Whitening Black-Box Neural Networks. Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks – we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.

Question: What is a neural network?


Face Detection Using Convolutional Neural Networks and Gabor Filters. This paper proposes a method for detecting facial regions by combining a Gabor filter and a convolutional neural network. The first stage uses the Gabor filter which extracts intrinsic facial features. As a result of this transformation we obtain four subimages. The second stage of the method concerns the application of the convolutional neural network to these four images. The approach presented in this paper yields better classification performance in comparison to the results obtained by the convolutional neural network alone.

Question: What is a gabor filter?


3D face matching and registration based on hyperbolic Ricci flow. 3D surface matching is fundamental for shape analysis. As a powerful method in geometric analysis, Ricci flow can flexibly design metrics by prescribed target curvature. In this paper we describe a novel approach for matching surfaces with complicated topologies based on hyperbolic Ricci flow. For surfaces with negative Euler characteristics, such as a human face with holes (eye contours), the canonical hyperbolic metric is conformal to the original and can be efficiently computed. Then the surface can be canonically decomposed to hyperbolic hexagons. By matching the corresponding hyperbolic hexagons, the matching between surfaces can be easily established. Compared to existing methods, hyperbolic Ricci flow induces diffeomorphisms between surfaces with complicated topologies with negative Euler characteristics, while avoiding singularities. Furthermore, all the boundaries are intrinsically mapped to hyperbolic lines as alignment constraints. Finally, we demonstrate the applicability of this intrinsic shape representation for 3D face matching and registration.

Question: What is the difference between a hyperbolic hexagon and a hyperbolic triangle?


Community-Affiliation Graph Model for Overlapping Network Community Detection. One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. Identifying such overlapping communities is crucial for the understanding the structure as well as the function of real-world networks. Even though community structure in networks has been widely studied in the past, practically all research makes an implicit assumption that overlaps between communities are less densely connected than the non-overlapping parts themselves. Here we validate this assumption on 6 large scale social, collaboration and information networks where nodes explicitly state their community memberships. By examining such ground-truth communities we find that the community overlaps are more densely connected than the non-overlapping parts, which is in sharp contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Practically all existing community detection methods fail to detect communities with dense overlaps. We propose Community-Affiliation Graph Model, a model-based community detection method that builds on bipartite node-community affiliation networks. Our method successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.

Question: What is the community affiliation graph model?


Mesh repair with user-friendly topology control. Limitations of current 3D acquisition technology often lead to polygonal meshes exhibiting a number of geometrical and topological defects which prevent them from widespread use. In this paper we present a new method for model repair which takes as input an arbitrary polygonal mesh and outputs a valid two-manifold triangle mesh. Unlike previous work, our method allows users to quickly identify areas with potential topological errors and to choose how to fix them in a userfriendly manner. Key steps of our algorithm include the conversion of the input model into a set of voxels, the use of morphological operators to allow the user to modify the topology of the discrete model, and the conversion of the corrected voxel set back into a two-manifold triangle mesh. Our experiments demonstrate that the proposed algorithm is suitable for repairing meshes of a large class of shapes.

Question: How to repair a mesh?


Web Engineering: A New Discipline for Development of Web-Based Systems. In most cases, development of Web-based systems has been ad hoc, lacking systematic approach and quality control and assurance procedures. Hence, there is now legitimate and growing concern about the manner in which Web-based systems are developed and their long-term quality and integrity. Web Engineering, an emerging new discipline, advocates a process and a systematic approach to development of high quality Web-based systems. It promotes the establishment and use of sound scientific, engineering and management principles, and disciplined and systematic approaches to development, deployment and maintenance of Web-based systems. This paper gives an introductory overview on Web Engineering. It presents the principles and roles of Web Engineering, assesses the similarities and differences between development of traditional software and Web-based systems, identifies key Web engineering activities and reviews some of the ongoing work in this area. It also highlights the prospects of Web engineering and the areas that need further study.

Question: What is web engineering?


Big Data Security: Survey on Frameworks and Algorithms. Technology today has progressed to an extent wherein collection of data is possible for every granular aspect of a business, in real time. Electronic devices, power grids and modern software all generate huge volumes of data, which is in the form of petabytes, exabytes and zettabytes. It is important to secure existing big data environments due to increasing threats of breaches and leaks from confidential data and increased adoption of cloud technologies due to the ability of buying processing power and storage on-demand. This is exposing traditional and new data warehouses and repositories to the outside world and at the risk of being compromised to hackers and malicious outsiders and insiders. In this paper the current big data scenario has been summarized along with challenges faced and security issues that need attention. Also some existing approaches have been described to illustrate current and standard directions to solving the issues.

Question: What is big data?


Enterprise Architecture design for ensuring strategic business IT alignment (integrating SAMM with TOGAF 9.1). Strategic business IT (Information Technology) alignment is one of the main objectives that is achieved from the implementation of Enterprise Architecture (EA) in an organization. EA helps organizations to define architecture of business, information systems and technology that capable for aligning business strategy with IT organizations, through the development of business models, business strategy, business processes and organizations that aligned with infrastructure, applications and IT organizations. A good design of Enterprise Architecture should consider various viewpoints of IT alignment with the organization's business needs. This paper provides a solution how to design Enterprise Architecture which provides guarantee for strategic business IT alignment that is designed through the integration of SAMM component with TOGAF 9.1 metamodel.

Question: How to design enterprise architecture for strategic business IT alignment?


Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm. In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and selecting salient features. The main goal of using MGSA-SVM is to decrease the number of features and to improve the SVM classification accuracy. Finally, the selected features and the tuned SVM classifier are used for detecting tumors. The experimental results show that the proposed method is able to optimize both feature selection and the SVM parameters for the breast cancer tumor detection.

Question: How to detect breast cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm?


Data Gloves for Sign Language Recognition System. Communication between deaf-dumb and a normal person have always been a challenging task . About 9 billion people in the world come into this category which is quite large number to be ignored. As deaf-dumb people use sign language for their communication which is difficult to understand by the normal people. This paper aims at eradicating the communication barrier between them by developing an embedded system which will translate the hand gestures into

Question: What is the sign language used by deaf-dumb people?


Multiuser Resource Allocation for Mobile-Edge Computation Offloading. Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we consider resource allocation in a MECO system comprising multiple users that time share a single edge cloud and have different computation loads. The optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under constraint on computation latency and for both the cases of infinite and finite edge cloud computation capacities. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Computing the threshold requires iterative computation. To reduce the complexity, a sub-optimal resource-allocation algorithm is proposed and shown by simulation to have close-to-optimal performance.

Question: How to allocate resources in a MECO system?


Joint Text Embedding for Personalized Content-based Recommendation. Learning a good representation of text is key to many recommendation applications. Examples include news recommendation where texts to be recommended are constantly published everyday. However, most existing recommendation techniques, such as matrix factorization based methods, mainly rely on interaction histories to learn representations of items. While latent factors of items can be learned e'ectively from user interaction data, in many cases, such data is not available, especially for newly emerged items. In this work, we aim to address the problem of personalized recommendation for completely new items with text information available. We cast the problem as a personalized text ranking problem and propose a general framework that combines text embedding with personalized recommendation. Users and textual content are embedded into latent feature space. Œe text embedding function can be learned end-to-end by predicting user interactions with items. To alleviate sparsity in interaction data, and leverage large amount of text data with liŠle or no user interactions, we further propose a joint text embedding model that incorporates unsupervised text embedding with a combination module. Experimental results show that our model can signi€cantly improve the e'ectiveness of recommendation systems on real-world datasets.

Question: How to learn a good representation of text?


Sensitivity analysis of a 1 to 18 GHz broadband DRGH antenna. In this paper some properties of a 1-18 GHz double ridged guide horn antenna (DRGH) with a feeding section including coaxial input and a back shorting plate are rigorously investigated. Most desired electromagnetic characteristics of this antenna is achieved by empirically finding sizes for different parameters, however there is no explanation for the effect of most of them in open literature. In order to have a clear idea of the effects of different parameters, a 1-18 GHz DRGH has been simulated with HFSS. It is understood from the results that the parameters near feeding point such as the initial distance between ridges, the distance between the center of the probe and the cavity, and the radius of the inserted probe play a significant role in controlling VSWR and gain and in shaping the radiation pattern for high frequencies

Question: What is the effect of different parameters on the radiation pattern of a DRGH?


Preimages for Step-Reduced SHA-2. In this paper, we present preimage attacks on up to 43step SHA-256 (around 67% of the total 64 steps) and 46-step SHA-512 (around 57.5% of the total 80 steps), which significantly increases the number of attacked steps compared to the best previously published preimage attack working for 24 steps. The time complexities are 2, 2 for finding pseudo-preimages and 2, 2 compression function operations for full preimages. The memory requirements are modest, around 2 words for 43-step SHA-256 and 46-step SHA-512. The pseudo-preimage attack also applies to 43-step SHA-224 and SHA-384. Our attack is a meet-in-the-middle attack that uses a range of novel techniques to split the function into two independent parts that can be computed separately and then matched in a birthday-style phase.

Question: What is the time complexity of the preimage attack?


A wideband unity-gain buffer in 0.13-μm CMOS. In this paper, an ultra wideband analog voltage-mode buffer is presented which can drive a load impedance of 50 Ω. The presented feedback-based buffer uses a compound amplifier which is a parallel combination of a high-DC gain operational amplifier and a operation transconductance amplifier to achieve a high unity gain bandwidth. A proof-of-concept prototype is designed and fabricated in a 0.13 μm CMOS process. The simulation and measurement results of the proposed buffer are in good agreement. The prototype buffer circuit consumes 7.34 mW from a 1.3-V supply, while buffering a 2 GHz sinusoidal input signal with a 0.4 V peak-to-peak (Vpp) amplitude and driving an AC-coupled 50-Ω load.

Question: What is a wideband unity-gain buffer?


Collecting and analyzing qualitative data for system dynamics: methods and models. System dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most influential authors. Data gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This paper describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate

Question: What is system dynamics?


ISCEV standard for clinical visual evoked potentials: (2016 update). Visual evoked potentials (VEPs) can provide important diagnostic information regarding the functional integrity of the visual system. This document updates the ISCEV standard for clinical VEP testing and supersedes the 2009 standard. The main changes in this revision are the acknowledgment that pattern stimuli can be produced using a variety of technologies with an emphasis on the need for manufacturers to ensure that there is no luminance change during pattern reversal or pattern onset/offset. The document is also edited to bring the VEP standard into closer harmony with other ISCEV standards. The ISCEV standard VEP is based on a subset of stimulus and recording conditions that provide core clinical information and can be performed by most clinical electrophysiology laboratories throughout the world. These are: (1) Pattern-reversal VEPs elicited by checkerboard stimuli with large 1 degree (°) and small 0.25° checks. (2) Pattern onset/offset VEPs elicited by checkerboard stimuli with large 1° and small 0.25° checks. (3) Flash VEPs elicited by a flash (brief luminance increment) which subtends a visual field of at least 20°. The ISCEV standard VEP protocols are defined for a single recording channel with a midline occipital active electrode. These protocols are intended for assessment of the eye and/or optic nerves anterior to the optic chiasm. Extended, multi-channel protocols are required to evaluate postchiasmal lesions.

Question: What is the ISCEV standard for clinical visual evoked potentials?


HarmTrace : Automatic functional harmonic analysis. Music scholars have been intensively studying tonal harmony for centuries, yielding numerous theories and models. Unfortunately, a large number of these theories are formulated in a rather informal fashion and lack mathematical precision. In this article we present HarmTrace, a functional model of Western tonal harmony, which builds on well-known theories of tonal harmony. In contrast to many other theories which remain purely theoretical, we present an implemented system that is evaluated empirically. Given a sequence of symbolic chord labels, HarmTrace automatically derives the harmonic relations between chords. For this, we use advanced functional programming techniques which are uniquely available in the Haskell programming language. We show that our system is fast, easy to modify and maintain, is robust against noisy data, and returns harmonic analyses that comply with Western tonal harmony theory.

Question: What is functional programming?


Enhancing SDN security for IoT-related deployments through blockchain. The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.

Question: What is the difference between SDN and IoT?


Warehousing and Protecting Big Data: State-Of-The-Art-Analysis, Methodologies, Future Challenges. This paper proposes a comprehensive critical survey on the issues of warehousing and protecting big data, which are recognized as critical challenges of emerging big data research. Indeed, both are critical aspects to be considered in order to build truly, high-performance and highly-flexible big data management systems. We report on state-of-the-art approaches, methodologies and trends, and finally conclude by providing open problems and challenging research directions to be considered by future efforts.

Question: What is a warehousing and protecting big data?


A Dual-Band Dual-Polarized Nested Vivaldi Slot Array With Multilevel Ground Plane. In this paper, we present a systematic approach to the design, optimization and characterization of a broadband 5:1 bandwidth (0.8 to 4.0 GHz) antenna subarray. The array element is an optimized-taper antipodal Vivaldi slot with a bandwidth of 2.5:1. Two such elements of different sizes and with 0.4 GHz (10% of the highest frequency) overlapping bandwidths are arrayed in a nested lattice above a multilevel ground plane that shields the feeds and electronics. Return loss, radiation patterns, cross-polarization and mutual coupling are measured from 0.5–5.0 GHz. This array demonstrates E plane patterns with 50 and 45 3-dB beamwidths in the lower and upper frequency bands, respectively. The coupling between the elements is characterized for different relative antenna positions in all three dimensions.

Question: What is a dual-band dual-polarized vivaldi slot array with a multilevel ground plane?


Time is of the essence: improving recency ranking using Twitter data. Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.

Question: How to improve the ranking of fresh URLs?


The classroom as a dashboard: co-designing wearable cognitive augmentation for K-12 teachers. When used in classrooms, personalized learning software allows students to work at their own pace, while freeing up the teacher to spend more time working one-on-one with students. Yet such personalized classrooms also pose unique challenges for teachers, who are tasked with monitoring classes working on divergent activities, and prioritizing help-giving in the face of limited time. This paper reports on the co-design, implementation, and evaluation of a wearable classroom orchestration tool for K-12 teachers: mixed-reality smart glasses that augment teachers' realtime perceptions of their students' learning, metacognition, and behavior, while students work with personalized learning software. The main contributions are: (1) the first exploration of the use of smart glasses to support orchestration of personalized classrooms, yielding design findings that may inform future work on real-time orchestration tools; (2) Replay Enactments: a new prototyping method for real-time orchestration tools; and (3) an in-lab evaluation and classroom pilot using a prototype of teacher smart glasses (Lumilo), with early findings suggesting that Lumilo can direct teachers' time to students who may need it most.

Question: What is the first large-scale Canadian peacekeeping mission?


Characterising the Behaviour of IEEE 802.11 Broadcast Transmissions in Ad Hoc Wireless LANs. This paper evaluates the performance of the IEEE 802.11 broadcast traffic under both saturation and nonsaturation conditions. The evaluation highlights some important characteristics of IEEE 802.11 broadcast traffic as compared to corresponding unicast traffic. Moreover, it underlines the inaccuracy of the broadcast saturation model proposed by Ma and Chen due to the absence of backoff counter freeze process when channel is busy. Computer simulations are used to validate the accuracy of the new model and demonstrate the importance of capturing the freezing of backoff counter in the analytical study of IEEE 802.11 broadcast.

Question: What is the difference between broadcast and unicast traffic?


Análisis de la Simplificación de Expresiones Numéricas en Español mediante un Estudio Empírico. In this paper we present the results of an empirical study carried out on a parallel corpus of original and manually simplified texts in Spanish and a subsequent survey, with the aim of targeting simplification operations concerning numerical expressions. For the purpose of the study, a "numerical expression" is understood as any phrase expressing quantity possibly modified with a numerical hedge, such as almost a quarter. Data is analyzed both in context and in isolation, and attention is paid to the difference the target reader makes to simplification. Our future work aims at computational implementation of the transformation rules extracted so far.

Question: What is the difference between a "numerical expression" and a "numerical hedge"?


Recurrent Network Models for Human Dynamics. We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture. The ERD model is a recurrent neural network that incorporates nonlinear encoder and decoder networks before and after recurrent layers. We test instantiations of ERD architectures in the tasks of motion capture (mocap) generation, body pose labeling and body pose forecasting in videos. Our model handles mocap training data across multiple subjects and activity domains, and synthesizes novel motions while avoiding drifting for long periods of time. For human pose labeling, ERD outperforms a per frame body part detector by resolving left-right body part confusions. For video pose forecasting, ERD predicts body joint displacements across a temporal horizon of 400ms and outperforms a first order motion model based on optical flow. ERDs extend previous Long Short Term Memory (LSTM) models in the literature to jointly learn representations and their dynamics. Our experiments show such representation learning is crucial for both labeling and prediction in space-time. We find this is a distinguishing feature between the spatio-temporal visual domain in comparison to 1D text, speech or handwriting, where straightforward hard coded representations have shown excellent results when directly combined with recurrent units [31].

Question: What is the difference between a recurrent neural network and a long short term memory network?


ISO/IEC 27000, 27001 and 27002 for Information Security Management. With the increasing significance of information technology, there is an urgent need for adequate measures of information security. Systematic information security management is one of most important initiatives for IT management. At least since reports about privacy and security breaches, fraudulent accounting practices, and attacks on IT systems appeared in public, organizations have recognized their responsibilities to safeguard physical and information assets. Security standards can be used as guideline or framework to develop and maintain an adequate information security management system (ISMS). The standards ISO/IEC 27000, 27001 and 27002 are international standards that are receiving growing recognition and adoption. They are referred to as "common language of organizations around the world" for information security [1]. With ISO/IEC 27001 companies can have their ISMS certified by a third-party organization and thus show their customers evidence of their security measures.

Question: What is the common language of organizations around the world?


Demystifying Learning Analytics in Personalised Learning. This paper presents learning analytics as a mean to improve students' learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper's contribution to knowledge is in considering personalised learning within the context framework of learning analytics.

Question: What is the difference between learning analytics and personalised learning?


Performance Evaluation and Optimization of Communication Infrastructure for the Next Generation Air Transportation System. Automatic dependent surveillance-broadcast (ADS-B) is one of the fundamental surveillance technologies to improve the safety, capacity, and efficiency of the national airspace system. ADS-B shares its frequency band with current radar systems that use the same 1,090 MHz band. The coexistence of radar systems and ADS-B systems is a key issue to detect and resolve conflicts in the next generation air transportation system (NextGen). This paper focuses on the performance evaluation of ADS-B with existing radar systems and performance optimization of ADS-B systems to improve the safety and efficiency of conflict detection and resolution in NextGen. We have developed a simulation environment which models the complex interplay among the air traffic load, the radar systems, the ADS-B systems, and the wireless channel. A simple model is used to derive an analytical expression for a performance metric of ADS-B. This model is then used to design an adaptive ADS-B protocol for maximizing the information coverage while guaranteeing reliable and timely communication in air traffic surveillance networks. Simulation results show that the effect of ADS-B interference on the current radar system is negligible. The operational ability of ADS-B meets the performance requirements of conflict detection and resolution in air traffic control. However, upgrades are required in the current radar system for operation within an ADS-B environment since the current radars can significantly degrade the ADS-B performance. Numerical results indicate that the proposed adaptive protocol has the potential to improve the performance of conflict detection and resolution in air traffic control.

Question: What is the performance of ADS-B?


CONTEXT-CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS. We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.

Question: What is a semi-supervised learning approach?


High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis. High-throughput genotyping platforms play important roles in plant genomic studies. Cotton (Gossypium spp.) is the world's important natural textile fiber and oil crop. Upland cotton accounts for more than 90% of the world's cotton production, however, modern upland cotton cultivars have narrow genetic diversity. The amounts of genomic sequencing and re-sequencing data released make it possible to develop a high-quality single nucleotide polymorphism (SNP) array for intraspecific genotyping detection in cotton. Here we report a high-throughput CottonSNP80K array and its utilization in genotyping detection in different cotton accessions. 82,259 SNP markers were selected from the re-sequencing data of 100 cotton cultivars and used to produce the array on the Illumina Infinium platform. 77,774 SNP loci (94.55%) were successfully synthesized on the array. Of them, 77,252 (99.33%) had call rates of >95% in 352 cotton accessions and 59,502 (76.51%) were polymorphic loci. Application tests using 22 cotton accessions with parent/F1 combinations or with similar genetic backgrounds showed that CottonSNP80K array had high genotyping accuracy, good repeatability, and wide applicability. Phylogenetic analysis of 312 cotton cultivars and landraces with wide geographical distribution showed that they could be classified into ten groups, irrelevant of their origins. We found that the different landraces were clustered in different subgroups, indicating that these landraces were major contributors to the development of different breeding populations of modern G. hirsutum cultivars in China. We integrated a total of 54,588 SNPs (MAFs >0.05) associated with 10 salt stress traits into 288 G. hirsutum accessions for genome-wide association studies (GWAS), and eight significant SNPs associated with three salt stress traits were detected. We developed CottonSNP80K array with high poly

Question: What is the difference between upland and modern cotton?


Pattern Recognition. In this paper a novel reconstruction method is presented that uses the topological relationship of detected image features to create a highly abstract but semantically rich 3D model of the reconstructed scenes. In the first step, a combined image-based reconstruction of points and lines is performed based on the current state of art structure from motion methods. Subsequently, connected planar threedimensional structures are reconstructed by a novel method that uses the topological relationships between the detected image features. The reconstructed 3D models enable a simple extraction of geometric shapes, such as rectangles, in the scene.

Question: What is a 3D model?


Music and language side by side in the brain: a PET study of the generation of melodies and sentences.. Parallel generational tasks for music and language were compared using positron emission tomography. Amateur musicians vocally improvised melodic or linguistic phrases in response to unfamiliar, auditorily presented melodies or phrases. Core areas for generating melodic phrases appeared to be in left Brodmann area (BA) 45, right BA 44, bilateral temporal planum polare, lateral BA 6, and pre-SMA. Core areas for generating sentences seemed to be in bilateral posterior superior and middle temporal cortex (BA 22, 21), left BA 39, bilateral superior frontal (BA 8, 9), left inferior frontal (BA 44, 45), anterior cingulate, and pre-SMA. Direct comparisons of the two tasks revealed activations in nearly identical functional brain areas, including the primary motor cortex, supplementary motor area, Broca's area, anterior insula, primary and secondary auditory cortices, temporal pole, basal ganglia, ventral thalamus, and posterior cerebellum. Most of the differences between melodic and sentential generation were seen in lateralization tendencies, with the language task favouring the left hemisphere. However, many of the activations for each modality were bilateral, and so there was significant overlap. While clarification of this overlapping activity awaits higher-resolution measurements and interventional assessments, plausible accounts for it include component sharing, interleaved representations, and adaptive coding. With these and related findings, we outline a comparative model of shared, parallel, and distinctive features of the neural systems supporting music and language. The model assumes that music and language show parallel combinatoric generativity for complex sound structures (phonology) but distinctly different informational content (semantics).

Question: What is the difference between music and language?


Bangla license plate reader for metropolitan cities of Bangladesh using template matching. A simple algorithm for license plate reader for metropolitan cities in Bangladesh has been proposed in this paper. We have made use of a feature of Bangla script called "Matra" which joins the characters of a word together. Since the various words of a standard Bangladeshi license plate also have Matra, we have proposed a method where these words are segmented as single connected components and later recognized through template matching of words. There are also numbers and single letters in the number plate which are segmented and recognized as single characters. We have demonstrated that if we exploit the feature of Matra this way, the complexity of the algorithm is decreased, and the proposed system works smoothly for images taken under various conditions.

Question: How to segment words in Bangla?


Automatic Personality and Interaction Style Recognition from Facebook Profile Pictures. In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.

Question: How to recognize personality and interaction style from profile pictures?


Expression of stressful experiences through writing: effects of a self-regulation manipulation for pessimists and optimists.. This study assessed the effectiveness of a writing task designed to foster self-regulatory coping with stressful experiences to reduce medical clinic visits and to promote adjustment. Students entering college (N = 122) who were classified as optimists or pessimists by using a dispositional optimism measure participated in a self-regulation task (expressing thoughts and feelings about entering college and then formulating coping plans), a disclosure task (expressing thoughts and feelings only), or a control task (writing about trivial topics) for 3 weekly writing sessions. Among optimists, both the self-regulation task and the disclosure task reduced illness-related clinic visits during the following month; among pessimists, only the self-regulation task reduced clinic visits. In general, the self-regulation task beneficially affected mood state and college adjustment whereas the disclosure task increased grade point averages.

Question: What is the effect of a self-regulation task on medical clinic visits?


Teaching Computer Organization and Architecture Using Simulation and FPGA Applications. This paper presents the design concepts and realization of incorporating micro-operation simulation and FPGA implementation into a teaching tool for computer organization and architecture. This teaching tool helps computer engineering and computer science students to be familiarized practically with computer organization and architecture through the development of their own instruction set, computer programming and interfacing experiments. A two-pass assembler has been designed and implemented to write assembly programs in this teaching tool. In addition to the microoperation simulation, the complete configuration can be run on Xilinx Spartan-3 FPGA board. Such implementation offers good code density, easy customization, easily developed software, small area, and high performance at low cost.

Question: What is the teaching tool for computer organization and architecture?


Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word vectors as input as is often done, we directly apply CNN to high-dimensional text data, which leads to directly learning embedding of small text regions for use in classification. In addition to a straightforward adaptation of CNN from image to text, a simple but new variation which employs bag-ofword conversion in the convolution layer is proposed. An extension to combine multiple convolution layers is also explored for higher accuracy. The experiments demonstrate the effectiveness of our approach in comparison with state-of-the-art methods.

Question: What is the difference between convolutional neural network and recurrent neural network?


Less is More: Nyström Computational Regularization. We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nyström Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.

Question: What is Nyström regularization?


Relationship Between Visual-Motor Integration , Eye-Hand Coordination , and Quality of Handwriting. If the influence of visual-motor integration (copying forms) on the quality of handwriting has been widely investigated, the influence of eye-hand coordination (tracing item) has been less well analyzed. The Concise Assessment Scale for Children's Handwriting (BHK), the Developmental Test of Visual Perception (DTVP-2), and the section "Manual Dexterity" of the Movement Assessment Battery for Children (M-ABC) were administered to a group of second grade children (N = 75; 8.1-year-olds). The association of visual-motor integration and eye-hand coordination are predictive of the quality of handwriting (p < . 001). These two skills should be taken into consideration when children are referred to occupational therapy for difficulties in handwriting.

Question: What is the relationship between visual-motor integration and eye-hand coordination?


Real Time Video Data Mining for Surveillance Video Streams. We extend our previous work [1] of the general framework for video data mining to further address the issue such as how to mine video data. To extract motions, we use an accumulation of quantized pixel differences among all frames in a video segment. As a result, the accumulated motions of segment are represented as a two dimensional matrix. Further, we develop how to capture the location of motions occurring in a segment using the same matrix generated for the calculation of the amount. We study how to cluster those segmented pieces using the features (the amount and the location of motions) we extract by the matrix above. We investigate an algorithm to find whether a segment has normal or abnormal events by clustering and modeling normal events, which occur mostly. In addition to deciding normal or abnormal, the algorithm computes Degree of Abnormality of a segment, which represents to what extent a segment is distant to the existing segments in relation with normal events. Our experimental studies indicate that the proposed techniques are promising.

Question: How to extract motions from a video stream?


Luhn Revisited: Significant Words Language Models. Users tend to articulate their complex information needs in only a few keywords, making underspecified statements of request the main bottleneck for retrieval effectiveness. Taking advantage of feedback information is one of the best ways to enrich the query representation, but can also lead to loss of query focus and harm performance in particular when the initial query retrieves only little relevant information when overfitting to accidental features of the particular observed feedback documents. Inspired by the early work of Luhn [23], we propose significant words language models of feedback documents that capture all, and only, the significant shared terms from feedback documents. We adjust the weights of common terms that are already well explained by the document collection as well as the weight of rare terms that are only explained by specific feedback documents, which eventually results in having only the significant terms left in the feedback model.  Our main contributions are the following. First, we present significant words language models as the effective models capturing the essential terms and their probabilities. Second, we apply the resulting models to the relevance feedback task, and see a better performance over the state-of-the-art methods. Third, we see that the estimation method is remarkably robust making the models in- sensitive to noisy non-relevant terms in feedback documents. Our general observation is that the significant words language models more accurately capture relevance by excluding general terms and feedback document specific terms.

Question: What is the difference between the Canadian Armed Forces and the Canadian Army?


You are what you say: privacy risks of public mentions. In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.

Question: How can I protect my privacy?


Building Enterprise Systems Infrastructure Flexibility as Enabler of Organisational Agility: Empirical Evidence. Enterprise systems (ES) that capture the most advanced developments of information technology are becoming common fixtures in most organisations. However, how ES affect organizational agility (OA) has been less researched and the existing research remains equivocal. From the perspective that ES can positively contribute to OA, this research via theory-based model development and rigorous empirical investigation of the proposed model, has bridged significant research gaps and provided empirical evidence for, and insights into, the effect of ES on OA. The empirical results based on data collected from 179 large organizations in Australia and New Zealand which have implemented and used ES for at least one year show that organisations can achieve agility out of their ES in two ways: by developing ES technical competences to build ES-enabled capabilities that digitise their key sensing and responding processes; and when ES-enabled sensing and responding capabilities are aligned in relatively turbulent environment.

Question: What is the effect of enterprise systems on organizational agility?


MagicFuzzer: Scalable deadlock detection for large-scale applications. We present MagicFuzzer, a novel dynamic deadlock detection technique. Unlike existing techniques to locate potential deadlock cycles from an execution, it iteratively prunes lock dependencies that each has no incoming or outgoing edge. Combining with a novel thread-specific strategy, it dramatically shrinks the size of lock dependency set for cycle detection, improving the efficiency and scalability of such a detection significantly. In the real deadlock confirmation phase, it uses a new strategy to actively schedule threads of an execution against the whole set of potential deadlock cycles. We have implemented a prototype and evaluated it on large-scale C/C++ programs. The experimental results confirm that our technique is significantly more effective and efficient than existing techniques.

Question: What is a deadlock?


Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation. In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

Question: What is the difference between the proposed method and other methods?


Content-based audio segmentation using support vector machines. Audio exists at everywhere, but is often out-of-order. It is necessary to arrange them into regularized classes in order to use them more easily. It is also useful, especially in video content analysis, to segment an audio stream according to audio types. In this paper, we present our work in applying support vector machines (SVMs) in audio segmentation and classification. Five audio classes are considered: silence, music, background sound, pure speech, and non-pure speech which includes speech over music and speech over noise. A SVM learns optimal class boundaries from training data to best separate between two classes. A sound clip is segmented by classifying each sub-clip of one second into one of these five classes. Experiments on a database composed of clips of 14870 seconds in total length show that the average accuracy rate for the SVM method is much better than that of the traditional Euclidean distance based (nearest neighbor) method.

Question: How to segment audio?


Image-based Human Character Modeling and Reconstruction for Virtual Reality Exposure Therapy. We design a modeling platform to construct virtual environments for Virtual Reality Exposure Therapy. In this platform, the scene is built by image-based reconstruction followed by manual refinement (when needed). The main technical contribution of this paper is on enhanced human character modeling, which is done through reconstructing 3D face geometry, body shape and posture from provided 2D photos.

Question: How to build a virtual environment for virtual reality exposure therapy?


Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band. The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown.

Question: What is the state of the art of millimeter-wave technology for automotive radar applications?


3D Object Proposals for Accurate Object Class Detection. In the Supplementary Material we first provide a description of the architecture of our object detection network. We then describe some details about our road estimation method. We also introduce the class-agnostic variant of our approach. We provide comprehensive results on object detection and orientation estimation, and recall statistics for both 2D and 3D box proposals. Finally, a visualization of success cases as well as failure modes are presented, followed by a detailed error analysis of our detector.

Question: What is the architecture of the object detection network?


Programs, life cycles, and laws of software evolution. By classifying programs according to their relationship to the environment in which they are executed, the paper identifies the sources of evolutionary pressure on computer applications and programs and shows why this results in a process of never ending maintenance activity. The resultant life cycle processes are then briefly discussed. The paper then introduces laws of Program Evolution that have been formulated following quantitative studies of the evolution of a number of different systems. Finally an example is provided of the application of Evolution Dynamics models to program release planning.

Question: What is the life cycle of a program?


Neuroplasticity in the context of motor rehabilitation after stroke. Approximately one-third of patients with stroke exhibit persistent disability after the initial cerebrovascular episode, with motor impairments accounting for most poststroke disability. Exercise and training have long been used to restore motor function after stroke. Better training strategies and therapies to enhance the effects of these rehabilitative protocols are currently being developed for poststroke disability. The advancement of our understanding of the neuroplastic changes associated with poststroke motor impairment and the innate mechanisms of repair is crucial to this endeavor. Pharmaceutical, biological and electrophysiological treatments that augment neuroplasticity are being explored to further extend the boundaries of poststroke rehabilitation. Potential motor rehabilitation therapies, such as stem cell therapy, exogenous tissue engineering and brain–computer interface technologies, could be integral in helping patients with stroke regain motor control. As the methods for providing motor rehabilitation change, the primary goals of poststroke rehabilitation will be driven by the activity and quality of life needs of individual patients. This Review aims to provide a focused overview of neuroplasticity associated with poststroke motor impairment, and the latest experimental interventions being developed to manipulate neuroplasticity to enhance motor rehabilitation.

Question: What is the role of neuroplasticity in motor rehabilitation after stroke?


The Effect of Synonym Substitution on Search Results. Synonyms or other semantic associations can be used in web search in query substitution to improve or augment the query to retrieve more relevant search results. The value of substitution depends on how well the synonyms preserve semantic meaning, as any attrition in meaning can result in semantic drift of query results. Many synonyms are not synonyms in the traditional, thesaurus sense, but are semantic associations discovered automatically from online data, with the risk of semantic drift in substitution. This discovery of synonyms or other semantic associations arises from different methods applied over web search logs, and in this paper we review the candidate synonym pairs of words or phrases generated from three different methods applied over the same web search logs. The suitability of the candidate synonym pairs for the purpose of query substitution is evaluated in an experiment where 68 subjects assessed the search results generated by both the original query and the substituted query. It was found that two of the discovery methods returned significantly worse results with the substitution than were returned by the original query for the majority of queries, with only around 20-22% of substituted queries generating either improved or equally-relevant results. The third method however returned a very similar level of superior results as the original query, and saw over 71% of substituted queries generating either improved or equally-relevant results. These results indicate that even when candidate synonym pairs are confirmed as being semantically associated using other methods, they still may not be suitable for query substitution, depending on the method of synonym discovery.

Question: What is the effect of synonym substitution on search results?


Avoiding and Treating Blindness From Fillers: A Review of the World Literature.. BACKGROUND As the popularity of soft tissue fillers increases, so do the reports of adverse events. The most serious complications are vascular in nature and include blindness.   OBJECTIVE To review the cases of blindness after filler injection, to highlight key aspects of the vascular anatomy, and to discuss prevention and management strategies.   METHODS A literature review was performed to identify all the cases of vision changes from filler in the world literature.   RESULTS Ninety-eight cases of vision changes from filler were identified. The sites that were high risk for complications were the glabella (38.8%), nasal region (25.5%), nasolabial fold (13.3%), and forehead (12.2%). Autologous fat (47.9%) was the most common filler type to cause this complication, followed by hyaluronic acid (23.5%). The most common symptoms were immediate vision loss and pain. Most cases of vision loss did not recover. Central nervous system complications were seen in 23.5% of the cases. No treatments were found to be consistently successful in treating blindness.   CONCLUSION Although the risk of blindness from fillers is rare, it is critical for injecting physicians to have a firm knowledge of the vascular anatomy and to understand key prevention and management strategies.

Question: What is the most common filler type to cause blindness?


Calibrating Large-area Mask Projection Stereolithography for Its Accuracy and Resolution Improvements. Solid freeform fabrication (SFF) processes based on mask image projection such as digital micro-mirror devices (DMD) have the potential to be fast and inexpensive. More and more research and commercial systems have been developed based on such digital devices. However, a digital light processing (DLP) projector based on DMD has limited resolution and certain image blurring. In order to use a DLP projector in the large-area mask projection stereolithography, it is critical to plan mask images in order to achieve high accuracy and resolution. Based on our previous work on optimized pixel blending, we present a calibration method for capturing the non-uniformity of a projection image by a low cost off-the-shelf DLP projector. Our method is based on two calibration systems, a geometric calibration system that can calibrate the position, shape, size, and orientation of a pixel and an energy calibration system that can calibrate the light intensity of a pixel. Based on both results, the light intensity at various grayscale levels can be approximated for each pixel. Developing a library of such approximation functions is critical for the optimized pixel blending to generate a better mask image plan. Experimental results verify our calibration results.

Question: How to calibrate a DLP projector?


A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation. We present an approach to interactivepredictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of 5 feedback requests for every input.

Question: What is the average number of feedback requests for every input?


Sex differences in mate preferences revisited: do people know what they initially desire in a romantic partner?. In paradigms in which participants state their ideal romantic-partner preferences or examine vignettes and photographs, men value physical attractiveness more than women do, and women value earning prospects more than men do. Yet it remains unclear if these preferences remain sex differentiated in predicting desire for real-life potential partners (i.e., individuals whom one has actually met). In the present study, the authors explored this possibility using speed dating and longitudinal follow-up procedures. Replicating previous research, participants exhibited traditional sex differences when stating the importance of physical attractiveness and earning prospects in an ideal partner and ideal speed date. However, data revealed no sex differences in the associations between participants' romantic interest in real-life potential partners (met during and outside of speed dating) and the attractiveness and earning prospects of those partners. Furthermore, participants' ideal preferences, assessed before the speed-dating event, failed to predict what inspired their actual desire at the event. Results are discussed within the context of R. E. Nisbett and T. D. Wilson's (1977) seminal article: Even regarding such a consequential aspect of mental life as romantic-partner preferences, people may lack introspective awareness of what influences their judgments and behavior.

Question: What is the difference between a speed date and a real date?


Effects of Platform-Switching on Peri-implant Soft and Hard Tissue Outcomes: A Systematic Review and Meta-analysis.. PURPOSE This systematic review and meta-analysis was aimed at evaluating the longitudinal effect of platform switching on implant survival rates as well as on soft and hard tissue outcomes.   MATERIALS AND METHODS An electronic search of the databases of the National Center for Biotechnology Information, PubMed, Ovid (MEDLINE), EMBASE, Web of Science, and Cochrane Collaboration Library was conducted in February 2015. Studies published in English with at least 10 human participants and a 12-month post-loading follow-up were included. Random effects meta-analyses of selected studies were applied to compare the primary and secondary outcomes of platform-switched (PS) and regular-platform (RP) implants, as well as the experimental designs and clinical outcomes.   RESULTS A total of 26 studies involving 1,511 PS implants and 1,123 RP implants were evaluated. Compared to RP implants, PS implants showed a slight increase in vertical marginal bone loss (VMBL) and pocket depth reduction (weighted mean differences were -0.23 mm and -0.20 mm, respectively). The PS implants had a mean VMBL of 0.36 ± 0.15 mm within the first year of service. The meta-regression suggested a trend of decreased bone resorption at sites with thick soft tissues at baseline.   CONCLUSION This study suggested that platform switching may have an indirect protective effect on implant hard tissue outcomes.

Question: What is the effect of platform switching on peri-implant soft and hard tissue outcomes?


A Fuzzy-based approach to programming language independent source-code plagiarism detection. Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative and semantic elements of similarity. This paper proposes a novel Fuzzy-based approach to source-code plagiarism detection, based on Fuzzy C-Means and the Adaptive-Neuro Fuzzy Inference System (ANFIS). In addition, performance of the proposed approach is compared to the Self- Organising Map (SOM) and the state-of-the-art plagiarism detection Running Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithms. The advantages of the proposed approach are that it is programming language independent, and hence there is no need to develop any parsers or compilers in order for the fuzzy-based predictor to provide detection in different programming languages. The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.

Question: What is a fuzzy-based approach to source-code plagiarism detection?


Accelerating fish detection and recognition by sharing CNNs with objectness learning. Daily increasing underwater visual data makes automatic object detection and recognition a great demand and challenging task. In this paper, we adopt a region proposal network to accelerate underwater object detection and recognition from Faster R-CNN. This process implement detection acceleration by using convolutional networks to generate high-quality object candidates, and by sharing these networks with original detection networks. Applied to a real-world fish dataset, which includes 24,277 ImageCLEF fish images belonging to 12 classes, this automatic detection and recognition system has a nearly real-time frame rate of 9.8 ftps, while yielding 15.1% higher Mean Average Precision (mAP) than the Deformable Parts Model (DPM) baseline.

Question: How to accelerate fish detection and recognition?


EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones. In order to facilitate the introduction of transit tracking and arrival time prediction in smaller transit agencies, we investigate an automatic, smartphone-based system which we call EasyTracker. To use EasyTracker, a transit agency must obtain smartphones, install an app, and place a phone in each transit vehicle. Our goal is to require no other input.  This level of automation is possible through a set of algorithms that use GPS traces collected from instrumented transit vehicles to determine routes served, locate stops, and infer schedules. In addition, online algorithms automatically determine the route served by a given vehicle at a given time and predict its arrival time at upcoming stops.  We evaluate our algorithms on real datasets from two existing transit services. We demonstrate our ability to accurately reconstruct routes and schedules, and compare our system's arrival time prediction performance with the current "state of the art" for smaller transit operators: the official schedule. Finally, we discuss our current prototype implementation and the steps required to take it from a research prototype to a real system.

Question: How to implement a transit tracking system using smartphones?


Cortical cores in network dynamics. Spontaneous brain activity at rest is spatially and temporally organized in networks of cortical and subcortical regions specialized for different functional domains. Even though brain networks were first studied individually through functional Magnetic Resonance Imaging, more recent studies focused on their dynamic 'integration'. Integration depends on two fundamental properties: the structural topology of brain networks and the dynamics of functional connectivity. In this scenario, cortical hub regions, that are central regions highly connected with other areas of the brain, play a fundamental role in serving as way stations for network traffic. In this review, we focus on the functional organization of a set of hub areas that we define as the 'dynamic core'. In the resting state, these regions dynamically interact with other regions of the brain linking multiple networks. First, we introduce and compare the statistical measures used for detecting hubs. Second, we discuss their identification based on different methods (functional Magnetic Resonance Imaging, Diffusion Weighted Imaging, Electro/Magneto Encephalography). Third, we show that the degree of interaction between these core regions and the rest of the brain varies over time, indicating that their centrality is not stationary. Moreover, alternating periods of strong and weak centrality of the core relate to periods of strong and weak global efficiency in the brain. These results indicate that information processing in the brain is not stable, but fluctuates and its temporal and spectral properties are discussed. In particular, the hypothesis of 'pulsed' information processing, discovered in the slow temporal scale, is explored for signals at higher temporal resolution.

Question: What is the dynamic core?


High-power GaN MMIC PA Over 40–4000MHz. We report a high-performance GaN MMIC power amplifier operating from 40MHz to 4,000MHz. The MMIC achieved 80W pulsed (100us pulse width and 10% duty cycle) output power (P5dB) with 54% efficiency at 40MHz, 50W with about 30% efficiency across most of the mid band, and gradually decreases to 30W with 22% efficiency at 4000MHz. Power gain is 25dB across the 40-4000MHz band. This ultra wideband performance is achieved by both tailoring the device output impedance, and using a unique wide-band, circuit-matching topology. Detailed design techniques of both the device and the matching circuit will be presented.

Question: What is the highest frequency of a gaN MMIC PA?


Activity Monitoring: Noticing Interesting Changes in Behavior. We introduce a problem class which we term activity monitoring. Such problems involve monitoring the behavior of a large population of entities for interesting events requiring action. We present a framework within which each of the individual problems has a natural expression, as well as a methodology for evaluating performance of activity monitoring techniques. We show that two superficially different tasks, news story monitoring and intrusion detection, can be expressed naturally within the framework, and show that key differences in solution methods can be compared.

Question: What is the difference between activity monitoring and intrusion detection?


Neural computation and self-organizing maps - an introduction. In this age of modern era, the use of internet must be maximized. Yeah, internet will help us very much not only for important thing but also for daily activities. Many people now, from any level can use internet. The sources of internet connection can also be enjoyed in many places. As one of the benefits is to get the on-line neural computation and self organizing maps an introduction computation neural systems series book, as the world window, as many people suggest.

Question: What is the world window?


Why healthcare workers don't wash their hands: a behavioral explanation.. OBJECTIVE To elucidate behavioral determinants of handwashing among nurses.   DESIGN Statistical modeling using the Theory of Planned Behavior and relevant components to handwashing behavior by nurses that were derived from focus-group discussions and literature review.   SETTING The community and 3 tertiary care hospitals.   PARTICIPANTS Children aged 9-10 years, mothers, and nurses.   RESULTS Responses from 754 nurses were analyzed using backward linear regression for handwashing intention. We reasoned that handwashing results in 2 distinct behavioral practices--inherent handwashing and elective handwashing--with our model explaining 64% and 76%, respectively, of the variance in behavioral intention. Translation of community handwashing behavior to healthcare settings is the predominant driver of all handwashing, both inherent (weighted beta =2.92) and elective (weighted beta =4.1). Intended elective in-hospital handwashing behavior is further significantly predicted by nurses' beliefs in the benefits of the activity (weighted beta =3.12), peer pressure of senior physicians (weighted beta =3.0) and administrators (weighted beta =2.2), and role modeling (weighted beta =3.0) but only to a minimal extent by reduction in effort (weighted beta =1.13). Inherent community behavior (weighted beta =2.92), attitudes (weighted beta =0.84), and peer behavior (weighted beta =1.08) were strongly predictive of inherent handwashing intent.   CONCLUSIONS A small increase in handwashing adherence may be seen after implementing the use of alcoholic hand rubs, to decrease the effort required to wash hands. However, the facilitation of compliance is not simply related to effort but is highly dependent on altering behavioral perceptions. Thus, introduction of hand rub alone without an associated behavioral modification program is unlikely to induce a sustained increase in hand hygiene compliance.

Question: What is the difference between inherent and elective handwashing?


Hilbert Space Embeddings of Hidden Markov Models. Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We propose a nonparametric HMM that extends traditional HMMs to structured and non-Gaussian continuous distributions. Furthermore, we derive a localminimum-free kernel spectral algorithm for learning these HMMs. We apply our method to robot vision data, slot car inertial sensor data and audio event classification data, and show that in these applications, embedded HMMs exceed the previous state-of-the-art performance.

Question: What is a nonparametric HMM?


Improved seam carving for video retargeting. Video, like images, should support content aware resizing. We present video retargeting using an improved seam carving operator. Instead of removing 1D seams from 2D images we remove 2D seam manifolds from 3D space-time volumes. To achieve this we replace the dynamic programming method of seam carving with graph cuts that are suitable for 3D volumes. In the new formulation, a seam is given by a minimal cut in the graph and we show how to construct a graph such that the resulting cut is a valid seam. That is, the cut is monotonic and connected. In addition, we present a novel energy criterion that improves the visual quality of the retargeted images and videos. The original seam carving operator is focused on removing seams with the least amount of energy, ignoring energy that is introduced into the images and video by applying the operator. To counter this, the new criterion is looking forward in time - removing seams that introduce the least amount of energy into the retargeted result. We show how to encode the improved criterion into graph cuts (for images and video) as well as dynamic programming (for images). We apply our technique to images and videos and present results of various applications.

Question: How to remove seams from images and videos?


Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization. Learning to remember long sequences remains a challenging task for recurrent neural networks. Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shorter local contexts than encouraging long sequence encoding. Associative memory, which studies the compression of multiple patterns in a fixed size memory, were rarely considered in recent years. Although some recent work tries to introduce associative memory in RNN and mimic the energy decay process in Hopfield nets, it inherits the shortcoming of rule-based memory updates, and the memory capacity is limited. This paper proposes a method to learn the memory update rule jointly with task objective to improve memory capacity for remembering long sequences. Also, we propose an architecture that uses multiple such associative memory for more complex input encoding. We observed some interesting facts when compared to other RNN architectures on some well-studied sequence learning tasks.

Question: How to improve memory capacity for remembering long sequences?


Robust Subspace Clustering via Smoothed Rank Approximation. Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for this problem which can recover the rank exactly under some restricted and theoretically interesting conditions. However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum. To seek a solution of higher accuracy than the nuclear norm, in this letter, we propose a rank approximation based on Logarithm-Determinant. We consider using this rank approximation for subspace clustering application. Our framework can model different kinds of errors and noise. Effective optimization strategy is developed with theoretical guarantee to converge to a stationary point. The proposed method gives promising results on face clustering and motion segmentation tasks compared to the state-of-the-art subspace clustering algorithms.

Question: What is the nuclear norm?


Addressing challenges in the clinical applications associated with CRISPR/Cas9 technology and ethical questions to prevent its misuse. Xiang Jin Kang, Chiong Isabella Noelle Caparas, Boon Seng Soh, Yong Fan 1 Key Laboratory for Major Obstetric Diseases of Guangdong Province, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Center for Reproductive Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China 2 Disease Modeling and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, 61 Biopolis Drive Proteos, Singapore 138673, Singapore & Correspondence: bssoh@imcb.a-star.edu.sg (B. S. Soh), yongfan011@gzhmu.edu.cn (Y. Fan)

Question: What is the key laboratory for major obstetric diseases of Guangdong province?


Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks. In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.

Question: What is a deep neural network?


Applications of SAT Solvers to AES Key Recovery from Decayed Key Schedule Images. Cold boot attack is a side channel attack which exploits the data remanence property of random access memory (RAM) to retrieve its contents which remain readable shortly after its power has been removed. Given the nature of the cold boot attack, only a corrupted image of the memory contents will be available to the attacker. In this paper, we investigate the use of an off-the-shelf SAT solver, CryptoMinSat, to improve the key recovery of the AES-128 key schedules from its corresponding decayed memory images. By exploiting the asymmetric decay of the memory images and the redundancy of key material inherent in the AES key schedule, rectifying the faults in the corrupted memory images of the AES-128 key schedule is formulated as a Boolean satisfiability problem which can be solved efficiently for relatively very large decay factors. Our experimental results show that this approach improves upon the previously known results.

Question: How to recover the key of a cipher?


Design and operation of Wi-Fi agribot integrated system. Robotics in agriculture is not a new concept; in controlled environments (green houses), it has a history of over 20 years. Research has been performed to develop harvesters for cherry tomatoes, cucumbers, mushrooms, and other fruits. In horticulture, robots have been introduced to harvest citrus and apples. In this paper autonomous robot for agriculture (AgriBot) is a prototype and implemented for performing various agricultural activities like seeding, weeding, spraying of fertilizers, insecticides. AgriBot is controlled with a Arduino Mega board having At mega 2560 microcontroller. The powerful Raspberry Pi a mini computer is used to control and monitor the working of the robot. The Arduino Mega is mounted on a robot allowing for access to all of the pins for rapid prototyping. Its hexapod body can autonomously walk in any direction, avoiding objects with its ultrasonic proximity sensor. Its walking algorithms allow it to instantly change direction and walk in any new direction without turning its body. An underbody sensory array allows the robot to know if a seed has been planted in the area at the optimal spacing and depth. AgriBot can then dig a hole, plant a seed in the hole, cover the seed with soil, and apply any pre-emergence fertilizers and/or herbicides along with the marking agent. AgriBot can then signal to other robots in the immediate proximity that it needs help planting in that area or that this area has been planted and to move on by communicating through Wi-Fi.

Question: What is the difference between a robot and a robot?


Depressive symptomatology among university students in Denizli, Turkey: prevalence and sociodemographic correlates.. AIM To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students.   METHODS A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis.   RESULTS Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%).   CONCLUSIONS Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with p

Question: What is the prevalence of depressive symptoms among university students in Denizli, Turkey?


Mesofluidic actuation for articulated finger and hand prosthetics. The loss of fingers and hands severely limits career and lifestyle options for the amputee. Unfortunately, while there have been strides made in advancements of upper arm and leg prosthetics, the state of the art in prosthetic hands is lagging far behind. Options are generally limited to claw like devices that provide limited gripping capacity. The overall objective of this paper is to demonstrate a path towards a low-cost prosthetic hand with multiple articulated fingers and a thumb that rivals the human hand in terms of weight, size, dexterity, range of motion, force carrying capacity and speed. We begin with a description of the functional requirements for a human hand. When comparing requirements with actuation technologies, the fluid power approach has the potential to realize a prosthetic hand that rivals a human hand in size, strength and dexterity. We introduce a new actuation technology, mesofluidics, that focuses on miniaturization of fluid power to the meso-scale (mm to cm). As a novel demonstration of the potential for this technology, we describe a proof-of-principle mesofluidic finger that has intrinsic actuation and control (actuators and control valves within the volume of the finger). This finger weighs 63 grams, is sized to the 50th percentile male finger, has a total of 25 mechanical parts and is capable of providing 10 kg (22 lbs) of pinch force.

Question: What is mesofluidics?


Supervised and Unsupervised methods to detect Insider Threat from Enterprise Social and Online Activity Data. Insider threat is a significant security risk for organizations, and detection of insider threat is of paramount concern to organizations. In this paper, we attempt to discover insider threat by analyzing enterprise social and online activity data of employees. To this end, we process and extract relevant features that are possibly indicative of insider threat behavior. This includes features extracted from social data including email communication patterns and content, and online activity data such as web browsing patterns, email frequency, and file and machine access patterns. Subsequently, we take two approaches to detect insider threat: (i) an unsupervised approach where we identify statistically abnormal behavior with respect to these features using state-of-the-art anomaly detection methods, and (ii) a supervised approach where we use labels indicating when employees quit the company as a proxy for insider threat activity to design a classifier. We test our approach on a real world data set with artificially injected insider threat events. We obtain a ROC score of 0.77 for the unsupervised approach, and a classification accuracy of 73.4% for the supervised approach. These results indicate that our proposed approaches are fairly successful in identifying insider threat events. Finally, we build a visualization dashboard that enables managers and HR personnel to quickly identify employees with high threat risk scores which will enable them to take suitable preventive measures and limit security risk.

Question: What is the canadian military?


An ontological analysis of the economic primitives of the extended-REA enterprise information architecture. The Resource-Event-Agent (REA) model for enterprise economic phenomena was first published in The Accounting Review in 1982. Since that time its concepts and use have been extended far beyond its original accountability infrastructure to a framework for enterprise information architectures. The granularity of the model has been extended both up (to enterprise value chains) and down (to workflow or task specification) in the aggregation plane, and additional conceptual type-images and commitment-images have been proposed as well. The REA model actually fits the notion of domain ontology well, a notion that is becoming increasingly important in the era of e-commerce and virtual companies. However, its present and future components have never been analyzed formally from an ontological perspective. This paper intends to do just that, relying primarily on the conceptual terminology of John Sowa. The economic primitives of the original REA model (economic resources, economic events, economic agents, duality relationships, stock-flow relationships, control relationships and responsibility relationships) plus some newer components (commitments, types, custody, reciprocal, etc.) will be analyzed individually and collectively as a specific domain ontology. Such a review can be used to guide further conceptual development of REA extensions.

Question: What is an ontological analysis?


Gradient descent GAN optimization is locally stable. Despite the growing prominence of generative adversarial networks (GANs), optimization in GANs is still a poorly understood topic. In this paper, we analyze the "gradient descent" form of GAN optimization, i.e., the natural setting where we simultaneously take small gradient steps in both generator and discriminator parameters. We show that even though GAN optimization does not correspond to a convex-concave game (even for simple parameterizations), under proper conditions, equilibrium points of this optimization procedure are still locally asymptotically stable for the traditional GAN formulation. On the other hand, we show that the recently proposed Wasserstein GAN can have non-convergent limit cycles near equilibrium. Motivated by this stability analysis, we propose an additional regularization term for gradient descent GAN updates, which is able to guarantee local stability for both the WGAN and the traditional GAN, and also shows practical promise in speeding up convergence and addressing mode collapse.

Question: What is the gradient descent form of GAN optimization?


Global supply chain design : A literature review and critique. In this paper, we review decision support models for the design of global supply chains, and assess the fit between the research literature in this area and the practical issues of global supply chain design. The classification scheme for this review is based on ongoing and emerging issues in global supply chain management and includes review dimensions for (1) decisions addressed in the model, (2) performance metrics, (3) the degree to which the model supports integrated decision processes, and (4) globalization considerations. We conclude that although most models resolve a difficult feature associated with globalization, few models address the practical global supply chain design problem in its entirety. We close the paper with recommendations for future research in global supply chain modeling that is both forward-looking and practically oriented. 2005 Elsevier Ltd. All rights reserved.

Question: What is the classification scheme for this review?


An empirical study on the impact of static typing on software maintainability. Static type systems play an essential role in contemporary programming languages. Despite their importance, whether static type systems impact human software development capabilities remains open. One frequently mentioned argument in favor of static type systems is that they improve the maintainability of software systems—an often-used claim for which there is little empirical evidence. This paper describes an experiment that tests whether static type systems improve the maintainability of software systems, in terms of understanding undocumented code, fixing type errors, and fixing semantic errors. The results show rigorous empirical evidence that static types are indeed beneficial to these activities, except when fixing semantic errors. We further conduct an exploratory analysis of the data in order to understand possible reasons for the effect of type systems on the three kinds of tasks used in this experiment. From the exploratory analysis, we conclude that developers using a dynamic type system tend to look at different files more frequently when doing programming tasks—which is a potential reason for the observed differences in time.

Question: What is the impact of static typing on software maintainability?


The Magnetoelectric Dipole—A Wideband Antenna for Base Stations in Mobile Communications. In this paper, stringent requirements imposed on the design of base station antennas for mobile communications are summarized. Conventional techniques for implementing base station antennas are reviewed. The complementary antenna concept of combining an electric dipole with a magnetic dipole is reconsidered. Recently, this kind of antenna has been commonly called a "Huygen's source." The purpose is to develop wideband unidirectional antennas with stable frequency characteristics and low back radiation. Based on this concept, the magnetoelectric dipole was invented by integrating an electric dipole with an -probe fed shorted quarter-wave patch antenna. A number of magnetoelectric dipoles with different radiation patterns and different polarizations have been developed in recent years. An overview of the characteristics of this new class of complementary antennas is presented. Major design challenges are explained. Finally, a new magnetoelectric dipole that is low in profile and robust in structure is presented. The magnetic dipole part of this antenna is realized by a triangular-shaped loop antenna. The antenna is inherently direct current (dc) grounded, which satisfies the requirement for outdoor applications.

Question: What is a magnetoelectric dipole?


Service Personnel, Technology, and Their Interaction in Influencing Customer Satisfaction. Managing both the technologies and the personnel needed for providing high-quality, multichannel customer support creates a complex and persistent operational challenge. Adding to this difficulty, it is still unclear how service personnel and these new communication technologies interact to influence the customer's perceptions of the service being provided. Motivated by both practical importance and inconsistent findings in the academic literature, this exploratory research examines the interaction of media richness, represented by three different technology contexts (telephone, e-mail, and online chat), with six customer service representative (CSR) characteristics and their influences on customer satisfaction. Using a large-sample customer survey data set, the article develops a multigroup structural equation model to analyze these interactions. Results suggest that CSR characteristics influence customer service satisfaction similarly across all three technology-mediated contexts. Of the characteristics studied, service representatives contribute to customer satisfaction more when they exhibit the characteristics of thoroughness, knowledgeableness, and preparedness, regardless of the richness of the medium used. Surprisingly, while three other CSR characteristics studied (courtesy, professionalism, and attentiveness) are traditionally believed to be important in face-to-face encounters, they had no significant impact on customer satisfaction in the technology-mediated contexts studied. Implications for both practitioners and researchers are drawn from the results and future research opportunities are

Question: How do customer service representatives influence customer satisfaction?