A Framework for Evaluating Mobile App Repackaging Detection Algorithms. Because it is not hard to reverse engineer the Dalvik bytecode used in the Dalvik virtual machine, Android application repackaging has become a serious problem. With repackaging, a plagiarist can simply steal others' code violating the intellectual property of the developers. More seriously, after repackaging, popular apps can become the carriers of malware, adware or spy-ware for wide spreading. To maintain a healthy app market, several detection algorithms have been proposed recently, which can catch some types of repackaged apps in various markets efficiently. However, they are generally lack of valid analysis on their effectiveness. After analyzing these approaches, we find simple obfuscation techniques can potentially cause false negatives, because they change the main characteristics or features of the apps that are used for similarity detections. In practice, more sophisticated obfuscation techniques can be adopted (or have already been performed) in the context of mobile apps. We envision this obfuscation based repackaging will become a phenomenon due to the arms race between repackaging and its detection. To this end, we propose a framework to evaluate the obfuscation resilience of repackaging detection algorithms comprehensively. Our evaluation framework is able to perform a set of obfuscation algorithms in various forms on the Dalvik bytecode. Our results provide insights to help gauge both broadness and depth of algorithms' obfuscation resilience. We applied our framework to conduct a comprehensive case study on AndroGuard, an Android repackaging detector proposed in Black-hat 2011. Our experimental results have demonstrated the effectiveness and stability of our framework.

Question: What is the framework for evaluating mobile app repackaging detection algorithms?


Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention. In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a firststage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first-stage representation to attend words appeared in itself, which is called "Inner-Attention" in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner-Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.

Question: How to recognize text entailment?


Robust real-time lane and road detection in critical shadow conditions. This paper presents the vision-based road detection system currently installed onto the MOB-LAB land vehicle. Based on a geometrical transform and on a fast morphological processing, the system is capable to detect road markings even in extremely severe shadow conditions on at and structured roads. The use of a special-purpose massively architecture (PAPRICA) allows to achieve a processing rate of about 17 Hz.

Question: What is a PAPRICA?


A Case Study of Automated Face Recognition: The Boston Marathon Bombings Suspects. While face recognition can be a useful tool, helping authorities to narrow leads and confirm a criminal suspect's identity, the media tended to highlight the technology's limitations in the Boston Marathon bombings investigation. Consequently, we were motivated to reexamine the efficacy of unconstrained face recognition using this incident as a case study. We chose two automated face recognition systems known for their superior unconstrained matching performance based on tests conducted by the US National Institute of Standards and Technology: NEC's NeoFace v3.1 and Google-owned Pittsburgh Pattern Recognition v5.2.2. To conduct the experiment, we added six reference images of the Tsarnaevs taken from press releases and news articles following their identification to a background database of 1 million mug shots. Against this database, we searched for matches of the five face images of the brothers extracted by the FBI from surveillance, smartphone, or point-and-shoot camera footage before their identification.

Question: What is the Boston Marathon bombing suspects' name?


Comparative study of algorithms for Atrial Fibrillation detection. Automatic detection of Atrial Fibrillation (AF) is necessary for the long-term monitoring of patients who are suspected to have AF. Several methods for AF detection exist in the literature. These methods are mainly based on two different characteristics of AF ECGs: the irregularity of RR intervals (RRI) and the fibrillatory electrical Atrial Activity (AA). The electrical AA is characterized by the absence of the P-wave (PWA) and special frequency properties (FSA). Nine AF detection algorithms were selected from literature and evaluated with the same protocol in order to study their performance under different conditions. Results showed that the highest sensitivity (Se=97.64%) and specificity (Sp=96.08%) was achieved with methods based on analysis of irregularity of RR interval, while combining RR and atrial activity analysis gave the highest positive predictive value (PPV=92.75%). Algorithms based on RR irregularity were also the most robust against noise (Se=85.79% and Sp=81.90% for SNR=0dB; and Se=82.52% and Sp=40.47% for SNR=−5dB).

Question: What is the difference between the regular and irregular RR intervals?


Graph-Based Relational Data Visualization. Relational databases are rigid-structured data sources characterized by complex relationships among a set of relations (tables). Making sense of such relationships is a challenging problem because users must consider multiple relations, understand their ensemble of integrity constraints, interpret dozens of attributes, and draw complex SQL queries for each desired data exploration. In this scenario, we introduce a twofold methodology, we use a hierarchical graph representation to efficiently model the database relationships and, on top of it, we designed a visualization technique for rapidly relational exploration. Our results demonstrate that the exploration of databases is deeply simplified as the user is able to visually browse the data with little or no knowledge about its structure, dismissing the need for complex SQL queries. We believe our findings will bring a novel paradigm in what concerns relational data comprehension.

Question: What is a relational database?


Automatic Generation of 3 D Thermal Maps of Building Interiors. Most existing approaches to characterizing thermal properties of buildings and heat emissions from their elements rely on manual inspection and as such are slow, and labor intensive. This is often a daunting task, which requires several days of on-site inspection. In this paper, we propose a fully automatic approach to construct a 3D thermal point cloud of the building interior reflecting the geometry including walls, floors, and ceilings, as well as structures such as furniture, lights, windows, and plug loads. Our approach is based on a wearable ambulatory backpack comprising multiple sensors such as Light Detection And Ranging (LiDAR) scanners, and Infrared and optical cameras. As the operator wearing the backpack walks through the building, the LiDAR scans are collected and processed in order to compute the 3D geometry of the building. Furthermore, the Infrared cameras are calibrated intrinsically and extrinsically such that the captured images are registered to the captured geometry. Thus, the temperature data in the Infrared images is associated with the geometry resulting in a "thermal 3D point cloud". The same process can be repeated using optical imagery resulting in a "visible 3D point cloud". By visualizing the two point clouds simultaneously in interactive rendering tools, we can virtually walk through the thermal and optical 3D point clouds, toggle between them, identify and annotate, "hot" regions, objects, plug loads, thermal and moisture leaks, and document their location with fine spatial granularity in the 3D point clouds.

Question: How to construct a 3D thermal point cloud of a building?


A direct fingerprint minutiae extraction approach based on convolutional neural networks. Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of minutiae. In this paper, a novel minutiae extraction approach based on deep convolutional neural networks is proposed, which directly extract minutiae on raw fingerprint images without any preprocess since we tactfully take the advantage of the strong representative capacity of deep convolutional neural networks. Minutiae can be effectively extracted due to the well designed architectures. Furthermore, the accuracy is guaranteed in that the comprehensive estimate is made to eliminate spurious minutiae. Moreover, a number of implement skills are employed both to avoid overfitting and to improve the robustness. This approach makes a good performance because it not only makes all use of information in fingerprint images but also learns the minutiae patterns from large amounts of data. Comparisons are made with previous works and a widely applied commercial fingerprint identification system. Results show that our approach performs better both in accuracy and robustness.

Question: What is a fingerprint minutiae extraction approach based on deep convolutional neural networks?


Detecting People in Artwork with CNNs. CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN's performance is the highest yet, it remains less than 60% AP, suggesting further work is needed for the cross-depiction problem.

Question: How to detect people in artwork?


An overview of online trust: Concepts, elements, and implications. Lack of trust has been repeatedly identified as one of the most formidable barriers to people for engaging in e-commerce, involving transactions in which financial and personal information is submitted to merchants via the Internet. The future of e-commerce is tenuous without a general climate of online trust. Building consumer trust on the Internet presents a challenge for online merchants and is a research topic of increasing interest and importance. This paper provides an overview of the nature and concepts of trust from multi-disciplinary perspectives, and it reviews relevant studies that investigate the elements of online trust. Also, a framework of trust-inducing interface design features articulated from the existing literature is presented. The design features were classified into four dimensions, namely (1) graphic design, (2) structure design, (3) content design, and (4) social-cue design. By applying the design features identified within this framework to e-commerce web site interfaces, online merchants might then anticipate fostering optimal levels of trust in their customers. 2003 Elsevier Ltd. All rights reserved.

Question: What is the difference between a framework and a model?


Mapping distributed brain function and networks with diffuse optical tomography.. Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

Question: What is the difference between positron emission tomography and functional magnetic resonance imaging?


3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction. With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.

Question: How to reconstruct a 3D object from a noisy point cloud?


High Performance Ternary Multiplier Using CNTFET. Ternary logic is a promising alternative to the conventional binary logic in VLSI design as it provides the advantages of reduced interconnects, higher operating speeds and smaller chip area. This work presents a ternary multiplier using carbon nanotube field effect transistors (CNTFETs). The proposed designs use in-depth analysis of addition required for designing a two trit multiplier. Based on this analysis two efficient adders are proposed. These adders are used to optimize the multiplier design. The proposed circuits are extensively simulated using HSPICE to obtain power, delay and power delay product. The circuit performances are compared with designs reported in recent literature. This circuit demonstrates a power delay product improvement up to 16.8%, with lesser transistor count of 16%. So, the use of these circuits in complex arithmetic circuits will be advantageous.

Question: What is the power delay product of a multiplier?


An Inductive 2-D Position Detection IC With 99.8% Accuracy for Automotive EMR Gear Control System. In this paper, the analog front end (AFE) for an inductive position sensor in an automotive electromagnetic resonance gear control applications is presented. To improve the position detection accuracy, a coil driver with an automatic two-step impedance calibration is proposed which, despite the load variation, provides the desired driving capability by controlling the main driver size. Also, a time shared analog-to-digital converter (ADC) is proposed to convert eight-phase signals while reducing the current consumption and area to 1/8 of the conventional structure. A relaxation oscillator with temperature compensation is proposed to generate a constant clock frequency in vehicle temperature conditions. This chip is fabricated using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process and the die area is 2 mm <inline-formula> <tex-math notation="LaTeX">$\times 1.5$ </tex-math></inline-formula> mm. The power consumption of the AFE is 23.1 mW from the supply voltage of 3.3 V to drive one transmitter (Tx) coil and eight receiver (Rx) coils. The measured position detection accuracy is greater than 99.8 %. The measurement of the Tx shows a driving capability higher than 35 mA with respect to the load change.

Question: What is the size of the die?


Projection Pursuit Regression. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How do I use the JSTOR archive?


What Constitutes Normal Sleeping Patterns ? Childhood to Adolescence 2 . 1 Developmental Patterns in Sleep Duration : Birth to Adolescence. Healthy, adequate sleep is integral to the process of growth and development during adolescence. At puberty, maturational changes in th e underlying homeostatic and circadian sleep regulatory mechanisms influence the sleep-wak e p tterns of adolescents. These changes interact with psychosocial factors, such as increas ing academic demands, hours spent in paid employment, electronic media use, and social opport unities, and constrict the time available for adolescents to sleep. Survey studies reveal tha t adolescents' habitual sleep schedules are associated with cumulative sleep loss. As a consequ ence, there is growing concern about the effects of insufficient sleep on adolescents' wakin g function. This review identifies and examines the characteristics of sleep and sleep los s in adolescents. It highlights the need for more research into the effects of chronic partial s leep deprivation in adolescents, and the process of extending sleep on weekends to recover t he effects of sleep debt. An understanding of chronic sleep deprivation and recovery sleep in adolescents will facilitate the development of evidence-based sleep guidelines and recommendati o s for recovery sleep opportunities when habitual sleep times are insufficient.

Question: What is the difference between a normal sleeping pattern and a sleeping pattern that is not normal?


Automatic Indexing Based on Bayesian Inference Networks. In this paper, a Bayesian inference network model for automatic indexing with index terms (descriptors) from a prescribed vocabulary is presented. It requires an indexing dictionary with rules mapping terms of the respective subject field onto descriptors and inverted lists for terms occuring in a set of documents of the subject field and descriptors manually assigned to these documents. The indexing dictionary can be derived automatically from a set of manually indexed documents. An application of the network model is described, followed by an indexing example and some experimental results about the indexing performance of the network model.

Question: What is a Bayesian inference network?


InTouch Tactile Tales: Haptic Feedback and Long-Distance Storytelling. The squeeze of an arm during a tense moment in a story -- an absentminded caress on the back of the hand while listening to an engaging tale -- the physical presence and interpersonal touch of a loved one can be an important part of reading to a child. However the thrill of the tale is often lost and the intimacy diluted when separated families have to resort to the flatness of a video-call or mobile app to share a bedtime story with their loved one. This project is setting out to create a physical story portal, a magical object which uses technologies to link teller and listener through sound, light and touch to help bridge this gap when families are separated. While the development of the InTouch reading system is still in the preliminary stages, a prototype handheld device has been created to allow adults and children to share touch-based messages during story time. It has been trialled with several families in the lab, and the initial results have pointed to both guidance and design considerations that should be taken into account for such a system to be successfully deployed.

Question: How can we use haptic feedback to tell a story?


Integrated Pedestrian and Direction Classification Using a Random Decision Forest. For analysing the behaviour of pedestrians in a scene, it is common practice that pedestrian localization, classification, and tracking are conducted consecutively. The direction of a pedestrian, being part of the pose, implies the future path. This paper proposes novel Random Decision Forests (RDFs) to simultaneously classify pedestrians and their directions, without adding an extra module for direction classification to the pedestrian classification module. The proposed algorithm is trained and tested on the TUD multi-view pedestrian and Daimler Mono Pedestrian Benchmark data-sets. The proposed integrated RDF classifiers perform comparable to pedestrian or direction trained separated RDF classifiers. The integrated RDFs yield results comparable to those of state-of-the-art and baseline methods aiming for pedestrian classification or body direction classification, respectively.

Question: What is the difference between a pedestrian and a direction?


ESPAC: Enabling Security and Patient-centric Access Control for eHealth in cloud computing. We consider the problem of patient self-controlled access privilege to highly sensitive Personal Health Information (PHI), where PHI is expected to be securely stored in cloud storage for uninterrupted anytime, anywhere remote access. In order to assure the privacy of PHI, we propose Efficient and Secure Patient-centric Access Control (ESPAC) scheme which allows data requesters to have different access privileges based on their roles, and then assigns different attribute sets to them. Extensive security and performance analyses demonstrate that the ESPAC scheme is able to achieve desired security requirements with acceptable communication delay.

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


Evaluation of 3D Feature Descriptors for Multi-modal Data Registration. We propose a framework for 2D/3D multi-modal data registration and evaluate 3D feature descriptors for registration of 3D datasets from different sources. 3D datasets of outdoor environments can be acquired using a variety of active and passive sensor technologies. Registration of these datasets into a common coordinate frame is required for subsequent modelling and visualisation. 2D images are converted into 3D structure by stereo or multiview reconstruction techniques and registered to a unified 3D domain with other datasets in a 3D world. Multi-modal datasets have different density, noise, and types of errors in geometry. This paper provides a performance benchmark for existing 3D feature descriptors across multi-modal datasets. This analysis highlights the limitations of existing 3D feature detectors and descriptors which need to be addressed for robust multi-modal data registration. We analyse and discuss the performance of existing methods in registering various types of datasets then identify future directions required to achieve robust multi-modal data registration.

Question: What is the difference between a 3D feature descriptor and a 3D feature detector?


Convolutional LSTM Networks for Video-based Person Re-identification. In this paper, we study the problem of video-based person re-identification. This is more challenging, and of greater practical interest, than conventional image-based person re-identification. To address this problem, we propose the use of convolutional Long Short Term Memory (LSTM) based networks to learn a video-based representation for person re-identification. To this end, we propose to jointly leverage deep Convolutional Neural Networks (CNNs) and LSTM networks. Given sequential video frames of a person, the spatial information encoded in the frames is first extracted by a set of CNNs. An encoderdecoder framework derived from LSTMs is employed to encode the resulting temporal of CNN outputs. This approach leads to a refined feature representation that is able to explicitly model the video as an ordered sequence, while preserving the spatial information. Comparative experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose.

Question: What is the state of the art for video-based person re-identification?


Cellular commitment in the developing cerebellum. The mammalian cerebellum is located in the posterior cranial fossa and is critical for motor coordination and non-motor functions including cognitive and emotional processes. The anatomical structure of cerebellum is distinct with a three-layered cortex. During development, neurogenesis and fate decisions of cerebellar primordium cells are orchestrated through tightly controlled molecular events involving multiple genetic pathways. In this review, we will highlight the anatomical structure of human and mouse cerebellum, the cellular composition of developing cerebellum, and the underlying gene expression programs involved in cell fate commitments in the cerebellum. A critical evaluation of the cell death literature suggests that apoptosis occurs in ~5% of cerebellar cells, most shortly after mitosis. Apoptosis and cellular autophagy likely play significant roles in cerebellar development, we provide a comprehensive discussion of their role in cerebellar development and organization. We also address the possible function of unfolded protein response in regulation of cerebellar neurogenesis. We discuss recent advancements in understanding the epigenetic signature of cerebellar compartments and possible connections between DNA methylation, microRNAs and cerebellar neurodegeneration. Finally, we discuss genetic diseases associated with cerebellar dysfunction and their role in the aging cerebellum.

Question: What is the cerebellum?


A Rule Based Approach to Discourse Parsing. In this paper we present an overview of recent developments in discourse theory and parsing under the Linguistic Discourse Model (LDM) framework, a semantic theory of discourse structure. We give a novel approach to the problem of discourse segmentation based on discourse semantics and sketch a limited but robust approach to symbolic discourse parsing based on syntactic, semantic and lexical rules. To demonstrate the utility of the system in a real application, we briefly describe the architecture of the PALSUMM system, a symbolic summarization system being developed at FX Palo Alto Laboratory that uses discourse structures constructed using the theory outlined to summarize written English prose texts. 1

Question: What is a rule based approach to discourse parsing?


Inductive Design of Maturity Models: Applying the Rasch Algorithm for Design Science Research. Maturity models are an established means to systematically document and guide the development of organizations using archetypal capability levels. Often, these models lack a sound foundation and/or are derived on the basis of an arbitrary design method. In order to foster the design of relevant and rigorous artifacts, this paper presents a method for maturity model construction that applies the Rasch algorithm and cluster analysis as a sound methodical foundation. The Rasch algorithm is widely used to improve scholarly intelligence and attainment tests. In order to demonstrate the application of the proposed method and to evaluate its usability and applicability, we present a design exemplar in the business intelligence domain.

Question: How to construct a maturity model?


Probabilistic Latent Semantic Indexing. Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.

Question: What is the difference between probabilistic latent semantic indexing and latent semantic indexing?


Mapping the antigenic and genetic evolution of influenza virus.. The antigenic evolution of influenza A (H3N2) virus was quantified and visualized from its introduction into humans in 1968 to 2003. Although there was remarkable correspondence between antigenic and genetic evolution, significant differences were observed: Antigenic evolution was more punctuated than genetic evolution, and genetic change sometimes had a disproportionately large antigenic effect. The method readily allows monitoring of antigenic differences among vaccine and circulating strains and thus estimation of the effects of vaccination. Further, this approach offers a route to predicting the relative success of emerging strains, which could be achieved by quantifying the combined effects of population level immune escape and viral fitness on strain evolution.

Question: How does the antigenic evolution of influenza virus compare to the genetic evolution of influenza virus?


Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas. The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. The appearance of buildings in SAR or interferometric SAR (InSAR) data is characterized by the consequences of the inherent oblique scene illumination, such as layover, occlusion by radar shadow, and multipath signal propagation. Therefore, particularly in dense built-up areas, building reconstruction is often impossible from a single SAR or InSAR measurement alone. But, the reconstruction quality can be significantly improved by a combined analysis of multi-aspect data. In this paper, two approaches are proposed to detect and reconstruct buildings of different size from multi-aspect high-resolution InSAR data sets. Both approaches focus on the recognition of buildings supported by knowledge-based analysis considering the mentioned SAR-specific effects observed in urban areas. Building features are extracted independently for each direction from the magnitude and phase information of the interferometric data. Initial primitives are segmented and afterward projected from slant-range into the world coordinate system. From the fused set of primitives of both flight directions, building hypotheses are generated. The first approach exploits the frequently observed lines of bright double-bounce scattering, which are used for building reconstruction in residential districts. In the case of larger buildings, such as industrial halls, often additional features of roof and facade elements are visible. Therefore, in a second approach, extended buildings are extracted by grouping primitives of different kinds. The two approaches are demonstrated in an urban environment for an InSAR data set, which has spatial resolution of about 30 cm and was taken from two orthogonal flight directions.

Question: What is the difference between the two approaches?


Optical character recognition of the Orthodox Hellenic Byzantine Music notation. In this paper we present for the first time, the development of a new system for the off-line optical recognition of the characters used in the Orthodox Hellenic Byzantine Music Notation, that has been established since 1814. We describe the structure of the new system and propose algorithms for the recognition of the 71 distinct character classes, based on Wavelets, 4-projections and other structural and statistical features. Using a Nearest Neighbor classifier, combined with a post classification schema and a tree-structured classification philosophy, an accuracy of 99.4 % was achieved, in a database of about 18,000 byzantine character patterns that have been developed for the needs of the system. Optical music recognition Off-line character recognition, Byzantine Music, Byzantine Music Notation, Wavelets, Projections, Neural Networks Contour processing, Nearest Neighbor Classifier Byzantine Music Data Base

Question: What is the off-line character recognition of the Orthodox Hellenic Byzantine Music notation?


A review of optical Braille recognition. The process to take a Braille document image and convert its content into its equivalent natural language characters, is called Optical Braille Recognition (OBR). It involves two main consecutive steps: Braille cell recognition and Braille cell transcription. Braille cell recognition contains few steps including: Image acquisition, Image De-skewing, Image pre-processing, dot recognition, cell recognition and segmentation. Image transcription aims to convert the segmented Braille cell, into its equivalent natural language characters. In this survey we aim to study the earlier works done by other researchers on both Braille cell recognition and transcription.

Question: What is the process of optical braille recognition?


Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation. For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering, classification, and anomaly detection on several real datasets.

Question: How to find the similarity between two time series?


Categorical Data Clustering using Frequency and Tf-Idf based Cosine Similarity. Clustering is the process of grouping a set of physical objects into classes of similar object. Objects in real world consist of both numerical and categorical data. Categorical data are not analyzed as numerical data due to the lack of inherit ordering. This paper describes Frequency and Tf-Idf Based Categorical Data Clustering (FTCDC) technique based on cosine similarity measure. The FTCDC system consists of four modules, such as data pre-processing, similarity matrix generation, cluster formation and validation. The System architecture of FTCDC is explained and its performance is examined using a simple example scenario. The performance on real world data is measured using accuracy and error rate. The performance of the system much relay on the similarity threshold selected for the clustering process.

Question: What is the difference between categorical and numerical data?


A scrum-based approach to CMMI maturity level 2 in web development environments. Scrum has become one of the most popular agile methodologies, either alone or combined with other agile practices. Besides, CMMI (Capability Maturity Model Integration) is accepted as a suitable model to measure the maturity of the organizations when developing or acquiring software. Although these two approaches are often considered antagonist, the use of an agile approach to reach certain CMMI maturity levels may result beneficial to organizations that develop Web systems, since they would take the advantages of both approaches. In Web community, this union may be very interesting, because agile approaches fits with the special needs of Web development, and they could be a useful tool for companies getting a certain grade of maturity. This work analyzes the goals of CMMI maturity level 2 and the feasibility of achieving them using the practices proposed by Scrum, trying to assess whether the use of this methodology is suitable for meeting the CMMI generic and specific goals or not. Finally, and based on this analysis, this paper raises a possible extension of Scrum, based on agile techniques, to accommodate the CMMI maturity level 2.

Question: What is the difference between CMMI and Scrum?


SOK: (State of) The Art of War: Offensive Techniques in Binary Analysis. Finding and exploiting vulnerabilities in binary code is a challenging task. The lack of high-level, semantically rich information about data structures and control constructs makes the analysis of program properties harder to scale. However, the importance of binary analysis is on the rise. In many situations binary analysis is the only possible way to prove (or disprove) properties about the code that is actually executed. In this paper, we present a binary analysis framework that implements a number of analysis techniques that have been proposed in the past. We present a systematized implementation of these techniques, which allows other researchers to compose them and develop new approaches. In addition, the implementation of these techniques in a unifying framework allows for the direct comparison of these apporaches and the identification of their advantages and disadvantages. The evaluation included in this paper is performed using a recent dataset created by DARPA for evaluating the effectiveness of binary vulnerability analysis techniques. Our framework has been open-sourced and is available to the security community.

Question: What is the state of the art in binary analysis?


A video object tracking algorithm combined Kalman filter and adaptive least squares under occlusion. Object occlusion is widespread in video surveillance due to the influence of angle and environment, which brings a great impact on the target tracking and makes the development of video object tracking encountered meeting many constraints. The challenge in video object tracking is how to track accurately when the target is obscured. We use the frame difference method to detect the target and smooth the target trajectory by using Kalman filter. When the target is occluded, we can select the appropriate fitting section after smoothing trajectories and use the least square method to fit the target motion path adaptively, so we can predict the object location. By comparing the experimental results with the actual motion of the target, it shows that the algorithm can be used to track most of the occlusion targets precisely.

Question: How to track an object when it is occluded?


Model-based vision: a program to see a walking person. For a machine to be able to 'see', it must know something about the object it is 'looking' at, A common method in machine vision is to provide the machine with general rather than specific knowledge about the object. An alternative technique, and the one used in thti paper, b a model-based approach in which particulars aboat the object are given and this drives the analysis. The computer program described here, the WALKER model, maps images into a description in which a person is represented by the series of hierarchical levels, i.e. a person has an arm which has a lower-arm which has a hand. l7te performance of the program ti illustrated by superimposing the machinegenerated picture over the original photographic images.

Question: What is a model-based vision?


Robust Audio Hashing for Content Identification. Nowadays most audio content identification systems are based on watermarking technology. In this paper we present a different technology, referred to as robust audio hashing. By extracting robust features and translating them into a bit string, we get an object called a robust hash. Content can then be identified by comparing hash values of a received audio clip with the hash values of previously stored original audio clips. A distinguishing feature of the proposed hash scheme is its ability to extract a bit string for every so many milliseconds. More precisely, for every windowed time interval a hash value of 32 bits is computed by thresholding energy differences of several frequency bands. A sequence of 256 hash values, corresponding to approximately 3 seconds of audio, can uniquely identify a song. Experimental results show that the proposed scheme is robust against severe compression, but bit errors do occur. This implies that searching and matching is a non-trivial task for large databases. For instance, a brute force search approach is already prohibitive for databases containing hash values of more than 100 songs. Therefore we propose a scheme that exploits a structured search algorithm that allows searching databases containing over 100,000 songs.

Question: What is the difference between audio hashing and watermarking?


Romantic Relationships among Young Adults : An Attachment Perspective. The present study used adult attachment as a theoretical framework to investigate individual differences in young adults' romantic relationships. Emphasis was put on studying the relational aspects of the self and coping strategies in an attempt to better understand how romantic partners feel about themselves and how they subsequently behave in periods of conflict. The sample comprised undergraduate university students (N=377) who responded to a self report questionnaire in English. Most findings were consistent with the proposed hypotheses. Results indicated that anxious individuals tend to report an ambivalent coping style and that fearful individuals were most vulnerable to health risks such as depression. Gender differences were also explored and results showed for instance that women generally tend to seek more support, and men might be more dismissing than women.

Question: What is the difference between anxious and fearful individuals?


Unobservable Communication over Fully Untrusted Infrastructure. Keeping communication private has become increasingly important in an era of mass surveillance and statesponsored attacks. While hiding the contents of a conversation has well-known solutions, hiding the associated metadata (participants, duration, etc.) remains a challenge, especially if one cannot trust ISPs or proxy servers. This paper describes a communication system called Pung that provably hides all content and metadata while withstanding global adversaries. Pung is a key-value store where clients deposit and retrieve messages without anyone— including Pung's servers—learning of the existence of a conversation. Pung is based on private information retrieval, which we make more practical for our setting with new techniques. These include a private multi-retrieval scheme, an application of the power of two choices, and batch codes. These extensions allow Pung to handle 103× more users than prior systems with a similar threat model.

Question: How does pung work?


LIMERIC: a linear message rate control algorithm for vehicular DSRC systems. Wireless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g. a vehicle's speed and location, or a traffic signal's state and timing). The aggregate of these uncoordinated broadcasts will lead to channel congestion under dense traffic scenarios, with a resulting reduction in the effectiveness of the collision avoidance applications making use of the transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology for deployment. This paper defines a new congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This "perfect" convergence avoids the limit cycle behavior inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including: guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust stability under all conditions, convergence with asynchronous updates, and the implications of measurement noise for statistical properties of convergence. The paper illustrates key analytical results using MATLAB numerical results, and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in

Question: What is the difference between a linear message rate control algorithm and a binary message rate control algorithm?


Mind's eye: A recurrent visual representation for image caption generation. In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans 21.0% of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.

Question: What is the bi-directional mapping between images and their sentence-based descriptions?


Musical Instrument Recognition in User-generated Videos using a Multimodal Convolutional Neural Network Architecture. This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval (MIR) field, where current approaches rely on the analysis of the good-quality audio material. This work addresses a real-world scenario with several research challenges, i.e. the analysis of user-generated videos that are varied in terms of recording conditions and quality and may contain multiple instruments sounding simultaneously and background noise. Our approach does not only focus on the analysis of audio information, but we exploit the multimodal information embedded in the audio and visual domains. In order to do so, we develop a Convolutional Neural Network (CNN) architecture which combines learned representations from both modalities at a late fusion stage. Our approach is trained and evaluated on two large-scale video datasets: YouTube-8M and FCVID. The proposed architectures demonstrate state-of-the-art results in audio and video object recognition, provide additional robustness to missing modalities, and remains computationally cheap to train.

Question: What is the convolutional neural network?


Compact Nonlinear Maps and Circulant Extensions. Kernel approximation via nonlinear random feature maps is w idely used in speeding up kernel machines. There are two main challenges for the conventional kernel ap proximation methods. First, before performing kernel approximation, a good kernel has to be chosen. Pickin g a good kernel is a very challenging problem in itself. Second, high-dimensional maps are often required i n order to achieve good performance. This leads to high computational cost in both generating the nonlinear ma ps, nd in the subsequent learning and prediction process. In this work, we propose to optimize the nonlinear m ps directly with respect to the classification objective in a data-dependent fashion. The proposed approa ch achieves kernel approximation and kernel learning in a joint framework. This leads to much more compact maps wit hout hurting the performance. As a by-product, the same framework can also be used to achieve more compact ke rnel maps to approximate a known kernel. We also introduce Circulant Nonlinear Maps, which uses a circu lant-structured projection matrix to speed up the nonlinear maps for high-dimensional data.

Question: What is the difference between a kernel and a nonlinear map?


Static analysis tools as early indicators of pre-release defect density. During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring further testing. We present an empirical approach for the early prediction of pre-release defect density based on the defects found using static analysis tools. The defects identified by two different static analysis tools are used to fit and predict the actual pre-release defect density for Windows Server 2003. We show that there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance. Discriminant analysis shows that the results of static analysis tools can be used to separate high and low quality components with an overall classification rate of 82.91%.

Question: What is the pre-release defect density of windows server 2003?


D Region Proposal U-Net with Dense and Residual Learning for Lung Nodule Detection.

Question: What is the difference between dense and residual learning?


Taming Subgraph Isomorphism for RDF Query Processing. RDF data are used to model knowledge in various areas such as l ife sciences, Semantic Web, bioinformatics, and social graphs . The size of real RDF data reaches billions of triples. This calls for a framework for efficiently processing RDF data. The core func tion of processing RDF data is subgraph pattern matching. There h av been two completely different directions for supporting ef ficient subgraph pattern matching. One direction is to develop spec ialized RDF query processing engines exploiting the properties of R DF data for the last decade, while the other direction is to deve lop fficient subgraph isomorphism algorithms for general, labele d graphs for over 30 years. Although both directions have a similar go al (i.e., finding subgraphs in data graphs for a given query grap h), they have been independently researched without clear reas on. We argue that a subgraph isomorphism algorithm can be easily mo dified to handle the graph homomorphism, which is the RDF patter n matching semantics, by just removing the injectivity const raint. In this paper, based on the state-of-the-art subgraph isomorp hism algorithm, we propose an in-memory solution, TurboHOM++, which is tamed for the RDF processing, and we compare it with the rep resentative RDF processing engines for several RDF benchmark s in a server machine where billions of triples can be loaded in mem ory. In order to speed upTurboHOM++, we also provide a simple yet effective transformation and a series of optimization tech niques. Extensive experiments using several RDF benchmarks show th at TurboHOM++ consistently and significantly outperforms the representative RDF engines. Specifically, TurboHOM++ outperforms its competitors by up tofiveorders of magnitude.

Question: What is the size of the canadian military?


SemLens: visual analysis of semantic data with scatter plots and semantic lenses. Querying the Semantic Web and analyzing the query results are often complex tasks that can be greatly facilitated by visual interfaces. A major challenge in the design of these interfaces is to provide intuitive and efficient interaction support without limiting too much the analytical degrees of freedom. This paper introduces SemLens, a visual tool that combines scatter plots and semantic lenses to overcome this challenge and to allow for a simple yet powerful analysis of RDF data. The scatter plots provide a global overview on an object collection and support the visual discovery of correlations and patterns in the data. The semantic lenses add dimensions for local analysis of subsets of the objects. A demo accessing DBpedia data is used for illustration.

Question: What is a scatter plot?


IP Lookup using Two-level Indexing and B-Trees. Networks are expanding very fast and the number of clients is increasing dramatically, this causes the router forwarding table to become very large and present more demand on faster router operations. In this paper, we address the problem of packet forwarding in the routers aiming to increase the speed of address lookup and minimize the memory required for storing the forwarding table. We propose a new algorithm that makes use of two-level indexing and Btrees. We test the approach and compare it to other famous IP lookup approaches. The preliminary simulations show 20% less memory requirements and the lookup speed scaling linearly with increasing table size.

Question: How to lookup IP address?


Development of 24 GHz rectennas for Fixed Wireless Access. We need electricity to use wireless information. If we reduce amount of batteries or electrical wires with a wireless power transmission technology via microwave (MPT), it is a green communication system. We Kyoto University propose a Fixed Wireless Access (FWA) system with the MPT with NTT, Japan. In this paper, we show mainly development results of 24GHz rectennas, rectifying antenna, for FWA. We developed some types of the rectennas. Finally we achieve 65% of RF-DC conversion efficiency with output filter of harmonic balance.

Question: What is a rectenna?


The Runtime System Problem Identification Method Based on Log Analysis. Currently system logs are an important source of information for system administrators to monitor system behaviors and to identify system problems. The manual examining is infeasible for the complex system and the existing automated methods for identifying system problems have different disadvantages such as the extreme dependency on the source code of the system, the low accuracy of predicting or identifying the system problems, or the requirement of the balanced and labeled training data set. This paper proposes a one- class Support Vector Machine (OCSVM) based method to identify the runtime system problems. Firstly, log sequences are generated for describing the running trajectories of the monitored system by parsing log messages; Secondly, variable length n-gram features are extracted, and moreover, the log sequences are represented as feature vectors based on these variable length n-gram features and Vector Space Model (VSM). Finally, all the feature vectors of the training log sequence set, which only includes the labeled normal log sequences, are input into OCSVM. Experimental results show that it performs better to use linear kernel to train OCSVM on our feature vectors than Gaussian kernel and the size of the sliding window hardly affects the performance of our method. Moreover, the proposed method achieves better performance on unbalanced training dataset than the method based on Random Indexing (RI) and weighted SVM.

Question: What is the runtime system problem identification method based on log analysis?


Improving TCP Throughput over Two-Way Asymmetric Links: Analysis and Solutions. The sharing of a common buffer by TCP data segments and acknowledgments in a network or internet has been known to produce the effect of ack compression, often causing dramatic reductions in throughput. We study several schemes for improving the performance of two-way TCP traffic over asymmetric links where the bandwidths in the two directions may differ substantially, possibly by many orders of magnitude. These approaches reduce the effect of ack compression by carefully controlling the flow of data packets and acknowledgments. We first examine a scheme where acknowledgments are transmitted at a higher priority than data. By analysis and simulation, we show that prioritizing acks can lead to starvation of the low-bandwidth connection. Next, we introduce and analyze a connection-level backpressure mechanism designed to limit the maximum amount of data buffered in the outgoing IP queue of the source of the low-bandwidth connection. We show that this approach, while minimizing the queueing delay for acks, results in unfair bandwidth allocation on the slow link. Finally, our preferred solution separates the acks from data packets in the outgoing queue, and makes use of a connection-level bandwidth allocation mechanism to control their bandwidth shares. We show that this scheme overcomes the limitations of the previous approaches, provides isolation, and enables precise control of the connection throughputs. We present analytical models of the dynamic behavior of each of these approaches, derive closed-form expressions for the expected connection efficiencies in each case, and validate them with simulation results.

Question: How to improve TCP throughput over two-way asymmetric links?


Surface-Wave Launchers for Beam Steering and Application to Planar Leaky-Wave Antennas. A linear array of surface-wave launchers (SWLs) is presented for surface-wave (SW) beam steering on a grounded dielectric slab (GDS). Specifically, a two-element array of directive Yagi-Uda like SWL antennas (operating between 21-24 GHz) is investigated. By varying the relative phase difference between SWL elements, the excited SW field distribution can be steered. In addition, a six-element linear array of SWLs with nonuniform weighting is also presented. If metallic gratings are placed on top of the GDS, the bound SW mode can be transformed into a radiated leaky-wave (LW) mode and directive beam patterns can be generated in the far field. A specific elliptical grating configuration is also investigated demonstrating single frequency cylindrical-sector LW beam steering. These slotted SWL arrays can be useful for novel SW beam scanning designs, millimeter wave power distribution systems and new LW antennas.

Question: What is a surface wave launcher?


Deep Neural Networks for Acoustic Modeling in Speech Recognition. Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feedforward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks with many hidden layers, that are trained using new methods have been shown to outperform Gaussian mixture models on a variety of speech recognition benchmarks, sometimes by a large margin. This paper provides an overview of this progress and represents the shared views of four research groups who have had recent successes in using deep neural networks for acoustic modeling in speech recognition.

Question: What is the difference between a neural network and a neural network?


What are the Gaps in Mobile Patient Portal? Mining Users Feedback Using Topic Modeling. Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. In this article, we extend the existing literature by discovering design gaps for patient portals from a systematic analysis of negative users' feedback from the actual use of patient portals. Specifically, we adopt topic modeling approach, LDA algorithm, to discover design gaps from online low rating user reviews of a common mobile patient portal, EPIC's mychart. To validate the extracted gaps, we compared the results of LDA analysis with that of human analysis. Overall, the results revealed opportunities to improve collaboration and to enhance the design of portals intended for patientcentered care.

Question: What are the gaps in mobile patient portal?


A Unified Local and Global Model for Discourse Coherence. We present a model for discourse coherence which combines the local entitybased approach of (Barzilay and Lapata, 2005) and the HMM-based content model of (Barzilay and Lee, 2004). Unlike the mixture model of (Soricut and Marcu, 2006), we learn local and global features jointly, providing a better theoretical explanation of how they are useful. As the local component of our model we adapt (Barzilay and Lapata, 2005) by relaxing independence assumptions so that it is effective when estimated generatively. Our model performs the ordering task competitively with (Soricut and Marcu, 2006), and significantly better than either of the models it is based on.

Question: What is a unified local and global model for discourse coherence?


Measuring the Diversity of Automatic Image Descriptions. Automatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of generic descriptions as a lack of diversity in the output, which we quantify using established metrics and two new metrics that frame image description as a word recall task. This framing allows us to evaluate system performance on the head of the vocabulary, as well as on the long tail, where system performance degrades. We use these metrics to examine the diversity of the sentences generated by nine state-of-the-art systems on the MS COCO data set. We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives. However, the adversarially-trained models only produce more types from the head of the vocabulary and not the tail. Besides vocabulary-based methods, we also look at the compositional capacity of the systems, specifically their ability to create compound nouns and prepositional phrases of different lengths. We conclude that there is still much room for improvement, and offer a toolkit to measure progress towards the goal of generating more diverse image descriptions.

Question: How do automatic image description systems produce generic sentences?


A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration. We generalize the alternating minimization algorithm recently proposed in [32] to efficiently solve a general, edge-preserving, variational model for recovering multichannel images degraded by withinand cross-channel blurs, as well as additive Gaussian noise. This general model allows the use of localized weights and higher-order derivatives in regularization, and includes a multichannel extension of total variation (MTV) regularization as a special case. In the MTV case, we show that the model can be derived from an extended half-quadratic transform of Geman and Yang [14]. For color images with three channels and when applied to the MTV model (either locally weighted or not), the per-iteration computational complexity of this algorithm is dominated by nine fast Fourier transforms. We establish strong convergence results for the algorithm including finite convergence for some variables and fast q-linear convergence for the others. Numerical results on various types of blurs are presented to demonstrate the performance of our algorithm compared to that of the MATLAB deblurring functions. We also present experimental results on regularization models using weighted MTV and higher-order derivatives to demonstrate improvements in image quality provided by these models over the plain MTV model.

Question: What is the difference between edge-preserving and non-edge-preserving variational models?


Reading Comprehension with Deep Learning. We train a model that combines attention with multi-perspective matching to perform question answering. For each question and context pair in SQuAD, we perform an attention calculation over each context before extracting features of the question and context, matching them from multiple perspectives. Whilst we did not have time to perform a hyper-parameter search or incorporate other features into our model, this joint model obtained an F1 score of 22.5 and an EM score of 13.0, outperforming our baseline model without attention, which attained an F1 score of 19.4 and an EM score of 10.0. In the future, we would like to implement an early stopping feature and a co-dependent representation of each question and context pair.

Question: What is the difference between a deep learning model and a traditional model?


3-D TCAD simulation to optimize the trench termination design for higher and robust BVdss. Optimizing the edge termination design around the periphery of active area is critically important for achieving the highest and stable breakdown voltage (BVDSS) for any power devices. Active cell structures can be assumed as two dimensional (2-D) in the central part of the die, however as the active cells terminate to the termination regions at the periphery of the die, 2-D and 3-D transition regions are formed at different locations of the die layout with respect to the last edge termination trench. Optimization of the 3-D termination region is imperative to ascertain equal or higher BVDSS of the termination region than the active cell region. Synopsys advanced multi-dimensional TCAD device simulation tool - "Sentaurus Device Editor (SDE) [1]" is adopted for designing and optimizing the 3-D termination transition region for a higher and robust BVDSS, which is validated by the experimental data.

Question: What is the difference between 2-D and 3-D TCAD simulation?


Wasserstein GAN. The problem this paper is concerned with is that of unsupervised learning. Mainly, what does it mean to learn a probability distribution? The classical answer to this is to learn a probability density. This is often done by defining a parametric family of densities (Pθ)θ∈Rd and finding the one that maximized the likelihood on our data: if we have real data examples {x}i=1, we would solve the problem

Question: What is the difference between a parametric family of densities and a parametric family of distributions?


Personality and motivations associated with Facebook use. Facebook is quickly becoming one of the most popular tools for social communication. However, Facebook is somewhat different from other Social Networking Sites as it demonstrates an offline-to-online trend; that is, the majority of Facebook Friends are met offline and then added later. The present research investigated how the Five-Factor Model of personality relates to Facebook use. Despite some expected trends regarding Extraversion and Openness to Experience, results indicated that personality factors were not as influential as previous literature would suggest. The results also indicated that a motivation to communicate was influential in terms of Facebook use. It is suggested that different motivations may be influential in the decision to use tools such as Facebook, especially when individual functions of Facebook are being considered. Ó 2008 Elsevier Ltd. All rights reserved. 1. Personality correlates and competency factors associated

Question: What personality factors are associated with facebook use?


Local Rademacher Complexity Bounds based on Covering Numbers. This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm. This result is convenient to apply in real applications and could yield refined local Rademacher complexity bounds for function classes satisfying general entropy conditions. We demonstrate the power of our complexity bounds by applying them to derive effective generalization error bounds.

Question: What is the local Rademacher complexity?


Handling Multiword Expressions in Causality Estimation. Previous studies on causality estimation mainly aquire causal event pairs from a large corpus based on lexico-syntactic patterns and coreference relations, and estimate causality by a statistical method. However, most of the previous studies assume event pairs can be represented by a pair of single words, therefore they cannot estimate multiword causality correctly (e.g."tired"-"give up") . In this paper, we create a list of multiword expressions and extend an existing method. Our evaluation demonstrates that the proper treatment of multiword expression events is effective and the proposed method outperforms the state-of-the-art causality estimation model.

Question: How to handle multiword expressions in causality estimation?


Automated Generation of Attack Trees. Attack trees are widely used to represent threat scenarios in a succinct and intuitive manner, suitable for conveying security information to non-experts. The manual construction of such objects relies on the creativity and experience of specialists, and therefore it is error-prone and impracticable for large systems. Nonetheless, the automated generation of attack trees has only been explored in connection to computer networks and levering rich models, whose analysis typically leads to an exponential blow-up of the state space. We propose a static analysis approach where attack trees are automatically inferred from a process algebraic specification in a syntax-directed fashion, encompassing a great many application domains and avoiding incurring systematically an exponential explosion. Moreover, we show how the standard propositional denotation of an attack tree can be used to phrase interesting quantitative problems, that can be solved through an encoding into Satisfiability Modulo Theories. The flexibility and effectiveness of the approach is demonstrated on the study of a national-scale authentication system, whose attack tree is computed thanks to a Java implementation of the framework.

Question: What is the size of the canadian military?


Towards Data-Driven Autonomics in Data Centers. Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.

Question: What is the big data SQL platform from the Google Cloud suite?


Robust Lane Detection and Tracking in Challenging Scenarios. A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.

Question: What is a lane-detection system?


PinMe: Tracking a Smartphone User around the World. With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing sensory data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the user has turned off the Global Positioning System (GPS) on the device. Previous research efforts rely on at least one of the two following fundamental requirements, which impose significant limitations on the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., device acceleration, for different potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure and device's timezone, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off. Unlike previously-proposed attacks, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. We demonstrate that PinMe can accurately estimate the user's location during four activities (walking, traveling on a train, driving, and traveling on a plane). We also suggest several defenses against the proposed attack.

Question: How can we track a smartphone user?


LogP: Towards a Realistic Model of Parallel Computation. A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding development of techniques that yield performance across a range of current and future parallel machines. This paper offers a new parallel machine model, called LogP, that reflects the critical technology trends underlying parallel computers. it is intended to serve as a basis for developing fast, portable parallel algorithms and to offer guidelines to machine designers. Such a model must strike a balance between detail and simplicity in order to reveal important bottlenecks without making analysis of interesting problems intractable. The model is based on four parameters that specify abstractly the computing bandwidth, the communication bandwidth, the communication delay, and the efficiency of coupling communication and computation. Portable parallel algorithms typically adapt to the machine configuration, in terms of these parameters. The utility of the model is demonstrated through examples that are implemented on the CM-5.

Question: What is the CM-5?


The eyeBook – Using Eye Tracking to Enhance the Reading Experience. Introduction A rapid development of eye tracking technology has been observed in recent years. Today's eye trackers can determine the current focus point of the eye precisely while being relatively unobtrusive in their application. Also, a variety of research and commercial groups has been working on this technology, and there is a growing interest for such devices on the market. Eye tracking has great potential and it can be assumed that it will advance further and might become a widespread technology used at a large number of personal or office computer workplaces. Approaches using simple webcams for eye tracking already exist, for example webcams integrated into laptop computers by default. Thus, they allow for new kinds of applications using eye gaze data. However, not only eye tracking technology is advancing rapidly to an easily usable state. Additionally, during the past 100 years researchers gathered a considerable amount of knowledge on eye movements, why and how they occur, and what they might mean. So, today, we have the technology and knowledge for tracking and analyzing eye movements, making an excellent starting point for sophisticated interactive gaze-based applications. Naive approaches where gaze data is directly employed for interacting with the system, e. g., pressing buttons on the screen with the "blink of an eye" generally have serious problems. Because the eyes are organs used for perceiving the world and not for manipulating the world, it is hard and against human nature to control eye movements deliberately. However, a highly promising approach is just to observe eye movements of the user during his or her daily work in front of the computer, to infer user intentions based on eye movement behavior, and to provide assistance where helpful. Gaze can be seen as a proxy for the user's attention, and eye movements are known to be usually tightly coupled with cognitive processes i

Question: What is the difference between naive and sophisticated approaches?


Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals. Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit nonlinear and nonstationary behaviors. These characteristics tend to undermine the reliability of existing hand-crafted EEG features that ignore time-varying information and impair the performances of classification models. In this paper, we propose a novel method that can automatically capture the nonstationary dynamics of EEG signals for diverse classification tasks. It consists of two components. The first component uses an autoregressive-deep variational autoencoder model for automatic feature extraction, and the second component uses a Gaussian mixture-hidden Markov model for EEG classification with the extracted features. We compare the performance of our proposed method and the state-of-the-art methods in two EEG classification tasks, subject, and event classification. Results show that our approach outperforms the others by averages of <inline-formula><tex-math notation="LaTeX">$\text{15}\%\pm \text{6.3}$</tex-math> </inline-formula> (p-value <inline-formula><tex-math notation="LaTeX">$<\text{0.05}$</tex-math></inline-formula>) and <inline-formula><tex-math notation="LaTeX">$\text{22}\%\pm \text{5.7}$</tex-math></inline-formula> (p-value <inline-formula><tex-math notation="LaTeX">$<\text{0.05}$</tex-math></inline-formula>) for subject and event classifications, respectively.

Question: How does deep variational autoencoder work?


A 12.77-MHz 31 ppm/°C On-Chip RC Relaxation Oscillator With Digital Compensation Technique. The design of a 12.77-MHz on-chip RC relaxation oscillator with digital compensation technique is presented. To maintain the frequency stability versus temperature and supply voltage variations, loop delay tuning by a digital feedback loop is developed in this system. In order to generate an on-chip reference for digital calibration, a replica comparator is added. The on-chip relaxation oscillator is fabricated in 0.18-μm CMOS process. The measured output frequency variation is 31 ppm/°C across -30 to 120 °C temperature range after compensation. The frequency variation over the supply voltage from 0.6 V to 1.1 V is ±0.5%/V. The measured total power consumption is 56.2 μW at 0.9-V supply voltage when the digital compensation blocks are enabled. After digital compensation, the compensation blocks can be shutdown for power saving, and the main oscillator consumes only 12.8 μW.

Question: What is the frequency of a relaxation oscillator?


Cross-domain security of cyber-physical systems. The interaction between the cyber domain and the physical domain components and processes can be leveraged to enhance the security of the cyber-physical system. In order to do so, we must first analyze various cyber domain and physical domain information flows, and characterize the relation between them using model functions. In this paper, we present a notion of cross-domain security of cyber-physical systems, whereby we present a security analysis framework that can be used for generating novel cross-domain attack models, attack detection methods, etc. We demonstrate how information flows such as discrete domain signal flows and continuous domain energy flows in the cyber and physical domain can be used to generate model functions using data-driven estimation, and use this model functions for performing various cross-domain security analysis. We also demonstrate the practical applicability of the cross-domain security analysis framework using the cyber-physical manufacturing system as a case study.

Question: What is the relation between the cyber and physical domains?


Clustering short text using Ncut-weighted non-negative matrix factorization. Non-negative matrix factorization (NMF) has been successfully applied in document clustering. However, experiments on short texts, such as microblogs, Q&A documents and news titles, suggest unsatisfactory performance of NMF. An major reason is that the traditional term weighting schemes, like binary weight and tfidf, cannot well capture the terms' discriminative power and importance in short texts, due to the sparsity of data. To tackle this problem, we proposed a novel term weighting scheme for NMF, derived from the Normalized Cut (Ncut) problem on the term affinity graph. Different from idf, which emphasizes discriminability on document level, the Ncut weighting measures terms' discriminability on term level. Experiments on two data sets show our weighting scheme significantly boosts NMF's performance on short text clustering.

Question: How to cluster short text?


A Natural Language Tutorial Dialogue System for Physics. We describe theWHY2-ATLAS intelligent tutoring system for qualitative physics that interacts with students via natural language dialogue. We focus on the issue of analyzing and responding to multi-sentential explanations. We explore approaches for achieving a deeper understanding of these explanations and dialogue management approaches and strategies for providing appropriate feedback on them.

Question: What is a natural language tutorial dialogue system for physics?


Accelerating Online CP Decompositions for Higher Order Tensors. Tensors are a natural representation for multidimensional data. In recent years, CANDECOMP/PARAFAC (CP) decomposition, one of the most popular tools for analyzing multi-way data, has been extensively studied and widely applied. However, today's datasets are often dynamically changing over time. Tracking the CP decomposition for such dynamic tensors is a crucial but challenging task, due to the large scale of the tensor and the velocity of new data arriving. Traditional techniques, such as Alternating Least Squares (ALS), cannot be directly applied to this problem because of their poor scalability in terms of time and memory. Additionally, existing online approaches have only partially addressed this problem and can only be deployed on third-order tensors. To fill this gap, we propose an efficient online algorithm that can incrementally track the CP decompositions of dynamic tensors with an arbitrary number of dimensions. In terms of effectiveness, our algorithm demonstrates comparable results with the most accurate algorithm, ALS, whilst being computationally much more efficient. Specifically, on small and moderate datasets, our approach is tens to hundreds of times faster than ALS, while for large-scale datasets, the speedup can be more than 3,000 times. Compared to other state-of-the-art online approaches, our method shows not only significantly better decomposition quality, but also better performance in terms of stability, efficiency and scalability.

Question: How to decompose a tensor?


Preparation of Personalized-dose Salbutamol Sulphate Oral Films with Thermal Ink-Jet Printing. To evaluate the use of thermal ink-jetting as a method for dosing drugs onto oral films. A Hewlett-Packard printer cartridge was modified so that aqueous drug solutions replaced the ink. The performance of the printer as a function of print solution viscosity and surface tension was determined; viscosities between 1.1 and 1.5 mm2 s-1 were found to be optimal, while surface tension did not affect deposition. A calibration curve for salbutamol sulphate was prepared, which demonstrated drug deposition onto an acetate film varied linearly with concentration (r2 = 0.9992). The printer was then used to deposit salbutamol sulphate onto an oral film made of potato starch. It was found that when doses were deposited in a single pass under the print head, then the measured dose was in good agreement with the theoretical dose. With multiple passes the measured dose was always significantly less than the theoretical dose. It is proposed that the losses arise from erosion of the printed layer by shearing forces during paper handling. The losses were predictable, and the variance in dose deposited was always less than the BP limits for tablet and oral syrup salbutamol sulphate preparations. TIJ printing offers a rapid method for extemporaneous preparation of personalized-dose medicines.

Question: How does thermal ink-jetting work?


OCR for bilingual documents using language modeling. Script based features are highly discriminative for text segmentation and recognition. Thus they are widely used in Optical Character Recognition(OCR) problems. But usage of script dependent features restricts the adaptation of such architectures directly for another script. With script independent systems, this problem can be solved to a certain extent for monolingual documents. But the problem aggravates in case of multilingual documents as it is very difficult for a single classifier to learn many scripts. Generally a script identification module identifies text segments and accordingly the script-dependent classifier is selected. This paper presents a unified framework of language model and multiple preprocessing hypotheses for word recognition from bilingual document images. Prior to text recognition, preprocessing steps such as binarization and segmentation are required for ease of recognition. But these steps induce huge combinatorial error propagating to final recognition accuracy. In this paper we use multiple preprocessing routines as alternate hypotheses and use a language model to verify each alternative and choose the best recognized sequence. We test this architecture for word recognition of Kannada-English and Telugu-English bilingual documents and achieved better recognition rates than single methods using same classifier.

Question: How to recognize a document in multiple languages?


Direct Manipulation Interfaces. Approach • Social information independent of physical analog s – Text (e.g., emote) – abstract graphical representations (e.g., chat cir cles) • Interested in Abstract Approach a. Creates and deploys working systems b. Lack of attention Babble Prototype

Question: What is the difference between a prototype and a working system?


Musculoskeletal symptoms among mobile hand-held device users and their relationship to device use: A preliminary study in a Canadian university population.. The study aims were, in a population of university students, staff, and faculty (n = 140), to: 1) determine the distribution of seven measures of mobile device use; 2) determine the distribution of musculoskeletal symptoms of the upper extremity, upper back and neck; and 3) assess the relationship between device use and symptoms. 137 of 140 participants (98%) reported using a mobile device. Most participants (84%) reported pain in at least one body part. Right hand pain was most common at the base of the thumb. Significant associations found included time spent internet browsing and pain in the base of the right thumb (odds ratio 2.21, 95% confidence interval 1.02-4.78), and total time spent using a mobile device and pain in the right shoulder (2.55, 1.25-5.21) and neck (2.72, 1.24-5.96). Although this research is preliminary, the observed associations, together with the rising use of these devices, raise concern for heavy users.

Question: What is the relationship between mobile device use and musculoskeletal symptoms?


Predicting patient revisits at the University of Virginia Health System Emergency Department. This study focuses on the predictive identification of patients frequently revisiting the University of Virginia Health System Emergency Department. Identifying these patients can help the Emergency Department formulate strategies that improve patient care and decrease excess Emergency Department utilization. The Health System in particular faces a number of unique challenges in its ongoing mission to reduce extraneous patient revisits. In addition to its status as an academic hospital, it serves a broad geographic region as one of five level-I trauma centers in the Commonwealth of Virginia. In this study we utilized 5 years of data from the University of Virginia Health System data warehouse. These data contain information on 91,297 patients and 196,902 unique encounters, including details on patient demographics, diagnoses and hospital departments visited. From these raw data we engineered features, trained gradient boosted decision trees, and experimented with unsupervised clustering techniques to best approximate 30-day Emergency Department revisit risk at the conclusion of each patient encounter. Our best model for revisit risk resulted in a Receiver Operator Characteristic Area Under the Curve of 0.75. Furthermore, we exhibit the real-time performance of our model as a tool to rank which at-risk patients should receive priority for Emergency Department resources. This test demonstrated a significant improvement over the current allocation of Emergency Department social worker resources with a daily Mean Average Precision of 0.83. The methodologies proposed in this paper exhibit an end-to-end framework to transform raw administrative claims and limited clinical data into predictive models that help the Emergency Department better manage resources and target interventions.

Question: How can we predict patient revisits at the University of Virginia Health System Emergency Department?


Differentially Private Empirical Risk Minimization. Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.

Question: What is the difference between the objective perturbation and the output perturbation?


Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches. Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like Go. While RL is emerging as a practical component in reallife systems, most successes have been in Single Agent domains. This report will instead specifically focus on challenges that are unique to Multi-Agent Systems interacting in mixed cooperative and competitive environments. The report concludes with advances in the paradigm of training Multi-Agent Systems called Decentralized Actor, Centralized Critic, based on an extension of MDPs called Decentralized Partially Observable MDPs, which has seen a renewed interest lately. 1 ar X iv :1 80 7. 09 42 7v 1 [ cs .A I] 2 5 Ju l 2 01 8

Question: What is the difference between a single agent and a multi agent?


Modern electrical machine analysis and design techniques applied to hybrid vehicle drive machines. In this paper a review is made of the different analysis and design techniques that can be used in modern electrical machine design, and references them to the developing area of hybrid vehicle drives. These machines are characterized by high transient torque requirement, compactness and forced cooling. While rare-earth magnet machines are commonly used in these applications, there is an increasing interest in motors without magnets. An example is made of the published Toyota Prius permanent-magnet machine specification. An alternative induction motor design is addressed.

Question: What is the Toyota Prius?


JSpIRIT: a flexible tool for the analysis of code smells. Code smells are a popular mechanism to identify structural design problems in software systems. Since it is generally not feasible to fix all the smells arising in the code, some of them are often postponed by developers to be resolved in the future. One reason for this decision is that the improvement of the code structure, to achieve modifability goals, requires extra effort from developers. Therefore, they might not always spend this additional effort, particularly when they are focused on delivering customer-visible features. This postponement of code smells are seen as a source of technical debt. Furthermore, not all the code smells may be urgent to fix in the context of the system's modifability and business goals. While there are a number of tools to detect smells, they do not allow developers to discover the most urgent smells according to their goals. In this article, we present a fexible tool to prioritize technical debt in the form of code smells. The tool is flexible to allow developer s to add new smell detection strategies and to prioritize smells, and groups of smells, based on the confguration of their manifold criteria. To illustrate this flexibility, we present an application example of our tool. The results suggest that our tool can be easily extended to be aligned with the developer's goals.

Question: What is a fexible tool for the analysis of code smells?