only will show distinct patterns of cognitive impairments and both forms of impairment will co-occur in the comorbid condition. Alternatively, if it could be demonstrated that deficits in time perception are associated with both dyslexia and AS, this would provide some evidence for a shared cognitive endophenotype underlying both conditions. Such a pattern would, in turn, give a possible explanation for how shared genetic risk factors might operate to cause these disorders and the comorbidity between them.


title: Information infrastructure: an actor-network perspective

abstract: Recent work on information systems has discussed the nature and the complexity of the Information Infrastructures (II) concept. This research has mainly focused on two aspects: studying the process that both shapes and stabilizes information infrastructures, and studying the role played by information infrastructure in leveraging business performance. Using the ideas proposed by the Actor Network Theory (ANT), this article suggests a new way to conceptualise the nature of II, that is, its ontology. Using ANT as an ontological foundation to analyse the relations among actors, the article proposes the concept of information infrastructures in action to highlight their dynamic nature. This leads us to consider information infrastructures not as stable entities, but rather as entities performed in, by, and through relations. The aim of this work is to overcome the limitations associated with studying information infrastructures that rely on stability and manageability assumptions. Conceiving information infrastructures in terms of performative forces that evolve dynamically, this work provides a framework to examine information infrastructure in terms of dynamic relationships by looking at the process that shapes these relationships. The article suggests that information infrastructures should not be studied retrospectively to understand how they are established, but rather should be studied focussing on the process of making. Here we study the action of making rather than the processes that made.


title: DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation

abstract: Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of performing de-novo molecular design. Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks.


title: Solving Square Jigsaw Puzzles Using Dynamic Programming and the Hungarian Procedure

abstract: Problem statement: To design, implement, and test an algorithm for solving the square jigsaw puzzle problem, which has many applications in image processing, pattern recognition, and computer vision such as restoration of archeological artifacts and image descrambling. Approach: The algorithm used the gray level profiles of border pixels for local matching of the puzzle pieces, which was performed using dynamic programming to facilitate non-rigid alignment of pixels of two gray level profiles. Unlike the classical best-first search, the algorithm simultaneously located the neighbors of a puzzle piece during the search using the well-known Hungarian procedure, which is an optimal assignment procedure. To improve the search for a global solution, every puzzle piece was considered as starting piece at various starting locations. Results: Experiments using four well-known images demonstrated the effectiveness of the proposed approach over the classical piece-by-piece matching approach. The performance evaluation was based on a new precision performance measure. For all four test images, the proposed algorithm achieved 100% precision rate for puzzles up to 8×8. Conclusion: The proposed search mechanism based on simultaneous allocation of puzzle pieces using the Hungarian procedure provided better performance than piece-by-piece used in classical methods.


title: The influence of persuasion, training and experience on user perceptions and acceptance of IT innovation

abstract: Information technology (IT) cannot produce any positive outcome unless it is adopted and used. Theories and empirical research suggest that IT adoption and usage are determined by user beliefs and attitudes toward IT. However, little is known about what factors affect the formation and change over time of user beliefs and attitudes. It is critical to understand such factors so that effective managerial interventions can be created and implemented to positively influence user acceptance and use of IT innovations. Based on theories of innovation diffusion, information technology adoption, and persuasion, this study investigates the effect of persuasion, training, and direct-use experience on the formation and change over time of user perceptions and adoption decisions of IT innovation. The results of a longitudinal experimental study show that persuasion significantly affects the formation of users’ initial perceptions, attitude toward, and intention to adopt IT. Training provided in the introduction stage of IT innovation helps the user form a more realistic expectation. As users’ direct-use experience with IT innovation increases over time, their perceptions and adoption intentions change substantially. The results suggest that persuasion, training, and direct-use experience are important variables that need to be considered in IT innovation and adoption research and


title: Media Literacy and the Challenge of New Information and Communication Technologies. The Communication Review(7

abstract: Within both academic and policy discourses, the concept of media literacy is being extended from its traditional focus on print and audiovisual media to encompass the internet and other new media. The present article addresses three central questions currently facing the public, policy-makers and academy: What is media literacy? How is it changing? And what are the uses of literacy? The article begins with a definition: media literacy is the ability to access, analyse, evaluate and create messages across a variety of contexts. This four-component model is then examined for its applicability to the internet. Having advocated this skills-based approach to media literacy in relation to the internet, the article identifies some outstanding issues for new media literacy crucial to any policy of promoting media literacy among the population. The outcome is to extend our understanding of media literacy so as to encompass the historically and culturally conditioned relationship among three processes: (i) the symb...


CHAPTER 5 SOFTWARE TESTING


title: Perspectives on Cognitive Neuroscience

abstract: How is it that we can perceive, learn and be aware of the world? The development of new techniques for studying large-scale brain activity, together with insights from computational modeling and a better understanding of cognitive processes, have opened the door for collaborative research that could lead to major advances in our understanding of ourselves.


title: The neural time course of art perception: An ERP study on the processing of style versus content in art

abstract: Abstract A central prerequisite to understand the phenomenon of art in psychological terms is to investigate the nature of the underlying perceptual and cognitive processes. Building on a study by Augustin, Leder, Hutzler, and Carbon (2008) the current ERP study examined the neural time course of two central aspects of representational art, one of which is closely related to object- and scene perception, the other of which is art-specific: content and style. We adapted a paradigm that has repeatedly been employed in psycholinguistics and that allows one to examine the neural time course of two processes in terms of when sufficient information is available to allow successful classification. Twenty-two participants viewed pictures that systematically varied in style and content and conducted a combined go/nogo dual choice task. The dependent variables of interest were the Lateralised Readiness Potential (LRP) and the N200 effect. Analyses of both measures support the notion that in the processing of art style follows content, with style-related information being available at around 224 ms or between 40 and 94 ms later than content-related information. The paradigm used here offers a promising approach to further explore the time course of art perception, thus helping to unravel the perceptual and cognitive processes that underlie the phenomenon of art and the fascination it exerts.


title: Millimeter-Wave Transition From Waveguide to Two Microstrip Lines Using Rectangular Patch Element

abstract: A millimeter-wave transition from a waveguide to two microstrip lines and the design methodology are proposed. A rectangular patch element in a short-terminated waveguide is analyzed by the cavity model of a patch antenna and the dyadic Green's function of the waveguide. The analysis points out that the rectangular patch element has an optimum width for wideband, which only has the function of the broad wall width of the waveguide. The transition also works as a divider. A numerical investigation of a transition having two microstrip lines validates the analytical model in terms of wideband, and indicates that the distance between the two microstrip lines has an optimum length for suppressing higher order modes. A prototype transition exhibits an insertion loss of 0.5 dB from 76 to 77 GHz, and a bandwidth of 6.9% (5.29 GHz) for the reflection coefficient below -15 dB for the waveguide port


title: Structural reliability analysis using Monte Carlo simulation and neural networks

abstract: This paper examines a methodology for computing the probability of structural failure by combining neural networks (NN) and Monte Carlo simulation (MCS). MCS is a powerful tool, simple to implement and capable of solving a broad range of reliability problems. However, its use for evaluation of very low probabilities of failure implies a great number of structural analyses, which can become excessively time consuming. The proposed methodology makes use of the capability of a NN to approximate a function for reproducing structural behavior, allowing the computation of performance measures at a much lower cost. This approach seems very attractive, and its main challenge lies in the ability of a NN to approximate accurately complex structural response. In order to assess the validity of this methodology, a test function and two structural examples are presented and discussed. The second example is also used to show how this methodology can be used to perform reliability-based structural optimization.


title: Title: A Risk-Metric Framework for Enterprise Risk Management

abstract: We describe a risk-metric framework that supports enterprise risk management. At the core of the framework is the notion of a risk profile that provides risk measurement for risk elements. By providing a generic template in which metrics can be codified in terms of metric space operators, risk profiles can be used to construct a variety of risk measures for different business contexts. These measures can vary from conventional economic risk calculations to the kinds of metrics that are used by decision support systems, such as those supporting inexact reasoning and that are considered to closely match how humans combine information.


title: Crude oil price forecasting using XGBoost

abstract: One of the most important role of economic variables in today's world countries are the price and the change of the price of crude oil. Changes in the price of crude oil have a very critical role in terms of treasury and budget, both in company and state planning. For example, one may choose one of the energy or natural gas indexed energy production plans based on the trend of the crude oil price, for planning to meet the need for electricity next year. Accurate forecasting of the crude oil price and realization of the forecasts based on this forecast will provide savings or gains in government and corporate economies, which can reach billions of dollars. There is a great need for this estimation in countries where crude oil production is low and heavily dependent on crude oil import. In this paper, the parameters which are the factors affecting the crude oil prices will be interpreted using XGBoost, a gradient boosting model, from machine learning libraries and estimation will be made.


title: Context Aware Ubiquitous Learning Environments for Peer-to-Peer Collaborative Learning

abstract: A ubiquitous learning environment provides an interoperable, pervasive, and seamless learning architecture to connect, integrate, and share three major dimensions of learning resources: learning collaborators, learning contents, and learning services. Ubiquitous learning is characterized by providing intuitive ways for identifying right learning collaborators, right learning contents and right learning services in the right place at the right time. Our context aware ubiquitous learning environment consists of three systems, namely peer-to-peer content access and adaptation system, personalized annotation management system, and multimedia real-time group discussion system. Since the effectiveness and efficiency of ubiquitous learning heavily relies on learners’ surrounding context, in this paper, we will address a context model and context acquisition mechanism for collecting contextual information at run time. We have built a context aware ubiquitous learning environment and in this paper we will address how this newly designed environment can fully support the needs of peer-to-peer collaborative learning.


title: Exploration of SNP variants affecting hair colour prediction in Europeans

abstract: DNA profiling is a key tool for forensic analysis; however, current methods identify a suspect either by direct comparison or from DNA database searches. In cases with unidentified suspects, prediction of visible physical traits e.g. pigmentation or hair distribution of the DNA donors can provide important probative information. This study aimed to explore single nucleotide polymorphism (SNP) variants for their effect on hair colour prediction. A discovery panel of 63 SNPs consisting of already established hair colour markers from the HIrisPlex hair colour phenotyping assay as well as additional markers for which associations to human pigmentation traits were previously identified was used to develop multiplex assays based on SNaPshot single-base extension technology. A genotyping study was performed on a range of European populations (n = 605). Hair colour phenotyping was accomplished by matching donor’s hair to a graded colour category system of reference shades and photography. Since multiple SNPs in combination contribute in varying degrees to hair colour predictability in Europeans, we aimed to compile a compact marker set that could provide a reliable hair colour inference from the fewest SNPs. The predictive approach developed uses a naive Bayes classifier to provide hair colour assignment probabilities for the SNP profiles of the key SNPs and was embedded into the Snipper online SNP classifier (http://mathgene.usc.es/snipper/). Results indicate that red, blond, brown and black hair colours are predictable with informative probabilities in a high proportion of cases. Our study resulted in the identification of 12 most strongly associated SNPs to hair pigmentation variation in six genes.


title: Big Data and Journalism: Epistemology, expertise, economics, and ethics

abstract: Big data is a social, cultural, and technological phenomenon—a complex amalgamation of digital data abundance, emerging analytic techniques, mythology about data-driven insights, and growing critique about the overall consequences of big-data practices for democracy and society. While media and communication scholars have begun to examine and theorize about big data in the context of media and public life broadly, what are the particular implications for journalism? This article introduces and applies four conceptual lenses—epistemology, expertise, economics, and ethics—to explore both contemporary and potential applications of big data for the professional logic and industrial production of journalism. These distinct yet inter-related conceptual approaches reveal how journalists and news media organizations are seeking to make sense of, act upon, and derive value from big data during a time of exploration in algorithms, computation, and quantification. In all, the developments of big data potentially hav...


title: Leveraging Time Series Data in Similarity Based Healthcare Predictive Models : The Case of Early ICU Mortality Prediction

abstract: Patient time series classification faces challenges in high degrees of dimensionality and missingness. In light of patient similarity theory, this study explores effective temporal feature engineering and reduction, missing value imputation, and change point detection methods that can afford similarity-based classification models with desirable accuracy enhancement. We select a piecewise aggregation approximation method to extract fine-grain temporal features and propose a minimalist method to impute missing values in temporal features. For dimensionality reduction, we adopt a gradient descent search method for feature weight assignment. We propose new patient status and directional change definitions based on medical knowledge or clinical guidelines about the value ranges for different patient status levels, and develop a method to detect change points indicating positive or negative patient status changes. We evaluate the effectiveness of the proposed methods in the context of early Intensive Care Unit mortality prediction. The evaluation results show that the k-Nearest Neighbor algorithm that incorporates methods we select and propose significantly outperform the relevant benchmarks for early ICU mortality prediction. This study makes contributions to time series classification and early ICU mortality prediction via identifying and enhancing temporal feature engineering and reduction methods for similarity-based time series classification.


title: A Coherent and Managed Runtime for ML on the SCC.

abstract: Intel’s Single-Chip Cloud Computer (SCC) is a many-core architecture which stands out due to its complete lack of cache-coherence and the presence of fast, on-die interconnect for inter-core messaging. Cache-coherence, if required, must be implemented in software. Moreover, the amount of shared memory available on the SCC is very limited, requiring stringent management of resources even in the presence of software cachecoherence. In this paper, we present a series of techniques to provide the ML programmer a cache-coherent view of memory, while effectively utilizing both private and shared memory. To that end, we introduces a new, type-guided garbage collection scheme that effectively exploits SCC’s memory hierarchy, attempts to reduce the use of shared memory in favor of message passing buffers, and provides a efficient, coherent global address space. Experimental results over a variety of benchmarks show that more than 99% of the memory requests can be potentially cached. These techniques are realized in MultiMLton, a scalable extension of MLton Standard ML compiler and runtime system on the SCC.


title: Resolving Shell Nouns

abstract: Shell nouns, such as fact and problem, occur frequently in all kinds of texts. These nouns themselves are unspecific, and can only be interpreted together with the shell content. We propose a general approach to automatically identify shell content of shell nouns. Our approach exploits lexicosyntactic knowledge derived from the linguistics literature. We evaluate the approach on a variety of shell nouns with a variety of syntactic expectations, achieving accuracies in the range of 62% (baseline = 33%) to 83% (baseline = 74%) on crowd-annotated data.


title: MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization

abstract: We propose a multi-expert restoration scheme to address the model drift problem in online tracking. In the proposed scheme, a tracker and its historical snapshots constitute an expert ensemble, where the best expert is selected to restore the current tracker when needed based on a minimum entropy criterion, so as to correct undesirable model updates. The base tracker in our formulation exploits an online SVM on a budget algorithm and an explicit feature mapping method for efficient model update and inference. In experiments, our tracking method achieves substantially better overall performance than 32 trackers on a benchmark dataset of 50 video sequences under various evaluation settings. In addition, in experiments with a newly collected dataset of challenging sequences, we show that the proposed multi-expert restoration scheme significantly improves the robustness of our base tracker, especially in scenarios with frequent occlusions and repetitive appearance variations.


title: [Hypofibrinogenemia caused by long-term administration of hemocoagulase: three cases report and literature review].

abstract: OBJECTIVE ::: A first report of 3 patients who developed hypofibrinogenemia due to long-term administration of hemocoagulase. ::: ::: ::: METHODS ::: The clinical data of three patients with hypofibrinogenemia due to long-term administration of hemocoagulase were analyzed, and the related literature was reviewed. ::: ::: ::: RESULTS ::: Case 1, a two-year old girl, had liver traumatic rupture and then treated with massive transfusion and fibrinogen infusion in addition to intravenous recombinant factor VIIa (two times) and hemocoagulase (2 U/d). The liver wound bleeding was soon stopped. However, her plasma fibrinogen level decreased to 0.12 g/L after continuous administration of hemocoagulase for 18 days. Case 2, a three-year old boy, had liver traumatic rupture and was treated with surgical repair, and then received hemocoagulase (2 U/d). On the 8th day, a large amount of blood was found to exude from abdominal cavity drainage tube and indwelling venous catheter, and his fibrinogen dropped to 0.24 g/L. Case 3 was a 45 year-old man who underwent a total mandibular resection because of malignant tumor, and he was given hemocoagulase (4 U/d). A continuous blood oozing was noted from his operation incision, and his fibrinogen level decreased to 0.25 g/L. All the three patients'plasma fibrinogen levels and coagulation tests returned to normal ranges after discontinuation of hemocoagulase administration and supplement of fibrinogen, and the bleeding stopped in cases 2 and 3. ::: ::: ::: CONCLUSION ::: Long-term use of hemocoagulase could induce hypofibrinogenemia and severe bleeding.


title: Formulation and Optimization of Alkaline Extracted Ispaghula Husk Microscopic Reservoirs of Isoniazid by Box-Behnken Statistical Design

abstract: The main objective of the present work was to formulate and optimize a microparticulate sustained release drug delivery system of isoniazid by using a novel, alkaline extracted ispaghula husk as a polymer. Isoniazid microspheres of alkaline extracted ispaghula husk were prepared by emulsification internal ionic gelation method. Results of preliminary trials indicated that the polymer concentration, cross-linking agent and stirring speed had a noticeable effect on size and surface morphology. A four-factor three-level Box-Behnken design was employed to study the effect of independent variables on dependent variables. The particle size and entrapment efficiency varied from 30.75 to 61.78 µm and 62.27% to 85.80% respectively, depending on the polymer concentration, concentration of cross-linker and stirring speed. Optimized microspheres batch based on point prediction tool of design software exhibited 83.43% drug entrapment and 51.53 µm particle size with 97.80% and 96.37% validity, respectively at the follo...


title: Dynamic modeling and experimental analysis of a two-ray undulatory fin robot

abstract: Bio-inspired undulatory fin propulsion holds considerable potential for endowing robotic underwater vehicles with low-speed manoeuvrability and stable station-keeping. Robotic fins typically comprise a number of serially arranged and individually actuated “fin rays”, interconnected by a membrane-like flexible surface. Propulsive forces are generated by the propagation of a traveling wave along the mechanism, via appropriately timed ray oscillations. The present paper describes a dynamic model for an elementary two-ray fin system, analyzed as a standard robot mechanism with additional contributions arising from the elastic deformation of the flexible membrane and from the hydrodynamic forces. The model's main aspects, particularly with regard to the hydrodynamic effects, are explored via simulation studies, as well as via experiments with a robotic prototype. The developed model can serve a number of purposes towards optimizing the mechanical design, the control strategies, and the propulsive efficacy of robotic undulatory fins.


title: Design of a capacitive DAC mismatch calibrator for split SAR ADC in 65 nm CMOS

abstract: This paper presents the design and implementation of a capacitive digital to analog converter (CDAC) mismatch calibrator used in split successive approximation resistor (SAR) analog to digital converter (ADC) in a 65 nm complementary metal oxide semiconductor (CMOS) process. The calibrator adopts a compensation capacitor connected to the least significant bit (LSB) capacitor array to calibrate the mismatch between the lowest-bit capacitor of the most significant bit (MSB) array and the LSB array. An 11-bit 50-MS/s split SAR ADC using this calibrator was developed. The measurement results show that the calibration process improves the differential nonlinearity (DNL) value from -1.2/+1.9 LSBs to -0.55/+0.75 LSBs and the integral nonlinearity (INL) value from -1.9/+2.12 LSBs to -0.95/+0.99 LSBs. The calibrated ADC achieves a signal to noise and distortion ratio (SNDR) of 58.95 dB near the Nyquist frequency and an effective number of bits (ENOB) of 9.5 bits.


title: Designing social networks to combat fear of missing out.

abstract: Fear of missing out, (hereafter referred to as FoMO), is increasingly becoming an issue of concern in relation to the use of Social Network Sites (SNSs). Despite its importance, the effects of FoMO continue to receive limited attention, while guidance on how SNSs design is responsible for developing should and, also, combatting it, remains inadequate. In this position paper, we argue that dual responsibility of SNSs design. We report on initial results of a multiphase empirical study which was undertaken to examine the features of social networks that may contribute to triggering FoMO, and to explore how future SNSs can be designed to aid people manage their FoMO. The study involved three focus group sessions and a diary study. We argue that future SNSs shall support interaction styles and protocols and their agreement and adherence processes to enable people prevent and combat FoMO and present styles for doing that.


title: An Improved Rate Matching Algorithm for 3GPP LTE Turbo Code

abstract: Long Term Evolution (LTE) describes the standardization work by the Third Generation Partnership Project (3GPP) to define a new high-speed radio access method for mobile communications systems. Rate Matching (RM) is an essential component in the adaptive coding rate system and has a great impact on the error correcting performance in turbo code system for 3GPP LTE. In this paper, we propose an efficient RM algorithm based on the circular buffer and interlaced puncturing applying to the 3GPP LTE system. More specifically, both system-parity cross output model of turbo code and puncturing parity bits preferentially pattern in the bit collection and puncturing process are introduced to obtain more stable performance in this RM algorithm. The simulation results indicate that the proposed algorithm can effectively improve the bit error ratio (BER) performance, especially at high code rate conditions, compared with traditional algorithm in LTE turbo code.


title: Exploiting the use of recurrent neural networks for driver behavior profiling

abstract: Driver behavior affects traffic safety, fuel/energy consumption and gas emissions. The purpose of driver behavior profiling is to understand and have a positive influence on driver behavior. Driver behavior profiling tasks usually involve an automated collection of driving data and the application of computer models to classify what characterizes the aggressiveness of drivers. Different sensors and classification methods have been employed for this task, although low-cost solutions, high performance and collaborative sensing remain open questions for research. This paper makes an investigation with different Recurrent Neural Networks (RNN), aiming to classify driving events employing data collected by smartphone accelerometers. The results show that specific configurations of RNN upon accelerometer data provide high accuracy results, being a step towards the development of safer transportation systems.


title: Omnidirectional planar dipole array antenna

abstract: A planar dipole array antenna having good omnidirectional radiation in the azimuthal plane is presented. The planar dipole array antenna comprises two 1 /spl times/ 2 dipole arrays arranged back to back and separated by a narrow rectangular ground plane, and is easily constructed by printing on both sides of a dielectric substrate. Prototypes of the proposed antenna for WLAN operation in the 2.4 GHz band (2400-2484 MHz) were constructed and studied. Good omnidirectional radiation with gain variations less than 2 dBi in the azimuthal plane has been obtained, and the measured maximum antenna gain for operating frequencies across the 2.4 GHz band is about 5.0-6.1 dBi. Effects of the ground-plane width and the array spacing on the performance of the proposed antenna are also studied.


title: Adaptive Particle Swarm Optimization

abstract: An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity.


title: Robotic assisted andrological surgery.

abstract: The introduction of the operative microscope for andrological surgery in the 1970s provided enhanced magnification and accuracy, unparalleled to any previous visual loop or magnification techniques. This technology revolutionized techniques for microsurgery in andrology. Today, we may be on the verge of a second such revolution by the incorporation of robotic assisted platforms for microsurgery in andrology. Robotic assisted microsurgery is being utilized to a greater degree in andrology and a number of other microsurgical fields, such as ophthalmology, hand surgery, plastics and reconstructive surgery. The potential advantages of robotic assisted platforms include elimination of tremor, improved stability, surgeon ergonomics, scalability of motion, multi-input visual interphases with up to three simultaneous visual views, enhanced magnification, and the ability to manipulate three surgical instruments and cameras simultaneously. This review paper begins with the historical development of robotic microsurgery. It then provides an in-depth presentation of the technique and outcomes of common robotic microsurgical andrological procedures, such as vasectomy reversal, subinguinal varicocelectomy, targeted spermatic cord denervation (for chronic orchialgia) and robotic assisted microsurgical testicular sperm extraction (microTESE).


title: A novel hybrid approach to detect and correct spelling in Tamil text

abstract: A spell checker is a tool to check the spelling of words in a document, validate them and in case of finding error in spelling, list out the possible words as suggestions. There are several researches available on spell checkers for European languages and Indian languages. But few for Tamil language, maybe, because of its nature as it is a highly inflectional and complex language. An efficient error detection and correction approach for spell checking in Tamil language has been proposed in this paper. A novel hybrid approach is adopted using tree-based algorithm with stemming and n-gram techniques. Testing results show that our system detects perfectively the error in spelling and provides most suitable suggestions for correcting the misspelt words with minimum of 91% accuracy.


title: Cloud application logging for forensics

abstract: Logs are one of the most important pieces of analytical data in a cloud-based service infrastructure. At any point in time, service owners and operators need to understand the status of each infrastructure component for fault monitoring, to assess feature usage, and to monitor business processes. Application developers, as well as security personnel, need access to historic information for debugging and forensic investigations. This paper discusses a logging framework and guidelines that provide a proactive approach to logging to ensure that the data needed for forensic investigations has been generated and collected. The standardized framework eliminates the need for logging stakeholders to reinvent their own standards. These guidelines make sure that critical information associated with cloud infrastructure and software as a service (SaaS) use-cases are collected as part of a defense in depth strategy. In addition, they ensure that log consumers can effectively and easily analyze, process, and correlate the emitted log records. The theoretical foundations are emphasized in the second part of the paper that covers the implementation of the framework in an example SaaS offering running on a public cloud service. While the framework is targeted towards and requires the buy-in from application developers, the data collected is critical to enable comprehensive forensic investigations. In addition, it helps IT architects and technical evaluators of logging architectures build a business oriented logging framework.


title: Impedance Modeling and Analysis of Grid-Connected Voltage-Source Converters

abstract: This paper presents small-signal impedance modeling of grid-connected three-phase converters for wind and solar system stability analysis. In the proposed approach, a converter is modeled by a positive-sequence and a negative-sequence impedance directly in the phase domain. It is further demonstrated that the two sequence subsystems are decoupled under most conditions and can be studied independently from each other. The proposed models are verified by experimental measurements and their applications are demonstrated in a system testbed.


title: Feature selection for biometrie recognition based on electrocardiogram signals

abstract: Currently the demand for the development of more precise and reliable methods of person identification have received attention from the academic community and industry, with Biometrics being one of these new approaches. The term ‘Biometrics’ is used to refer to identification techniques based on physical or behavioural characteristics. As biometric recognition becomes increasingly popular, the fear of circumvention, obfuscation and replay attacks is a rising concern. Since the traditional biometric modalities (face, iris and fingerprint) are not able to supply the needs of every possible security requirement, numerous emerging biometric modalities are presented, trying to fill the gap. Biomedical signals, like electrocardiogram (ECG) and electroencephalogram (EEG), have been proposed as emerging biometric modalities. The advantages of using the ECG for biometric recognition can be summarized as universality, permanence, uniqueness, robustness to attacks, liveness detection. According to the utilized features, the existing ECG based biometric systems can be classified to fiducial, non-fiducial and hybrids systems. This papers analyses the impact of some feature selection strategies like Genetic Algorithm, Memetic Algorithm and Particle Swarm Optimization on the performance of Biometric Systems based on ECG using K-Nearest Neighbours, Support Vector Machines, Optimum Path Forest and a Euclidean Distance Classifier for classification task. The results show that there is a subset of features extracted from the ECG signal that provides high recognition rates.


title: Design and fabrication of a three dimensional printable non-assembly articulated hand exoskeleton for rehabilitation

abstract: Robotic rehabilitation has proven to be cost-effective in accelerating the rehabilitation process by eliminating the constant need for supervision by a therapist. This work aimed to design and develop a novel three-dimensional (3D) printable non-assembly five-fingered robotic hand exoskeleton for rehabilitation. A single degree-of-freedom (DOF) linkage was designed to actuate each finger with 3 output links that correspond to the three phalanxes of the human finger. We used a parametric modelling approach that suits the dimensions of individual's hand. The fabrication of this dynamic model was achieved by printing the complete assembly including all the driving links, output links, and joints. We manufactured a prototype and developed real-time actuation and control. The reported unique linkage design, combined with parametric modelling and 3D printing technology, will pave the way for mass customization of active assistive and resistive hand exoskeletons.


title: Induction of Root and Pattern Lexicon for Unsupervised Morphological Analysis of Arabic

abstract: We propose an unsupervised approach to learning non-concatenative morphology, which we apply to induce a lexicon of Arabic roots and pattern templates. The approach is based on the idea that roots and patterns may be revealed through mutually recursive scoring based on hypothesized pattern and root frequencies. After a further iterative refinement stage, morphological analysis with the induced lexicon achieves a root identification accuracy of over 94%. Our approach differs from previous work on unsupervised learning of Arabic morphology in that it is applicable to naturally-written, unvowelled text.


title: Chapter 58: Analysis of Log File Data to Understand Behavior and Learning in an Online Community

abstract: How do we study the behavior of users in online communities? Researchers from a variety of disciplines have evolved a rich set of both quantitative and qualitative approaches to studying human‐computer interaction (HCI) and computer-mediated communication (CMC), and these methods are useful in the study of online communities. Unique to the study of CMC and online communities is the possibility of collecting log file data .I ti spossible for the computer to record every command typed by users—in some cases, every keystroke. In cases where users interact only online, we can collect a comprehensive record of all of their interactions. The completeness of the record and ease of collecting it are unprecedented. However, log file data is more often collected than analyzed. We can save everything, but what does it mean? This chapter presents two examples of the use of log file data to understand user behavior and learning in one online environment, MOOSE Crossing. MOOSE Crossing is a text-based virtual reality environment (or “MUD”) in which kids aged eight and older learn objectoriented programming and practice their creative writing. From analysis of log file data from the environment, we gained significant new insights into user behavior and learning. We will briefly discuss an example of qualitative log file analysis, in which a close reading of records of online interaction provides insights into how children learn from one another in this environment (Bruckman, 2000). The rest of the chapter contains an extended example of the use of quantitative log file analysis to try to understand whether there is any relationship between gender and programming achievement on this site. (An earlier version of this analysis was published in Proceedings of CSCL 2002 (Bruckman et al., 2002).) Quantitative and qualitative methods are generally more powerful when used in a complementary fashion. In the data to follow, our qualitative findings suggested that girls are especially strong achievers in this learning environment. However, quantitative analysis of log file data shows that boys and girls use the environment in similar ways. A small group of girls form the core of “the regulars”, the most active, dedicated members who are highly visible to both researchers and other members. The regulars have extraordinary levels


title: Joint feature selection and structure preservation for domain adaptation

abstract: The essence of domain adaptation is to explore common latent factors shared by the involved domains. These factors can be specific features or geometric structures. Most of previous methods exploit either the shared features or the shared geometric structures separately. However, the two strategies are complementary with each other and jointly exploring them is more optimal. This paper proposes a novel approach, named joint Feature Selection and Structure Preservation (FSSP), for unsupervised domain adaptation. FSSP smoothly integrates structure preservation and feature selection into a unified optimization problem. Intensive experiments on text categorization, image classification and video event recognition demonstrate that our method performs better, even with up to 30% improvement in average, compared with the state-of-the-art methods.


title: Life satisfaction and family functions as-predictors of problematic Internet use in university students

abstract: Male college students use the Internet more than female students.Daily Internet usage time has a large impact on its negative consequences.Smoking, alcohol use, and gambling are risk factors for problematic Internet use.Problematic Internet use increases as students' life satisfaction decreases.Problematic Internet use is higher in students with poor family relationships. One goal of this research was to analyze problematic Internet use in university students according to such variables as gender, grade point average, satisfaction with one's department, mother's/father's education level, smoking, alcohol consumption, gambling behavior, relationship between parents, length of Internet use, amount of time spent on the Internet daily, and using the Internet for academic purposes. Another goal was to analyze family functioning and life satisfaction as predictors of problematic Internet use in university students. The study sample comprised 663 university students from Dokuz Eylul University, Izmir. The Problematic Internet Use Scale, Family Evaluation Scale, Life Satisfaction Scale, and a questionnaire requesting demographic information were administered. The results revealed that the family functioning dimensions of problem solving, roles, and behavioral control, as well as gender, age, gambling behavior, perception of the relationship between one's parents, number of years of Internet use, amount of time spent on the Internet daily, and using the Internet for academic purposes explained 48% of the total variance in Internet use. There was also a significant relationship between university students' life satisfaction and total Internet use, rate of Internet overuse, and the social benefits and negative consequences of Internet use.


title: Pyramidal RoR for image classification

abstract: The Residual Networks of Residual Networks (RoR) exhibits excellent performance in the image classification task, but sharply increasing the number of feature map channels makes the characteristic information transmission incoherent, which losses a certain of information related to classification prediction, limiting the classification performance. In this paper, a Pyramidal RoR network model is proposed by analysing the performance characteristics of RoR and combining with the PyramidNet. Firstly, based on RoR, the Pyramidal RoR network model with channels gradually increasing is designed. Secondly, we analysed the effect of different residual block structures on performance, and chosen the residual block structure which best favoured the classification performance. Finally, we add an important principle to further optimize Pyramidal RoR networks, drop-path is used to avoid over-fitting and save training time. In this paper, image classification experiments were performed on CIFAR-10/100 and SVHN datasets, and we achieved the current lowest classification error rates were 2.96%, 16.40% and 1.59%, respectively. Experiments show that the Pyramidal RoR network optimization method can improve the network performance for different data sets and effectively suppress the gradient disappearance problem in DCNN training.


title: Analyzing data sets with missing data: an empirical evaluation of imputation methods and likelihood-based methods

abstract: Missing data are often encountered in data sets used to construct software effort prediction models. Thus far, the common practice has been to ignore observations with missing data. This may result in biased prediction models. The authors evaluate four missing data techniques (MDTs) in the context of software cost modeling: listwise deletion (LD), mean imputation (MI), similar response pattern imputation (SRPI), and full information maximum likelihood (FIML). We apply the MDTs to an ERP data set, and thereafter construct regression-based prediction models using the resulting data sets. The evaluation suggests that only FIML is appropriate when the data are not missing completely at random (MCAR). Unlike FIML, prediction models constructed on LD, MI and SRPI data sets will be biased unless the data are MCAR. Furthermore, compared to LD, MI and SRPI seem appropriate only if the resulting LD data set is too small to enable the construction of a meaningful regression-based prediction model.


title: A Security Architecture for 5G Networks

abstract: 5G networks will provide opportunities for the creation of new services, for new business models, and for new players to enter the mobile market. The networks will support efficient and cost-effective launch of a multitude of services, tailored for different vertical markets having varying service and security requirements, and involving a large number of actors. Key technology concepts are network slicing and network softwarization, including network function virtualization and software-defined networking. The presented security architecture builds upon concepts from the 3G and 4G security architectures but extends and enhances them to cover the new 5G environment. It comprises a toolbox for security relevant modeling of the systems, a set of security design principles, and a set of security functions and mechanisms to implement the security controls needed to achieve stated security objectives. In a smart city use case setting, we illustrate its utility; we examine the high-level security aspects stemming from the deployment of a large number of IoT devices and network softwarization.


title: Edge Bundling in Information Visualization

abstract: The edge, which can encode relational data in graphs and multidimensional data in parallel coordinates plots, is an important visual primitive for encoding data in information visualization research. However, when data become very large, visualizations often suffer from visual clutter as thousands of edges can easily overwhelm the display and obscure underlying patterns. Many edge-bundling techniques have been proposed to reduce visual clutter in visualizations. In this survey, we briefly introduce the visual-clutter problem in visualizations. Thereafter, we review the cost-based, geometry-based, and image-based edge-bundling methods for graphs, parallel coordinates, and flow maps. We then describe the various visualization applications that use edge-bundling techniques and discuss the evaluation studies concerning the effectiveness of edge-bundling methods. An edge-bundling taxonomy is proposed at the end of this survey.


title: Aldo van Eyck’s Playgrounds: Aesthetics, Affordances, and Creativity

abstract: After World War II, the Dutch architect Aldo van Eyck developed hundreds of playgrounds in the city of Amsterdam. These public playgrounds were located in parks, squares, and derelict sites, and consisted of minimalistic aesthetic play equipment that was supposed to stimulate the creativity of children. Over the last decades, these playgrounds have been studied by sociologists, theorists of art and architecture, and psychologists. Adopting an ecological approach to the human environment, it is argued that the abstract forms of van Eyck’s play sculptures indeed stimulate the creativity of the child. Whereas a slide or a swing almost dictates what a child is supposed to do, van Eyck’s play equipment invites the child to actively explore the numerous affordances (action possibilities) it provided. However, it is argued that the standardization (e.g. equal distances between blocks or bars) that tends to characterize van Eyck’ play equipment has negative effects on the playability. This standardization, which was arguably the result of the aesthetic motives of the designer, might be appealing to children when simply looking at the equipment, but it is not of overriding importance to them when playing in it. Indeed, a recent study indicates that the affordances provided by messy structures appear to have a greater appeal to playing children.


title: Adaptive Energy-Control for In-Memory Database Systems

abstract: The ever-increasing demand for scalable database systems is limited by their energy consumption, which is one of the major challenges in research today. While existing approaches mainly focused on transaction-oriented disk-based database systems, we are investigating and optimizing the energy consumption and performance of data-oriented scale-up in-memory database systems that make heavy use of the main power consumers, which are processors and main memory. We give an in-depth energy analysis of a current mainstream server system and show that modern processors provide a rich set of energy-control features, but lack the capability of controlling them appropriately, because of missing application-specific knowledge. Thus, we propose the Energy-Control Loop (ECL) as an DBMS-integrated approach for adaptive energy-control on scale-up in-memory database systems that obeys a query latency limit as a soft constraint and actively optimizes energy efficiency and performance of the DBMS. The ECL relies on adaptive workload-dependent energy profiles that are continuously maintained at runtime. In our evaluation, we observed energy savings ranging from 20% to 40% for a real-world load profile.


title: Electric spring for power quality improvement

abstract: In this paper, we discuss the principles of operating the electric spring (ES) as a reactive power compensator and as a power factor corrector. The theory on electric springs with capacitors for voltage stabilization is reviewed to present a general idea on the behavior of ES. Further discussion focuses on the principle of ES with batteries to cover its eight possible operating modes and their usefulness in providing line current regulation. An input current control scheme is designed for ES with batteries to validate its capability in power factor correction. A low-voltage single-phase power system with different types of loads has been built for verifying the feasibility of proposed theory of ES with batteries. Experimental results show that the ES is capable of performing the eight operating modes when changing the power consumption of the non-critical load, and that with the proposed input current control, the ES can achieve power factor correction for both RL and RC loads.


title: Positional Cloning in Maize (Zea mays subsp. mays, Poaceae)

abstract: Premise of the study: Positional (or map-based) cloning is a common approach to identify the molecular lesions causing mutant phenotypes. Despite its large and complex genome, positional cloning has been recently shown to be feasible in maize, opening up a diverse collection of mutants to molecular characterization. Methods and Results: Here we outline a general protocol for positional cloning in maize. While the general strategy is similar to that used in other plant species, we focus on the unique resources and approaches that should be considered when applied to maize mutants. Conclusions: Positional cloning approaches are appropriate for maize mutants and quantitative traits, opening up to molecular characterization the large array of genetic diversity in this agronomically important species. The cloning approach described should be broadly applicable to other species as more plant genomes become available.


title: An Android Cloud Storage Apps Forensic Taxonomy

abstract: Abstract Mobile phones have been playing a very significant role in our daily activities for the last decade. With the increase need for these devices, people are now more reliant on their smartphone applications for their daily tasks and many prefer to save their mobile data on a cloud platform to access them anywhere on any device. Cloud technology is the new way for better data storage, as it offers better security, more flexibility, and mobility. Many smartphones have been investigated as subjects, objects, or tools of the crime. Many of these investigations include analyzing data stored through cloud storage apps which contributes to importance of cloud apps forensics on mobile devices. In this paper, various cloud Android applications are analyzed using the forensics tool XRY and a forensics taxonomy for investigation of these apps is suggested. The proposed taxonomy reflects residual artifacts retrievable from 31 different cloud applications. It is expected that the proposed taxonomy and the forensic findings in this paper will assist future forensic investigations involving cloud based storage applications.


Classic Nintendo Games are ( NP-) Hard


Author identification in short texts


title: Sex Trafficking and the Exploitation of Adolescents

abstract: Human trafficking affects a surprisingly large number of adolescents around the globe. Women and girls make up the majority of sex trafficking victims. Nurses must be aware of sex trafficking as a form of sexual violence in the adolescent population. Nurses can play a role in identifying, intervening, and advocating for victims of human trafficking as they currently do for patients that are the victims of other types of violent crimes.


Design And Implementation Of A Robotic Arm Based On Haptic Technology


title: Improving Lexical Embeddings with Semantic Knowledge

abstract: Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We propose a new learning objective that incorporates both a neural language model objective (Mikolov et al., 2013) and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements.


title: Emotion-Related Visual Mismatch Responses in Schizophrenia: Impairments and Correlations with Emotion Recognition

abstract: Background and Objectives ::: Mismatch negativity (MMN) is an event-related potential (ERP) measure of preattentional sensory processing. While deficits in the auditory MMN are robust electrophysiological findings in schizophrenia, little is known about visual mismatch response and its association with social cognitive functions such as emotion recognition in schizophrenia. Our aim was to study the potential deficit in the visual mismatch response to unexpected facial emotions in schizophrenia and its association with emotion recognition impairments, and to localize the sources of the mismatch signals. ::: Experimental Design ::: The sample comprised 24 patients with schizophrenia and 24 healthy control subjects. Controls were matched individually to patients by gender, age, and education. ERPs were recorded using a high-density 128-channel BioSemi amplifier. Mismatch responses to happy and fearful faces were determined in 2 time windows over six regions of interest (ROIs). Emotion recognition performance and its association with the mismatch response were also investigated. ::: Principal Observations ::: Mismatch signals to both emotional conditions were significantly attenuated in patients compared to controls in central and temporal ROIs. Controls recognized emotions significantly better than patients. The association between overall emotion recognition performance and mismatch response to the happy condition was significant in the 250–360 ms time window in the central ROI. The estimated sources of the mismatch responses for both emotional conditions were localized in frontal regions, where patients showed significantly lower activity. ::: Conclusions ::: Impaired generation of mismatch signals indicate insufficient automatic processing of emotions in patients with schizophrenia, which correlates strongly with decreased emotion recognition.


title: Mapping Ontologies into Cyc

abstract: The advent of Web services, and the Semantic Web described by domain ontologies, highlight the bottleneck to their growth: ontology mapping, merging, and integration. In this paper we present the process by which over the last 15 years several ontologies of varying complexity have been mapped or integrated with Cyc, a large commonsense knowledge base. These include SENSUS, FIPS 10-4, several large (300k-term) pharmaceutical thesauri, large portions of WordNet, MeSH/Snomed/UMLS, and the CIA World Factbook. This has to date required trained ontologists talking with subject matter experts. To break that bottleneck – to enable subject matter experts to directly map/merge/integrate their ontologies – we have been developing interactive clarification-dialog-based tools.


title: Music to the inner ears: Exploring individual differences in musical imagery

abstract: In two studies, we explored the frequency and phenomenology of musical imagery. Study 1 used retrospective reports of musical imagery to assess the contribution of individual differences to imagery characteristics. Study 2 used an experience sampling design to assess the phenomenology of musical imagery over the course of one week in a sample of musicians and non-musicians. Both studies found episodes of musical imagery to be common and positive: people rarely wanted such experiences to end and often heard music that was personally meaningful. Several variables predicted musical imagery, including personality, musical preferences, and positive mood. Musicians tended to hear musical imagery more often, but they reported less frequent episodes of deliberately-generated imagery. Taken together, the present research provides new insights into individual differences in musical imagery, and it supports the emerging view that such experiences are common, positive, and more voluntary than previously recognized.


title: Human-in-the-loop Control of a Humanoid Robot for Disaster Response: A Report from the DARPA Robotics Challenge Trials

abstract: The DARPA Robotics Challenge DRC requires teams to integrate mobility, manipulation, and perception to accomplish several disaster-response tasks. We describe our hardware choices and software architecture, which enable human-in-the-loop control of a 28 degree-of-freedom Atlas humanoid robot over a limited bandwidth link. We discuss our methods, results, and lessons learned for the DRC Trials tasks. The effectiveness of our system architecture was demonstrated as the WPI-CMU DRC Team scored 11 out of a possible 32 points, ranked seventh out of 16 at the DRC Trials, and was selected as a finalist for the DRC Finals.