title: Selective self-presentation in computer-mediated communication : Hyperpersonal dimensions of technology , language , and cognition

abstract: The hyperpersonal model of computer-mediated communication (CMC) posits that users exploit the technological aspects of CMC in order to enhance the messages they construct to manage impressions and facilitate desired relationships. This research examined how CMC users managed message composing time, editing behaviors, personal language, sentence complexity, and relational tone in their initial messages to different presumed targets, and the cognitive awareness related to these processes. Effects on several of these processes and outcomes were obtained in response to different targets, partially supporting the hyperpersonal perspective of CMC, with unanticipated gender and status interaction effects suggesting behavioral compensation through CMC, or overcompensation when addressing presumably undesirable partners.


title: Realistic modeling of simple and complex cell tuning in the hmax model, and implications for invariant object recognition in cortex

abstract: Abstract : Riesenhuber & Poggio recently proposed a model of object recognition in cortex which, beyond integrating general beliefs about the visual system in a quantitative framework, made testable predictions about visual processing. In particular, they showed that invariant object representation could be obtained with a selective pooling mechanism over properly chosen afferents through a MAX operation: For instance, at the complex cells level, pooling over a group of simple cells at the same preferred orientation and position in space but at slightly different spatial frequency would provide scale tolerance, while pooling over a group of simple cells at the same preferred orientation and spatial frequency but at slightly different position in space would provide position tolerance. Indirect support for such mechanisms in the visual system comes from the ability of the architecture at the top level to replicate shape tuning as well as shift and size invariance properties of "view-tuned cells" (VTUs) found in inferotemporal cortex (IT), the highest area in the ventral visual stream, thought to be crucial in mediating object recognition in cortex. There is also now good physiological evidence that a MAX operation is performed at various levels along the ventral stream. However, in the original paper by Riesenhuber & Poggio, tuning and pooling parameters of model units in early and intermediate areas were only qualitatively inspired by physiological data. Many studies have investigated the tuning properties of simple and complex cells in primary visual cortex, V1. We show that units in the early levels of HMAX can be tuned to produce realistic simple and complex cell-like tuning, and that the earlier findings on the invariance properties of model VTUs still hold in this more realistic version of the model.


title: A survey of unmanned aerial vehicles (UAVs) for traffic monitoring

abstract: The focus of this paper is on surveying UAV-based systems for traffic monitoring and management. Although there has been voluminous research on the subject, unmanned aerial vehicles (UAVs) are proven to be a viable and less time-consuming alternative to real-time traffic monitoring and management, providing the eye-in-the-sky solution to the problem.


title: Rethinking sketching as sampling: Linear transforms of graph signals

abstract: Sampling of bandlimited graph signals has well-documented merits for dimensionality reduction, affordable storage, and online processing of streaming network data. Most existing sampling methods are designed to minimize the error incurred when reconstructing the original signal from its samples. Oftentimes these parsimonious signals serve as inputs to computationally-intensive linear transformations (e.g., graph filters). Hence, interest shifts from reconstructing the signal itself towards instead approximating the output of the prescribed linear operator efficiently. In this context, we propose a novel sampling scheme that leverages the bandlimitedness of the input as well as the transformation whose output we wish to approximate. We formulate problems to jointly optimize sample selection and a sketch of the target linear transformation, so when the latter is affordably applied to the sampled input signal the result is close to the desired output. The developed sampling plus reduced-complexity processing pipeline is particularly useful for streaming data, where the linear transform has to be applied fast and repeatedly to successive inputs.


title: Combined regression and ranking

abstract: Many real-world data mining tasks require the achievement of two distinct goals when applied to unseen data: first, to induce an accurate preference ranking, and second to give good regression performance. In this paper, we give an efficient and effective Combined Regression and Ranking method (CRR) that optimizes regression and ranking objectives simultaneously. We demonstrate the effectiveness of CRR for both families of metrics on a range of large-scale tasks, including click prediction for online advertisements. Results show that CRR often achieves performance equivalent to the best of both ranking-only and regression-only approaches. In the case of rare events or skewed distributions, we also find that this combination can actually improve regression performance due to the addition of informative ranking constraints.


title: Towards identifying software project clusters with regard to defect prediction

abstract: Background: This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study perfomed before showed the need for a further in-depth analysis in order to identify project clusters. Aims: The goal of this research is to perform clustering on software projects in order to identify groups of software projects with similar characteristic from the defect prediction point of view. One defect prediction model should work well for all projects that belong to such group. The existence of those groups was investigated with statistical tests and by comparing the mean value of prediction efficiency. Method: Hierarchical and k-means clustering, as well as Kohonen's neural network was used to find groups of similar projects. The obtained clusters were investigated with the discriminant analysis. For each of the identified group a statistical analysis has been conducted in order to distinguish whether this group really exists. Two defect prediction models were created for each of the identified groups. The first one was based on the projects that belong to a given group, and the second one - on all the projects. Then, both models were applied to all versions of projects from the investigated group. If the predictions from the model based on projects that belong to the identified group are significantly better than the all-projects model (the mean values were compared and statistical tests were used), we conclude that the group really exists. Results: Six different clusters were identified and the existence of two of them was statistically proven: 1) cluster proprietary B -- T=19, p=0.035, r=0.40; 2) cluster proprietary/open - t(17)=3.18, p=0.05, r=0.59. The obtained effect sizes (r) represent large effects according to Cohen's benchmark, which is a substantial finding. Conclusions: The two identified clusters were described and compared with results obtained by other researchers. The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used. Furthermore, a method of clustering was suggested and applied.


title: Development of Bengali Named Entity Tagged Corpus and its Use in NER Systems

abstract: The rapid development of language tools using machine learning techniques for less computerized languages requires appropriately tagged corpus. A Bengali news corpus has been developed from the web archive of a widely read Bengali newspaper. A web crawler retrieves the web pages in Hyper Text Markup Language (HTML) format from the news archive. At present, the corpus contains approximately 34 million wordforms. The date, location, reporter and agency tags present in the web pages have been automatically named entity (NE) tagged. A portion of this partially NE tagged corpus has been manually annotated with the sixteen NE tags with the help of Sanchay Editor 1 , a text editor for Indian languages. This NE tagged corpus contains 150K wordforms. Additionally, 30K wordforms have been manually annotated with the twelve NE tags as part of the IJCNLP08 NER Shared Task for South and South East Asian Languages 2 . A table driven semi-automatic NE tag conversion routine has been developed in order to convert the sixteen-NE tagged corpus to the twelve-NE tagged corpus. The 150K NE tagged corpus has been used to develop Named Entity Recognition (NER) system in Bengali using pattern directed shallow parsing approach, Hidden Markov Model (HMM), Maximum Entropy (ME) Model, Condi


title: Fully Reused VLSI Architecture of FM0/Manchester Encoding Using SOLS Technique for DSRC Applications

abstract: The dedicated short-range communication (DSRC) is an emerging technique to push the intelligent transportation system into our daily life. The DSRC standards generally adopt FM0 and Manchester codes to reach dc-balance, enhancing the signal reliability. Nevertheless, the coding-diversity between the FM0 and Manchester codes seriously limits the potential to design a fully reused VLSI architecture for both. In this paper, the similarity-oriented logic simplification (SOLS) technique is proposed to overcome this limitation. The SOLS technique improves the hardware utilization rate from 57.14% to 100% for both FM0 and Manchester encodings. The performance of this paper is evaluated on the postlayout simulation in Taiwan Semiconductor Manufacturing Company (TSMC) 0.18- $\mu{\rm m}$ 1P6M CMOS technology. The maximum operation frequency is 2 GHz and 900 MHz for Manchester and FM0 encodings, respectively. The power consumption is 1.58 mW at 2 GHz for Manchester encoding and 1.14 mW at 900 MHz for FM0 encoding. The core circuit area is $65.98\times 30.43~\mu{\rm m}^{2}$ . The encoding capability of this paper can fully support the DSRC standards of America, Europe, and Japan. This paper not only develops a fully reused VLSI architecture, but also exhibits an efficient performance compared with the existing works.


title: An Algebraic Theory of Actors and Its Application to a Simple Object-Based Language

abstract: The development of Simula by Ole-Johan Dahl and Kristen Nygaard introduced a number of important programming language concepts – object which supports modularity in programming through encapsulation of data and procedures, the concept of class which organizes behavior and supports Abstract Data Types, and the concept inheritance which provides subtyping relations and reuse [6]. Peter Wegner terms programming languages which use objects as object-based languages, and reserves the term object-oriented languages for languages which also support classes and inheritance [58].


title: Acoustic Segment Modeling with Spectral Clustering Methods

abstract: This paper presents a study of spectral clustering-based approaches to acoustic segment modeling (ASM). ASM aims at finding the underlying phoneme-like speech units and building the corresponding acoustic models in the unsupervised setting, where no prior linguistic knowledge and manual transcriptions are available. A typical ASM process involves three stages, namely initial segmentation, segment labeling, and iterative modeling. This work focuses on the improvement of segment labeling. Specifically, we use posterior features as the segment representations, and apply spectral clustering algorithms on the posterior representations. We propose a Gaussian component clustering (GCC) approach and a segment clustering (SC) approach. GCC applies spectral clustering on a set of Gaussian components, and SC applies spectral clustering on a large number of speech segments. Moreover, to exploit the complementary information of different posterior representations, a multiview segment clustering (MSC) approach is proposed. MSC simultaneously utilizes multiple posterior representations to cluster speech segments. To address the computational problem of spectral clustering in dealing with large numbers of speech segments, we use inner product similarity graph and make reformulations to avoid the explicit computation of the affinity matrix and Laplacian matrix. We carried out two sets of experiments for evaluation. First, we evaluated the ASM accuracy on the OGI-MTS dataset, and it was shown that our approach could yield 18.7% relative purity improvement and 15.1% relative NMI improvement compared with the baseline approach. Second, we examined the performances of our approaches in the real application of zero-resource query-by-example spoken term detection on SWS2012 dataset, and it was shown that our approaches could provide consistent improvement on four different testing scenarios with three evaluation metrics.


title: Enabling the coexistence of LTE and Wi-Fi in unlicensed bands

abstract: The expansion of wireless broadband access network deployments is resulting in increased scarcity of available radio spectrum. It is very likely that in the near future, cellular technologies and wireless local area networks will need to coexist in the same unlicensed bands. However, the two most prominent technologies, LTE and Wi-Fi, were designed to work in different bands and not to coexist in a shared band. In this article, we discuss the issues that arise from the concurrent operation of LTE and Wi-Fi in the same unlicensed bands from the point of view of radio resource management. We show that Wi-Fi is severely impacted by LTE transmissions; hence, the coexistence of LTE and Wi-Fi needs to be carefully investigated. We discuss some possible coexistence mechanisms and future research directions that may lead to successful joint deployment of LTE and Wi-Fi in the same unlicensed band.


title: A comparative study of audio compression based on compressed sensing and Sparse Fast Fourier transform (SFFT): Performance and challenges

abstract: Audio compression has become one of the basic multimedia technologies. Choosing an efficient compression scheme that is capable of preserving the signal quality while providing a high compression ratio is desirable in the different standards worldwide. In this paper we study the application of two highly acclaimed sparse signal processing algorithms, namely, Compressed Sensing (CS) and Sparse Fart Fourier transform, to audio compression. In addition, we present a Sparse Fast Fourier transform (SFFT)-based framework to compress audio signal. This scheme embeds the K-largest frequencies indices as part of the transmitted signal and thus saves in the bandwidth required for transmission.


title: Privacy Preservation by Disassociation

abstract: In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniques (a) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure. We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.


title: GIFT: A Real-Time and Scalable 3D Shape Search Engine

abstract: Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different view points. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, thus cannot satisfy the basic requirement of scalability for search engines. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: (1) efficient projection and view feature extraction using GPU acceleration, (2) the first inverted file, referred as F-IF, is utilized to speed up the procedure of multi-view matching, (3) the second inverted file (S-IF), which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and Inverted File (Twice), as GIFT. Besides its high efficiency, GIFT also outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.


title: SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data

abstract: This paper considers the automatic design of fuzzy-rule-based classification systems from labeled data. The performance of classifiers and the interpretability of generated rules are of major importance in these systems. In past research, some genetic-based algorithms have been used for the rule learning process. These genetic fuzzy systems have utilized different approaches to encode rules. In this paper, we have proposed a novel steady- state genetic algorithm to extract a compact set of good fuzzy rules from numerical data (SGERD). The selection mechanism of this algorithm is nonrandom, and only the best individuals can survive. Our approach is very simple and fast, and can be applied to high-dimensional problems with numerical attributes. To select the rules having high generalization capabilities, our algorithm makes use of some rule- and data-dependent parameters. We have also proposed an enhancing function that modifies the rule evaluation measures in order to assess the candidate rules more effectively before their selection. Experiments on some well-known data sets are performed to show the performance of SGERD.


title: Structural Aging: The Facial Recurve Concept

abstract: Cutaneous facial aging is responsible for the increasingly wrinkled and blotchy appearance of the skin, whereas aging of the facial structures is attributed primarily to gravity. This article purports to show, however, that the primary etiology of structural facial aging relates instead to repeated contractions of certain facial mimetic muscles, the age marker fascicules, whereas gravity only secondarily abets an aging process begun by these muscle contractions. Magnetic resonance imaging (MRI) has allowed us to study the contrasts in the contour of the facial mimetic muscles and their associated deep and superficial fat pads in patients of different ages. The MRI model shows that the facial mimetic muscles in youth have a curvilinear contour presenting an anterior surface convexity. This curve reflects an underlying fat pad lying deep to these muscles, which acts as an effective mechanical sliding plane. The muscle’s anterior surface convexity constitutes the key evidence supporting the authors’ new aging theory. It is this youthful convexity that dictates a specific characteristic to the muscle contractions conveyed outwardly as youthful facial expression, a specificity of both direction and amplitude of facial mimetic movement. With age, the facial mimetic muscles (specifically, the age marker fascicules), as seen on MRI, gradually straighten and shorten. The authors relate this radiologic end point to multiple repeated muscle contractions over years that both expel underlying deep fat from beneath the muscle plane and increase the muscle resting tone. Hence, over time, structural aging becomes more evident as the facial appearance becomes more rigid.


title: CleanOS: Limiting mobile data exposure with idle eviction

abstract: Mobile-device theft and loss have reached gigantic proportions. Despite these threats, today's mobile devices are saturated with sensitive information due to operating systems that never securely erase data and applications that hoard it on the vulnerable device for performance or convenience. This paper presents CleanOS, a new Android-based operating system that manages sensitive data rigorously and maintains a clean environment at all times. To do so, CleanOS leverages a key property of today's mobile applications - the use of trusted, cloud-based services. Specifically, CleanOS identifies and tracks sensitive data in RAM and on stable storage, encrypts it with a key, and evicts that key to the cloud when the data is not in active use on the device. We call this process idle eviction of sensitive data. To implement CleanOS, we used the TaintDroid mobile taint-tracking system to identify sensitive data locations and instrumented Android's Dalvik interpreter to securely evict that data after a specified period of non-use. Our experimental results show that CleanOS limits sensitive-data exposure drastically while incurring acceptable overheads on mobile networks.


title: Network-based indicators of Bitcoin bubbles

abstract: The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions.


title: Demystifying the Millennial student: a reassessment in measures of character and engagement in professional education.

abstract: The characteristic profile of Millennial Generation students, driving many educational reforms, can be challenged by research in a number of fields including cognition, learning style, neurology, and psychology. This evidence suggests that the current aggregate view of the Millennial student may be less than accurate. Statistics show that Millennial students are considerably diverse in backgrounds, personalities, and learning styles. Data are presented regarding technological predilection, multitasking, reading, critical thinking, professional behaviors, and learning styles, which indicate that students in the Millennial Generation may not be as homogenous in fundamental learning strategies and attitudes as is regularly proposed. Although their common character traits have implications for instruction, no available evidence demonstrates that these traits impact their fundamental process of learning. Many curricular strategies have been implemented to address alleged changes in the manner by which Millennial students learn. None has clearly shown superior outcomes in academic accomplishments or developing expertise for graduating students and concerns persist related to the successful engagement of Millennial students in the process of learning. Four factors for consideration in general curricular design are proposed to address student engagement and optimal knowledge acquisition for 21st century learners. Anat Sci Educ 4: 214-226. © 2011 American Association of Anatomists.


title: Automatically recommending code reviewers based on their expertise: an empirical comparison

abstract: Code reviews are an essential part of quality assurance in Free, Libre, and Open Source Software (FLOSS) projects. However, finding a suitable reviewer can be difficult, and delayed or forgotten reviews are the consequence. Automating reviewer selection with suitable algorithms can mitigate this problem. We compare empirically six algorithms based on modification expertise and two algorithms based on review expertise on four major FLOSS projects. Our results indicate that the algorithms based on review expertise yield better recommendations than those based on modification expertise. The algorithm Weighted Review Count (WRC) recommends at least one out of five reviewers correctly in 69 % to 75 % of all cases, which is one of the best results achieved in the comparison.


title: Switch-induced error voltage on a switched capacitor

abstract: A concise analytical expression for switch-induced error voltage on a switched capacitor is derived from the distributed MOSFET model. The result can be interpreted in terms of a simple lumped equivalent circuit. With this expression the dependence is investigated of the error voltage on process parameters and on switch turnoff rate, source resistance, and other circuit parameters. These results can be used to quickly predict the error voltage. The analytical expression is in close agreement with computer simulations and experiments.


title: Learning to Recognize Activities from the Wrong View Point

abstract: Appearance features are good at discriminating activities in a fixed view, but behave poorly when aspect is changed. We describe a method to build features that are highly stable under change of aspect. It is not necessary to have multiple views to extract our features. Our features make it possible to learn a discriminative model of activity in one view, and spot that activity in another view, for which one might poses no labeled examples at all. Our construction uses labeled examples to build activity models, and unlabeled, but corresponding, examples to build an implicit model of how appearance changes with aspect. We demonstrate our method with challenging sequences of real human motion, where discriminative methods built on appearance alone fail badly.


title: NEW DIRECTIONS in MILITARY and SECURITY STUDIES: ARTIFICIAL INTELLIGENCE and MILITARY DECISION MAKING PROCESS

abstract: The nature and range of threats to security have extremely changed that caused a following reaction which is the transition in the nature of the war as well. Under the changing circumstances of operational environment and in the face of new security environment which is more complex and ambiguous than before, modern armies have started to look for alternatives or for better options to surpass the challenge of transition in the new era. In order to comprehend the security environment better and find out new or alternative ways for solutions, International Conference on Military and Security Studies (ICMSS-2015) launched in Turkish War Colleges Command, Istanbul on March 10 th and 11 th . This report is prepared to mention the importance of the military decision making and present the summary of one of the workshops which is “Decision Making and Artificial Intelligence” for the purpose of helping the research in the field of the study.


title: Application of the Finite-Difference Time-Domain Method to Sinusoidal Steady-State Electromagnetic-Penetration Problems

abstract: A numerical method for predicting the sinusoidal steady-state electromagnetic fields penetrating an arbitrary dielectric or conducting body is described here. The method employs the finite-difference time-domain (FD-TD) solution of Maxwell's curl equations implemented on a cubic-unit-cell space lattice. Small air-dielectric loss factors are introduced to improve the lattice truncation conditions and to accelerate convergence of cavity interior fields to the sinusoidal steady state. This method is evaluated with comparison to classical theory, method-of-moment frequency-domain numerical theory, and experimental results via application to a dielectric sphere and acylindrical metal cavity with an aperture. Results are also given for a missile-like cavity with two different types of apertures illuminated by an axial-incidence plane wave.


title: A LSTM-based method for stock returns prediction: A case study of China stock market

abstract: Prediction of stock market has attracted attention from industry to academia [1, 2]. Various machine learning algorithms such as neural networks, genetic algorithms, support vector machine, and others are used to predict stock prices.


title: Adaptive MCMC with Bayesian Optimization

abstract: This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to nondifferentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust the parameters of the proposal mechanism automatically to ensure efficient mixing of the Markov chains.


title: Unseen Class Discovery in Open-world Classification

abstract: This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject examples from unseen or novel classes that have not appeared in training. Specifically, this paper focuses on discovering the hidden unseen classes of the rejected examples. Clearly, without prior knowledge this is difficult. However, we do have the data from the seen training classes, which can tell us what kind of similarity/difference is expected for examples from the same class or from different classes. It is reasonable to assume that this knowledge can be transferred to the rejected examples and used to discover the hidden unseen classes in them. This paper aims to solve this problem. It first proposes a joint open classification model with a sub-model for classifying whether a pair of examples belongs to the same or different classes. This sub-model can serve as a distance function for clustering to discover the hidden classes of the rejected examples. Experimental results show that the proposed model is highly promising.


title: Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

abstract: Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios. A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time. In this paper, we propose a channel pruning technique for accelerating the computations of deep convolutional neural networks (CNNs) that does not critically rely on this assumption. Instead, it focuses on direct simplification of the channel-to-channel computation graph of a CNN without the need of performing a computationally difficult and not-always-useful task of making high-dimensional tensors of CNN structured sparse. Our approach takes two stages: first to adopt an end-to- end stochastic training method that eventually forces the outputs of some channels to be constant, and then to prune those constant channels from the original neural network by adjusting the biases of their impacting layers such that the resulting compact model can be quickly fine-tuned. Our approach is mathematically appealing from an optimization perspective and easy to reproduce. We experimented our approach through several image learning benchmarks and demonstrate its interesting aspects and competitive performance.


title: Chinese tallow ( Triadica sebifera ) invasion in maritime forests: The role of anthropogenic disturbance and its management implication

abstract: Land-use and forest management practices may facilitate the invasion success of non-native plants in forests. In this study, we tested if agricultural land abandonment and subsequent forest management contributed to the invasion success of Chinese tallow (Triadica sebifera (L.) Small) in the maritime forest of Parris Island, SC. We compared the abundance of Chinese tallow between disturbed and remnant forests, described Chinese tallow establishment patterns in relation to forest management activities, and characterized the structure and composition of disturbed and remnant forests in order to better understand relationships between stand characteristics and invasibility as indicated by Chinese tallow abundance. We found that stands in agricultural land use in 1939 but reforested with slash pine (Pinus elliottii Englem.) since the 1970s (i.e., disturbed forests) had significantly more Chinese tallow stems than stands that remained forested since 1939 (i.e., remnant forests). Remnant forests had significantly greater woody species richness and were more variable in species composition and structure than disturbed forests. Disturbed forests were dominated by early successional, shade intolerant species with a denser woody understory, while remnant forests included species associated with late successional habitats. The number of forest management events was positively associated with Chinese tallow abundance, explaining 34% of the total variation in stem density. Chinese tallow individuals commonly established immediately after forest thinning and their numbers increased exponentially through time. Our findings support that Chinese tallow establishment was strongly related to anthropogenic disturbance including historical agricultural land-use and forest management. This suggests that Chinese tallow invasion may be a symptom, rather than the driver, of the ecological degradation induced by persistent human perturbations.


title: Efficient spam detection across Online Social Networks

abstract: Online Social Networks (OSNs) have become more and more popular in the whole world. People share their personal activities, views and opinions among different OSNs. At the same time, social spam appears more frequently and in various formats throughout popular OSNs. Therefore, efficient detection of spam has become an important and popular problem. This paper focuses on spam detection across multiple online social networks by leveraging the knowledge of detecting similar spam within a social network and using it in different networks. We chose Facebook and Twitter for our study targets, considering that they share the most similar features in posts, topics, and user activities, etc. We collected two datasets from them and performed analysis based on our proposed methodology. The results show that detection combined with spam in Facebook show a more than 50% decrease of spam tweets in Twitter, and detection combined with spam of Twitter shows a nearly 71.2% decrease of spam posts in Facebook. This means similar spam of one social network can greatly facilitate spam detection in other social networks. We proposed a new perspective of spam detection in OSNs.


title: Orthodontic single tooth intrusion with a removable interocclusal device: A clinical report

abstract: Interdisciplinary therapy was used to treat a patient missing a single posterior tooth opposing an extruded tooth. The simple technique used a removable interocclusal device and elastic band for orthodontic intrusion over a 2-month period after implant placement in the opposing arch to aid in prosthetic site development. This approach was designed to increase the interocclusal space needed to restore the implant, obtain a more consistent occlusal plane, and improve esthetics. Clinical and radiographic examinations at the 6-month follow-up revealed an acceptable occlusal relationship and improved esthetic appearance that was achieved with the documented course of treatment.


title: An N2logN back-projection algorithm for SAR image formation

abstract: We propose a fast algorithm for far-field SAR imaging based on a new fast back-projection algorithm developed for tomography. We also modify the algorithm for the near-field scenario. The fast back-projection algorithm for SAR has a computational complexity O(N/sup 2/logN). Compared to traditional FFT-based methods, our new algorithm has potential advantages: the new algorithm does not need frequency-domain interpolation, which becomes complex for the wide-angle case; the new approach is applicable to the near-field scenario, taking into account wavefront curvature; and the back-projection algorithm can be easily adapted to parallel computing architectures. For some scenarios of interest, the computational cost of the new back-projection approach is similar to or less than that for FFT based algorithms.


title: MPSoCSim: An extended OVP simulator for modeling and evaluation of Network-on-Chip based heterogeneous MPSoCs

abstract: In this paper a SystemC simulator for Network-on-Chip (NoC) based Multiprocessor Systems-on-Chip (MPSoCs) is presented. The simulator currently supports mesh topology with wormhole switching and several routing algorithms such as XY-, a minimal West-First and an adaptive West-First algorithm. The impact of routing algorithms regarding performance can be analyzed by means of the presented simulator. In order to simulate a heterogeneous MPSoC, ARM processors and MicroBlazes can be attached to the NoC. Processor and peripheral models used within the test platforms are provided by Imperas/OVP. Moreover, traffic generators are available to analyze the system. An additional SystemC component enables the readout of simulation time from within the application. For evaluation of the simulator multiple platforms and applications were put under test and compared with a hardware implementation. The comparison shows that the simulator improves the development of MPSoCs by early estimation of system requirements.


title: Identi fi cation of MCI individuals using structural and functional connectivity networks

abstract: Abstract Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.


title: Systems Theory Model for Information Security

abstract: Architecting security solutions for today's diverse computer systems is a challenge. The modern business environment is comprised of many different applications, e-mail, databases, e-commerce, and more. Each of these has its own threat profile and associated business risk. The complexity of the computing environment extends to the design of security solutions. Current methodologies for designing security systems include piecemeal designs and patchwork systems comprised of multiple point solutions. As the complexity of the business driven systems increase, these methods are being strained to keep up with security requirements. Systems science provides information on how complex systems interact with their environment, and this guidance can be applied to designing security architectures. Analysis and design of security systems using systems theory provides a new path to reduce the complexity.


title: A Security Metrics Taxonomization Model for Software-Intensive Systems

abstract: Abstract: We introduce a novel high-level security metrics objective taxonomization model for soft-ware-intensive systems. The model systematizes and organizes security metrics development activities. It focuses on the security level and security performance of technical systems while taking into account the alignment of metrics objectives with different business and other management goals. The model emphasizes the roles of security-enforcing mechanisms, the overall security quality of the system un-der investigation, and secure system lifecycle, project and business management. Security correctness, effectiveness and efficiency are seen as the fundamental measurement objectives, determining the di-rections for more detailed security metrics development. Integration of the proposed model with risk-driven security metrics development approaches is also discussed. Keywords: Security Metrics, Security Objectives, Tax onomy, Correctness, Effectiveness, Efficiency 1. Introduction The increasing complexity and connectivity of software-intensive systems, products and services are boosting the needs for pertinent and reliable software security and trusted system solutions. Systematic approaches to measur-ing security are needed to obtain evidence of the security level and performance in systems, products and services. In addition, early security evidence will enable cost-effective secure software development. It is easier to make business and engineering decisions concerning security if sufficient and credible evidence of security is available. The field of developing security metrics systematically is young. The complication behind the immaturity of secu-rity metrics is that the current practice of security is still a highly diverse field, and holistic and widely accepted ap-proaches are still missing [1]. , attempts to measure secu-rity have only obtained limited success [2]. Lately, security metrics has become an emerging research area rapidly gaining momentum. The main contribution of this study is to introduce a novel model for security metrics objective taxonomization of technical systems and discuss the motivation for it. The model systematizes and organizes security metrics devel-opment. We analyze the role of different emphasis areas and fundamental measurement objectives and show how the model can be integrated with risk-driven security met-rics development activities. In our model, we have made a premeditated choice not to divide security metrics into technical, operational and organizational metrics, which is the most common classification. The rest of this article is organized in the following way. Section 2 analyzes related work, and Section 3 gives a short introduction to security metrics. Section 4 presents our Security Metrics Objective Segments (SMOS) model, and Section 5 discusses the design of security metrics tax-onomies with the help of the proposed model. Section 6 analyzes how the model can be integrated with the security metrics development process. Section 7 incorporates a dis-cussion on the results and security metrics in general terms, and finally, Section 8 gives conclusions and finalizes the study with some future research questions.


title: Asymmetric Head Gear: A Comparison between Unilateral Outer Bow Expansion and Unilateral Outer Bow Shortening - an Energy Approach using the Finite Element Method.

abstract: OBJECTIVE ::: To compare the influence of outer bow length difference and unilateral outer bow expansion on the 'asymmetric effect' of the headgear. ::: ::: ::: METHODS ::: Twelve 3D finite element models of a headgear with two tubes were designed, which were similar except for the outer bow length or the degree of unilateral expansion in Solid-Works 2011. A 3N force was applied to the outer bow ends in ANSYS Workbench 12.1 and the distalising, lateral forces to molars, moments and the energy of the system were evaluated. ::: ::: ::: RESULTS ::: As the degree of unilateral expansion increased, the net differences in all findings were increased up to a point, and then changed. There was an increasing pattern in the length difference group. Buccal movement was observed in the intact/shorter side molar. ::: ::: ::: CONCLUSION ::: Unilateral shortened outer bow asymmetric headgears are more efficient and more predictable in clinical application than the unilateral outer bow expansion.


title: Quality Properties Evaluation for Software Requirements Specifications: An Exploratory Analysis

abstract: Most software problems arise from deficiencies in the manner in which software requirements are elicited and expressed. Ensuring that the Software Requirements Specification document (SRS) has the necessary quality is crucial to the success of any software development project, since its information is used across all project stages. However, assessing the quality of a SRS is not a simple process, mainly by the multitude of proposals, often contradictory, of the attributes to be evaluated and the methodologies used for that purpose. This work is intended to be a compendium of the most important tendencies and strategies in the field that serves as a starting point for developing comprehensive models and tools for quality attributes evaluation in a SRS.


title: Boolean Difference Based Reliability Evaluation of Fault-Tolerant Circuit Structures on FPGAs

abstract: The reliability of FPGA based hardware designs has become an important field of research particularly for space computing. Traditionally, redundancy is utilized in FPGA based designs to achieve reliable or error-tolerant computing. However, the redundant designs vary according to the granularity level and the voter placement algorithms used for the hardware design. The resulting circuit configurations vary in area, latency and power as well as in the achieved reliability. While the evaluation of area, latency and power is done by the FPGA design tools, quantitative data for reliability are usually not derived. Consequently, there is a need for an automated reliability evaluation tool especially considering the huge design space of redundant circuit structures. In this paper we combine the Boolean difference error calculator (BDEC), a probabilistic reliability model for hardware designs, with a reliability model for fault-tolerant circuit structures. As a result, we are able to study the reliability of fault-tolerant circuit structures at the logic layer. We focus on fault-tolerant circuits to be implemented in FPGA and show how to extend our combined model from combinational to sequential circuits. For an automated analysis, we develop a MATLAB-based tool utilizing our extended BDEC model. An application of our automated BDEC tool has been provided with a case study on dynamic reliability management to demonstrate how such a tool providing quantitative reliability data can improve the four-dimensional Pareto optimization for area, latency, power and reliability.


title: Challenges in software evolution

abstract: Today's information technology society increasingly relies on software at all levels. Nevertheless, software quality generally continues to fall short of expectations, and software systems continue to suffer from symptoms of aging as they are adapted to changing requirements and environments. The only way to overcome or avoid the negative effects of software aging is by placing change and evolution in the center of the software development process. In this article we describe what we believe to be some of the most important research challenges in software evolution. The goal of this document is to provide novel research directions in the software evolution domain.


title: Common-Mode Noise Reduction Schemes for Weakly Coupled Differential Serpentine Delay Microstrip Lines

abstract: This paper proposes design schemes to reduce the common mode noise from weakly coupled differential serpentine delay microstrip lines (DSDMLs). The proposed approach is twofold: we leverage strongly coupled vertical-turn-coupled traces (VTCTs) instead of weakly coupled VTCTs (conventional pattern) and add guard traces. Time-and frequency-domain analyses of the proposed schemes for reducing the common-mode noise are performed by studying the transmission waveform and the differential-to-common mode conversion using the circuit solver HSPICE and the 3-D full-wave simulator HFSS, respectively. Compared to the conventional design of the weakly coupled DSDMLs, the proposed solutions yield a reduction of about 54% of the peak-to-peak amplitude of the common-mode noise, while the differential impedance remains matched along the complete length of the DSDML. Moreover, the range of frequencies, over which the magnitude of the differential-to-common mode conversion is now significantly reduced, is very wide, i.e., about 0.3-10 GHz. Furthermore, the differential insertion and reflection loss introduced by the newly proposed designs are almost the same as the ones achieved by using the conventional design. Finally, a favorable comparison between simulated and measured results confirms the excellent common-mode noise reduction performance of the proposed schemes.


title: Efficient Functional Encryption for Inner-Product Values with Full-Hiding Security

abstract: We construct an efficient non-generic private-key functional encryption (FE) for inner-product values with full-hiding security, where confidentiality is assured not only for encrypted data but also for functions associated with secret keys. Recently, Datta et al. presented such a scheme in PKC 2016 and this is the only scheme that achieved full-hiding security. Our scheme has an advantage over their scheme for the following points.


title: Experimental study of self-heating effect (SHE) in SOI MOSFETs: Accurate understanding of temperatures during AC conductance measurement, proposals of 2ω method and modified pulsed IV

abstract: In this paper, a new method for accurate characterization of MOSFETs' thermal properties is proposed. First, experimental verification of conventional AC conductance technique is conducted. We developed novel measurement techniques composed of AC conductance technique and four-probe gate resistance method. Using the new technique, it is clarified that MOSFETs' channel temperature under AC conductance measurement is not isothermal because of “Static SHE”, and that AC conductance technique is not suitable for complete thermal characterization. Next, we modified conventional pulsed IV method. It enables us to determine accurate channel thermal resistance and to obtain isothermal IV curve. The new pulsed IV method enables to measure accurate thermal properties of the devices.


title: Impact and Challenges of Requirement Engineering in Agile Methodologies: A Systematic Review

abstract: Requirement Engineering is one of important stage in development life cycle. All requirements required for development of product is collected in this phase. A high standard product can be developed by agile methodology in less budget and time. Importance of agile practices have been enhanced since it offers assist cooperation too software engineering. Being basic phase of software engineering, requirement engineering has different processes. The elements of direct correspondence is one of spry way which not at all like to other conventional and traditional approaches .Although a lot of research has been done on agile practices and role of requirement in agile methodologies but still there is need of studies on change manage management ,requirement prioritization, prototyping and nonfunctional requirement in agile methodologies. Aim of this review paper is to present the limitations in presentation of requirement engineering phases in agile practices and what are the issues and challenges that agile person faces in implementation of agile practices. Many research studies from different sources have been reviewed on basis of inclusion and exclusion criteria. Most RE activities has been discussed in review. Evidence helps to prove that how RE process was performed in scrums. Mostly research has been conducted on general agile methodologies, few authors specified RE practices in other methodologies of agile. Finding of this research is the work of researchers that will be beneficial for those who are interested in finding interesting area of research in this field because many techniques of agile (extreme programming, crystal methodology, lean ) requires further study and practical results as clarified by studies.


title: Link-Level Performance of Downlink NOMA with SIC Receiver Considering Error Vector Magnitude

abstract: This paper presents the link-level performance of downlink non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) receiver, considering the influence of error vector magnitude (EVM). In our evaluations, for the case of a 1-by-2 transmission and the number of NOMA multiplexed users is two, we clarify the performance of NOMA for cell-center user equipment (UE) with codeword level SIC (CWIC) receiver and the performance of the cell-edge UE, which does not apply SIC. Based on link-level performance evaluations, we show that the performance of CWIC receiver is almost the same as that of ideal SIC when the allocated transmit power ratio to cell-center UE is less than 0.35, the modulation and coding scheme (MCS) of cell-center UE is 16QAM (R = 0.51) or 64QAM (R = 0.47), and the MCS of cell-edge UE is QPSK (R = 0.51). Furthermore, we also show that, when EVM value is 4 % and the allocated power ratio to cell-center UE is between 0.2 and 0.37, the level of degradation due to the influence of EVM is below 0.6 dB, which is almost the same level of degradation as that of orthogonal frequency division multiple access (OFDMA) for the EVM value required by 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE). On the other hand, we show that the influence of EVM is marginal for cell-edge UE compared to cell-center UE. However, when the cell-center UE uses a higher order modulation such as 64QAM, we show that the required SNR of cell-edge UE increases due to the increase in inter-user interference. Based on these results, we clarify that it is important for the base station (BS) to take into account the target BLER and influence of EVM when selecting the MCS and allocated transmit power ratio of NOMA UEs.


title: Time-aware Recommender System Using Naïve Bayes Classifier Weighting Technique

abstract: Collaborative filtering method have been widely used in the recommender system which has problems of scalability and highly time consuming between the process of recommendation and traditional method didn't concern over the difference of time that a user has rating for an item. In this paper, we proposed a Naive Bayes Classifier technique that uses the user rating for the item and item information to construct a model with time awareness. It will provide better scalability and accuracy recommendation result even the system has more sparsity. In addition, it decreases time consuming by constructing a model in the offline and calculate the recommendation results in online phase. We show the result from our proposed can make the system get more efficient.


title: The Art of Appeal in Electronic Commerce : Understanding the Impact of Product and Website Quality on Online Purchases

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to advance product appeal and website appeal as focal psychological mechanisms that can be invoked by business-to-consumer e-commerce sites to mitigate problems of information asymmetry via signaling to bolster consumers’ purchase intention under the influence of trust. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Survey approach was employed to validate the research model. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Website appeal partially mediates the positive effect of product appeal on consumers’ purchase intention. Trust in e-commerce sites not only increases purchase intention directly, but it also reinforces the positive relationship between website appeal and purchase intention while attenuating the positive relationship between product appeal and purchase intention. Service content quality, search delivery quality, and enjoyment are confirmed as positive antecedents of website appeal whereas diagnosticity and justifiability are established as positive antecedents of product appeal. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: This study not only delineates product and website appeal as complementary drivers of consumer purchase on e-commerce sites, but it also derives five signals that aid in bolstering both product and website appeal. Trust is revealed to exert a moderating influence on the impact of product and website appeal on purchase intention. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: Practitioners should prioritize their resource allocation to enhance qualities most pertinent to product and website appeal. E-commerce sites should offer product-oriented functionalities to facilitate product diagnosticity and reassure consumers of their purchase decisions. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study distinguishes between product and website appeal as well as between their respective antecedents. It also uncovers how trust can alter the effects of both website and product appeal on consumers’ purchase intention.


title: The mental health of refugee children.

abstract: The UK is facing a major increase in the number of people seeking asylum each year, of whom approximately a quarter are children. The stressors to which refugees are exposed are described in three stages: (1) while in their country of origin; (2) during their flight to safety; and (3) when having to settle in a country of refuge. The evidence concerning the impact of displacement on children's mental health is reviewed and a framework for conceptualising the risk factors is proposed. The available literature shows consistently increased levels of psychological morbidity among refugee children, especially post-traumatic stress disorder, depression, and anxiety disorders. The principles underlying the delivery of mental health care for these children are also considered. It is argued that much primary prevention can be undertaken in the school context. Some key aspects of British immigration law are examined and the tension between the law and the best interests of the child principle is discussed. There is particular concern for the plight of unaccompanied children. Attention to the mental health needs of this vulnerable group is urgently required.


title: Spatial Frequency Loss for Learning Convolutional Autoencoders

abstract: This paper presents a learning method for convolutional autoencoders (CAEs) for extracting features from images. CAEs can be obtained by utilizing convolutional neural networks to learn an approximation to the identity function in an unsupervised manner. The loss function based on the pixel loss (PL) that is the mean squared error between the pixel values of original and reconstructed images is the common choice for learning. However, using the loss function leads to blurred reconstructed images. A method for learning CAEs using a loss function computed from features reflecting spatial frequencies is proposed to mitigate the problem. The blurs in reconstructed images show lack of high spatial frequency components mainly constituting edges and detailed textures that are important features for tasks such as object detection and spatial matching. In order to evaluate the lack of components, a convolutional layer with a Laplacian filter bank as weights is added to CAEs and the mean squared error of features in a subband, called the spatial frequency loss (SFL), is computed from the outputs of each filter. The learning is performed using a loss function based on the SFL. Empirical evaluation demonstrates that using the SFL reduces the blurs in reconstructed images.


title: Image segmentation for lung region in chest X-ray images using edge detection and morphology

abstract: Studies of medical image segmentation have long been done as a mean to distinguish object region from one to another for further image analysis. The segmentation of lung region in chest X-ray (CXR) based on object edge detection is one of the popular method applied. Early edge detection algorithms like Sobel, Prewitt and Laplacian have been used to segment the lung however, none of them can successfully generate a truly satisfied segmentation output. The reason for this fail is because they are high pass filter that is sensitive to image noise. Hence, the requirement for better edge detection algorithm that can cope with reasonable lower and upper threshold value for image noise like canny edge should be highlighted. Moreover, combining this algorithm with morphology method (dilation and erosion) will produce better outcome. Therefore, this paper has proposed method for segmenting lung region in CXR images using canny edge filter and morphology. Although the filter can detect the lung edge, unfortunately, the final edges lines produce are still unsatisfied. To solve the problem, Euler number method is applied to extract the lung region before executing the edge detection using the filter. The implementation produced convincing result as most of the segmented image is almost similar to the ground truth image.


title: Deep Reinforcement Learning for Mention-Ranking Coreference Models

abstract: Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning. In this paper we instead apply reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics. We experiment with two approaches: the REINFORCE policy gradient algorithm and a reward-rescaled max-margin objective. We find the latter to be more effective, resulting in significant improvements over the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task.


title: Confidence limits for the indirect effect: Distribution of the product and resampling methods

abstract: The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.


title: Design of package BGA pin-out for >25Gb/s high speed SerDes considering PCB via crosstalk

abstract: Fast growing bandwidth demands for networking and data center applications continuously face difficult signal integrity challenges. Among them, serializer/deserializer (SerDes) package pin-out and its corresponding printed circuit board (PCB) via crosstalk have become a significant source of high jitter and limit the performance of high speed serial links. In this paper, a variety of far-end crosstalk (FEXT) and near-end crosstalk (NEXT) SerDes pin-out patterns are designed and investigated to explore the crosstalk performance for interfaces running 25Gb/s and beyond. The impact of PCB routing layer selection and via stub are also considered. Both frequency domain and time domain analysis are studied based on 3-dimensional (3D) full wave electromagnetic (EM) simulation, and the results are compared along with the calculated integrated crosstalk noise (ICN) to find the pattern with better signal isolation. A PCB test vehicle is designed and manufactured to provide measurement and verification results by using broadband micro-probes and multi-port vector network analyzer (VNA). The optimized pin-out is then selected to evaluate the overall link performance based on the input/output buffer information specification algorithmic modeling interface (IBIS-AMI) model from silicon vendors.


title: Compositional Model-Based Sketch Generator in Facial Entertainment

abstract: Face sketch synthesis (FSS) plays an important role in facial entertainment, which includes face sketch morphing among two styles, multiview FSS and face sketch expression manipulation. For facial entertainment, most existing FSS methods generate sketches with over-smoothing effects, i.e., fine details are suppressed more or less. In this paper, we propose a face sketch generator based on the compositional model to handle this issue. It decomposes a face into different components instead of patches as before, and each component has several candidate templates. Multilevel B-spline approximation is utilized to delicately polish the chosen templates of all components. To fuse these components, Poisson blending is employed instead of the weighted average operator. The proposed compositional method crucially reduces the high frequency loss and improves the synthesis performance in comparison to the state-of-the-art methods. Experiments on face sketch morphing, expression manipulation, and multiview FSS, make further efforts to demonstrate the effectiveness of the proposed method.


title: On the importance of single directions for generalization

abstract: Despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance. However, the differences between the learned solutions of networks which generalize and those which do not remain unclear. Additionally, the tuning properties of single directions (defined as the activation of a single unit or some linear combination of units in response to some input) have been highlighted, but their importance has not been evaluated. Here, we connect these lines of inquiry to demonstrate that a network's reliance on single directions is a good predictor of its generalization performance, across networks trained on datasets with different fractions of corrupted labels, across ensembles of networks trained on datasets with unmodified labels, across different hyperparameters, and over the course of training. While dropout only regularizes this quantity up to a point, batch normalization implicitly discourages single direction reliance, in part by decreasing the class selectivity of individual units. Finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.


title: A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

abstract: We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network (parameterized as $x\mapsto W_N \cdots W_1x$) by minimizing the $\ell_2$ loss over whitened data. Convergence at a linear rate is guaranteed when the following hold: (i) dimensions of hidden layers are at least the minimum of the input and output dimensions; (ii) weight matrices at initialization are approximately balanced; and (iii) the initial loss is smaller than the loss of any rank-deficient solution. The assumptions on initialization (conditions (ii) and (iii)) are necessary, in the sense that violating any one of them may lead to convergence failure. Moreover, in the important case of output dimension 1, i.e. scalar regression, they are met, and thus convergence to global optimum holds, with constant probability under a random initialization scheme. Our results significantly extend previous analyses, e.g., of deep linear residual networks (Bartlett et al., 2018).


title: A Wideband and Low-Profile Discrete Dielectric Lens Using 3-D Printing Technology

abstract: In this paper, a novel wideband and low-profile discrete dielectric lens (DDL) antenna is presented. The proposed unit cells of DDL with the square-pyramid structure can realize wideband antireflection at an air–dielectric interface. By increasing the phase compensation range, the DDL antenna with very large gain bandwidth but large DDL thickness would be achieved. Furthermore, based on the large gain bandwidth technology, a novel multifolded lens structure is proposed to realize the low-profile design, in which a broadband polarization rotation reflection surface is proposed, while a polarized grid is used appropriately. Finally, the proposed multifolded DDL antenna can achieve a very large gain bandwidth, while the thicknesses of the DDL unit cell and the total antenna can be greatly reduced. For demonstration, the proposed multifolded DDL antenna is fabricated and measured. The fabrication was implemented by using a simple and low-cost 3-D printing technology. The results show that the proposed lens antenna can realize a 1 dB gain drop bandwidth of 43%, thickness of less than 1/3 focal-length-to-diameter ratio, and maximum aperture efficiency of 32%. Good agreement can be observed between the measurement and simulation.


title: Design metrics for blind ADC-based wireline receivers

abstract: ADC-based receivers use an ADC in the front end to convert the incoming signal to digital where significant equalization can be done in digital domain. These receivers can be classified as phase-tracking and blind architectures. In the former, the VCO phase is controlled through a feedback loop so as to sample the received data in the middle of the data eye. In the latter, the received signal is sampled with a blind clock, i.e. not in a loop, and the data at the center is obtained by data processing techniques such as data interpolation and extrapolation. This paper compares the two architectures in terms of their design complexity and cost, and derives equations that relate the required ADC resolution to channel loss and to the characteristics of the FFE/DFE that follow the ADC.


title: XoR: Hybrid drive exoskeleton robot that can balance

abstract: We propose a novel exoskeleton robot prototype aimed at a brain-machine interface and rehabilitation for postural control for elderly people, people with spinal cord injury, stroke patients, and others with similar needs. By arranging pneumatic muscles with electric motors in a optimal way, one can achieve both weight-reduction and torque-controllability. Its anthropomorphic design and torque-controllability enable users to implement and test various rehabilitation/compensation programs consistent with human motor control and learning mechanism. Hybrid drive itself is not new, but its specialized application to lightweight exoskeleton is novel. This paper reports the design and development of the robot, particularly addressing a hybrid drive for load-bearing tasks such as standing and postural maintenance. The experimental data as well as the attached videos demonstrate the effectiveness of the proposed system.


title: Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem

abstract: In retailer management, the Newsvendor problem has widely attracted attention as one of basic inventory models. In the traditional approach to solving this problem, it relies on the probability distribution of the demand. In theory, if the probability distribution is known, the problem can be considered as fully solved. However, in any real world scenario, it is almost impossible to even approximate or estimate a better probability distribution for the demand. In recent years, researchers start adopting machine learning approach to learn a demand prediction model by using other feature information. In this paper, we propose a supervised learning that optimizes the demand quantities for products based on feature information. We demonstrate that the original Newsvendor loss function as the training objective outperforms the recently suggested quadratic loss function. The new algorithm has been assessed on both the synthetic data and real-world data, demonstrating better performance.


title: Detection of regular objects in baggage using multiple X-ray views

abstract: In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of a method based on multiple X-ray views to detect some regular prohibited items with very defined shapes and sizes. The method consists of two steps: ‘structure estimation’, to obtain a geometric model of the multiple views from the object to be inspected (a baggage), and ‘parts detection’, to detect the parts of interest (prohibited items). The geometric model is estimated using a structure from motion algorithm. The detection of the parts of interest is performed by an adhoc segmentation algorithm (object dependent) followed by a general tracking algorithm based on geometric and appearance constraints. In order to illustrate the effectiveness of the proposed method, experimental results on detecting regular objects −razor blades and guns− are shown yielding promising results.


title: A MIMO System With Multifunctional Reconfigurable Antennas

abstract: A multiple-input-multiple-output (MIMO) system equipped with a new class of antenna arrays, henceforth referred to as multifunction reconfigurable antenna arrays (MRAAs), is investigated. The elements of MRAA, i.e., multifunction reconfigurable antennas (MRAs) presented in this work are capable of dynamically changing the sense of polarization of the radiated field thereby providing two reconfigurable modes of operation, i.e., polarization diversity and space diversity. The transmission signaling scheme can also be switched between transmit diversity (TD) and spatial multiplexing (SM). The results show that the reconfigurable modes of operation of an MRAA used in conjunction with adaptive space-time modulation techniques provide additional degrees of freedom to the current adaptive MIMO systems, resulting in more robust system in terms of quality, capacity and reliability. A performance gain up to 30 dB is possible with the proposed system over conventional fixed antenna MIMO systems depending on the channel conditions


title: A dataset for Movie Description

abstract: Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length HD movies. In addition we also collected the aligned movie scripts which have been used in prior work and compare the two different sources of descriptions. In total the MPII Movie Description dataset (MPII-MD) contains a parallel corpus of over 68K sentences and video snippets from 94 HD movies. We characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are far more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production.


title: Qualitative Decision Methods for Multi-Attribute Decision Making

abstract: The fundamental problem underlying all multi-criteria decision analysis (MCDA) problems is that of dominance between any two alternatives: "Given two alternatives A and B, each described by a set criteria, is A preferred to B with respect to a set of decision maker (DM) preferences over the criteria?". Depending on the application in which MCDA is performed, the alternatives may represent strategies and policies for business, potential locations for setting up new facilities, designs of buildings, etc. The general objective of MCDA is to enable the DM to order all alternatives in order of the stated preferences, and choose the ones that are best, i.e., optimal with respect to the preferences over the criteria. This article presents and summarizes a recently developed MCDA framework that orders the set of alternatives when the relative importance preferences are incomplete, imprecise, or qualitative in nature.


title: Distributed fair scheduling in a wireless LAN

abstract: Fairness is an important issue when accessing a shared wireless channel. With fair scheduling , it is possible to allocate bandwidth in proportion to weights of the packet flows sharing the channel. This paper presents a fully distributed algorithm for fair scheduling in a wireless LAN. The algorithm can be implemented without using a centralized coordinator to arbitrate medium access. The proposed protocol is derived from the Distributed Coordination Function in the IEEE 802.11 standard. Simulation results show that the proposed algorithm is able to schedule transmission such that the bandwidth allocated to different flows is proportional to their weights. An attractive feature of the proposed approach is that it can be implemented with simple modifications to the IEEE 802.11 standard.


title: Modified "double-Z-plasty" in the closure of medial canthal defects.

abstract: The correction of medial canthal defects following tumor resection is a very common problem in oculoplastic surgery, and consequently, many surgical techniques have been devised to accomplish this. Limberg's "rhomboid flap" is a technique which has been modified especially to suit the particular anatomy of the medial canthus. This technique embodies mobilization of thin eyelid skin as opposed to thicker nose, brow, or facial skin. A representative case is presented.


title: Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC

abstract: In the fall of 2013, we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a for-credit version of the course on-campus using the Coursera pl...


title: Movie genre classification by exploiting audio-visual features of previews

abstract: We present a method to classify movies on the basis of audio-visual cues present in previews. A preview summarizes the main idea of a movie providing a suitable amount of information to perform genre classification. In our approach movies are initially classified into action and non-action by computing the visual disturbance feature and average shot length of every movie. Visual disturbance is defined as a measure of motion content in a clip. Next we use color, audio and cinematic principles for further classification into comedy, horror drama/other and movies containing explosions and gunfire. This work is a step towards automatically building and updating a video database, thus resulting in minimum human intervention. Other potential applications include browsing and retrieval of videos on the Internet (video-on-demand), video libraries, and rating of movies.


title: Multivariate Short-Term Traffic Flow Forecasting Using Time-Series Analysis

abstract: Existing time-series models that are used for short-term traffic condition forecasting are mostly univariate in nature. Generally, the extension of existing univariate time-series models to a multivariate regime involves huge computational complexities. A different class of time-series models called structural time-series model (STM) (in its multivariate form) has been introduced in this paper to develop a parsimonious and computationally simple multivariate short-term traffic condition forecasting algorithm. The different components of a time-series data set such as trend, seasonal, cyclical, and calendar variations can separately be modeled in STM methodology. A case study at the Dublin, Ireland, city center with serious traffic congestion is performed to illustrate the forecasting strategy. The results indicate that the proposed forecasting algorithm is an effective approach in predicting real-time traffic flow at multiple junctions within an urban transport network.


Localized immunity to the large roundworm Ascaris suum in swine


title: Therapy with Sodium Stibogluconate in Stearylamine-Bearing Liposomes Confers Cure against SSG-Resistant Leishmania donovani in BALB/c Mice

abstract: Background ::: Resistance of Leishmania donovani to pentavalent antimonials, the first-line treatment of visceral leishmaniasis (VL), has become a critical issue worldwide. Second-line and new drugs are also not devoid of limitations. Suitable drug-delivery systems can improve the mode of administration and action of the existing antimonials, thus increasing their clinical life.


title: Liquid Injectable Silicone: A Review of Its History, Immunology, Technical Considerations, Complications, and Potential

abstract: For over five decades, liquid injectable silicone has been used for soft-tissue augmentation. Its use has engendered polarized reactions from the public and from physicians. Adherents of this product tout its inert chemical structure, ease of use, and low cost. Opponents of silicone cite the many reports of complications, including granulomas, pneumonitis, and disfiguring nodules that are usually the result of large-volume injection and/or industrial grade or adulterated material. Unfortunately, as recently as 2006, reports in The New England Journal of Medicine and The New York Times failed to distinguish between the use of medical grade silicone injected by physicians trained in the microdroplet technique and the use of large volumes of industrial grade products injected by unlicensed or unskilled practitioners. This review separates these two markedly different procedures. In addition, it provides an overview of the chemical structure of liquid injectable silicone, the immunology of silicone reactions within the body, treatment for cosmetic improvement including human immunodeficiency virus lipoatrophy, technical considerations for its injection, complications seen following injections, and some considerations of the future for silicone soft-tissue augmentation.


title: Analysis and extension of spectral methods for nonlinear dimensionality reduction

abstract: Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions of sparse matrices. While these algorithms aim to preserve certain proximity relations on average, their embeddings are not explicitly designed to preserve local features such as distances or angles. In this paper, we show how to construct a low dimensional embedding that maximally preserves angles between nearby data points. The embedding is derived from the bottom eigenvectors of LLE and/or Laplacian eigenmaps by solving an additional (but small) problem in semidefinite programming, whose size is independent of the number of data points. The solution obtained by semidefinite programming also yields an estimate of the data's intrinsic dimensionality. Experimental results on several data sets demonstrate the merits of our approach.


title: The plastic human brain cortex.

abstract: Plasticity is an intrinsic property of the human brain and represents evolution’s invention to enable the nervous system to escape the restrictions of its own genome and thus adapt to environmental pressures, physiologic changes, and experiences. Dynamic shifts in the strength of preexisting connections across distributed neural networks, changes in task-related cortico-cortical and corticosubcortical coherence and modifications of the mapping between behavior and neural activity take place in response to changes in afferent input or efferent demand. Such rapid, ongoing changes may be followed by the establishment of new connections through dendritic growth and arborization. However, they harbor the danger that the evolving pattern of neural activation may in itself lead to abnormal behavior. Plasticity is the mechanism for development and learning, as much as a cause of pathology. The challenge we face is to learn enough about the mechanisms of plasticity to modulate them to achieve the best behavioral outcome for a given subject.


title: Inside the Black Box: How to Explain Individual Predictions of a Machine Learning Model : How to automatically generate insights on predictive model outputs, and gain a better understanding on how the model predicts each individual data point.

abstract: Machine learning models are becoming more and more powerful and accurate, but their good predictions usually come with a high complexity. Depending on the situation, such a lack of interpretability ...


title: Calibration method of robot base frame using procrustes analysis

abstract: A novel effective robot base frame calibration method using Procrustes Analysis is presented. The errors from the design, manufacture and assembly of robot in the mechanism cause the deviation of nominal base frame used by the robot controller and the actual mechanical base frame. The actual mechanical base frame can be established by external precision measurement. The theory of Procrustes Analysis is introduced into the analysis of the relation of the two base frames. In order to make sure the rotation matrix is orthonormal and normalized, an optimal estimation of the relative rotation between the two base frames is solved out under the criterion of optimal Frobenius norm approximation. Meanwhile, by the Lagrange Multiplier method and Singular Value Decomposition (SVD), an orthonormal rotation matrix can be reached. Finally, experiments on a 6-DOF industrial robot researched and developed independently are performed with FARO ARM to demonstrate the superiority of the proposed method. Using this method, the relation of the two base frames is obtained, and it makes the motion planning possible. The calibration method is useful to other problem that formulated in the same mathematical form, such as multirobot coordination and robot hand-eye system calibration.


title: Male preferences for female waist-to-hip ratio and body mass index in the highlands of Papua New Guinea

abstract: One hundred men, living in three villages in a remote region of the Eastern Highlands of Papua New Guinea were asked to judge the attractiveness of photo- graphs of women who had undergone micrograft surgery to reduce their waist-to-hip ratios (WHRs). Micrograft sur- gery involves harvesting adipose tissue from the waist and reshaping the buttocks to produce a low WHR and an ''hourglass'' female figure. Men consistently chose postop- erative photographs as being more attractive than preop- erative photographs of the same women. Some women gained, and some lost weight, postoperatively, with result- ant changes in body mass index (BMI). However, changes in BMI were not related to men's judgments of attractive- ness. These results show that the hourglass female figure is rated as attractive by men living in a remote, indigenous community, and that when controlling for BMI, WHR plays a crucial role in their attractiveness judgments. Am J Phys Anthropol 000:000-000, 2010. V V C 2009 Wiley-Liss, Inc.


title: Mobile Based Secure Digital Wallet for Peer to Peer Payment System

abstract: E-commerce in today's conditions has the highest dependence on network infrastructure of banking. However, when the possibility of communicating with the Banking network is not provided, business activities will suffer. This paper proposes a new approach of digital wallet based on mobile devices without the need to exchange physical money or communicate with banking network. A digital wallet is a software component that allows a user to make an electronic payment in cash (such as a credit card or a digital coin), and hides the low-level details of executing the payment protocol that is used to make the payment. The main features of proposed architecture are secure awareness, fault tolerance, and infrastructure-less protocol.


title: Domain and Function: A Dual-Space Model of Semantic Relations and Compositions

abstract: Given appropriate representations of the semantic relations between carpenter and wood and between mason and stone (for example, vectors in a vector space model), a suitable algorithm should be able to recognize that these relations are highly similar (carpenter is to wood as mason is to stone; the relations are analogous). Likewise, with representations of dog, house, and kennel, an algorithm should be able to recognize that the semantic composition of dog and house, dog house, is highly similar to kennel (dog house and kennel are synonymous). It seems that these two tasks, recognizing relations and compositions, are closely connected. However, up to now, the best models for relations are significantly different from the best models for compositions. In this paper, we introduce a dual-space model that unifies these two tasks. This model matches the performance of the best previous models for relations and compositions. The dual-space model consists of a space for measuring domain similarity and a space for measuring function similarity. Carpenter and wood share the same domain, the domain of carpentry. Mason and stone share the same domain, the domain of masonry. Carpenter and mason share the same function, the function of artisans. Wood and stone share the same function, the function of materials. In the composition dog house, kennel has some domain overlap with both dog and house (the domains of pets and buildings). The function of kennel is similar to the function of house (the function of shelters). By combining domain and function similarities in various ways, we can model relations, compositions, and other aspects of semantics.


title: Digital Screen Media and Cognitive Development.

abstract: In this article, we examine the impact of digital screen devices, including television, on cognitive development. Although we know that young infants and toddlers are using touch screen devices, we know little about their comprehension of the content that they encounter on them. In contrast, research suggests that children begin to comprehend child-directed television starting at ∼2 years of age. The cognitive impact of these media depends on the age of the child, the kind of programming (educational programming versus programming produced for adults), the social context of viewing, as well the particular kind of interactive media (eg, computer games). For children <2 years old, television viewing has mostly negative associations, especially for language and executive function. For preschool-aged children, television viewing has been found to have both positive and negative outcomes, and a large body of research suggests that educational television has a positive impact on cognitive development. Beyond the preschool years, children mostly consume entertainment programming, and cognitive outcomes are not well explored in research. The use of computer games as well as educational computer programs can lead to gains in academically relevant content and other cognitive skills. This article concludes by identifying topics and goals for future research and provides recommendations based on current research-based knowledge.


title: Quality stabilisation of fresh herbs using a combined vacuum-microwave drying process

abstract: The quality of dried herbs mainly depends on three parameters: colour, aroma, and absence of off-flavour defects. The quality evaluation of dried products should always be related to the properties of the original fresh product. Especially convective hot-air drying (HAD) causes heat damage of the plant tissue and therefore affects the quality of the final product. The present study involves the investigation of vacuum-microwave drying (VMD) as a processing method for drying herbs. Colour, aroma, and off-flavour of vacuum-microwave dried parsley was evaluated and compared with corresponding properties of hot-air dried parsley (at 75 °C). VMD-parsley was greener in colour, exhibited a higher content of essential oils and less hay-like off-flavour defects than those prepared by HAD. In detail, L*a*b*-colour measurement revealed that VMD-samples had higher green hues directly after drying than those prepared by HAD. Also, the colour retention over 8 weeks, when stored in darkness, is significantly higher by using a vacuum-microwave drying process in comparison with convective drying. Colour degradation was observed in all samples, but was faster for hot-air dried parsley. Therefore a visible colour improvement of dried herbs can be obtained, but the coherence of final water content with colour stability must be considered. As can be seen, the rate of chlorophyll degradation increases with an increase of water content in the final product. Investigation of the impact of drying methods on the flavour quality showed, that VMD preserved over 90 % of the essential oils during processing compared with only 30 % by HAD. Furthermore VMD-samples were rated better than HAD-samples by a sensory panel for parsley-like, green-grassy and hay/straw-like odour impressions as well as the general impression related to fresh parsley.


title: A case control study of HPV and abnormal cervical cytology among HIV infected and non infected females.

abstract: Introduction : HPV is a known cause of abnormal cervical cytology and cervical cancer. The prevalence of cervical cancer among HIV infected women is higher. This study compared the prevalence of HPV and abnormal cervical cytology between HIV positive and negative females. Methods : In this case-control study, 100 consecutive HIV-positive females were included as cases and 75 HIV-negative females were taken as controls. Cervical smears and scrapes were taken for Pap smear and HPV DNA by PCR and compared. Results : Prevalence of HPV among HIV-positive females (35%; n=35) was higher than HIV-negatives (8%; n=6) and was statistically significant (<0.05). The incidence of LSIL, HSIL and ASCUS in the HIV-positive was 16%, 3%, 2% respectively and 5%, 2.5%, 1.3% respectively in the HIV-negative. LSIL lesion showed statistical significance (p <0.05) but HSIL and ASCUS lesions had no such significance (p values 0.65 and 0.56 respectively). Conclusions : A strong correlation was found between HPV positivity and abnormal cervical lesions diagnosed on cytology, especially among the HIV positive population especially LSILs. Therefore, screening of cervical cancer should include HPV identification and cervical cytology, especially among HIV positive women, so that prompt and appropriate treatment of HIV would help in reducing the prevalence of cervical cancer.


title: A Style-Aware Content Loss for Real-time HD Style Transfer

abstract: Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a style or shows no benefit from more images. Moreover, previous work has relied on a direct comparison of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can introduce an extra bias, since it has been assembled without artistic consideration. To circumvent these issues, we propose a style-aware content loss, which is trained jointly with a deep encoder-decoder network for real-time, high-resolution stylization of images and videos. We propose a quantitative measure for evaluating the quality of a stylized image and also have art historians rank patches from our approach against those from previous work. These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.