An Architecture for Action, Emotion, and Social Behavior. The Oz project at Carnegie Mellon is studying the construction of artistically e ective simulated worlds. Such worlds typically include several agents, which must exhibit broad behavior. To meet this need, we are developing an agent architecture, called Tok, that presently supports reactivity, goals, emotions, and social behavior. Here we brie y introduce the requirements of our application, summarize the Tok architecture, and describe a particular agent we have constructed that exhibits the desired social behavior. 1 THE OZ PROJECT AND BROAD AGENTS 1 1 The Oz Project and Broad Agents The Oz project at Carnegie Mellon University is developing technology for artistically interesting simulated worlds [3]. We want to let human users participate in dramatically e ective worlds that include moderately competent, emotional agents. We work with artists in the CMU Drama and English Departments, to help focus our technology on genuine artistic needs. An Oz world has four primary components. There is a simulated physical environment, a set of automated agents which help populate the world, a user interface to allow one or more people to participate in the world [14], and a planner concerned with the long term structure of the user's experience [2]. One of the keys to an artistically engaging experience is for the user to be able to \suspend disbelief". That is, the user must be able to imagine that the world portrayed is real, without being jarred out of this belief by the world's behavior. The automated agents, in particular, must not be blatantly unreal. Thus, part of our e ort is aimed at producing agents with a broad set of capabilities, including goal-directed reactive behavior, emotional state and behavior, social knowledge and behavior, and some natural language abilities. For our purpose, each of these capacities can be as limited as is necessary to allow us to build broad, integrated agents [4]. Oz worlds can

Question: What is the purpose of the Oz project?


Don't Interrupt Me While I Type: Inferring Text Entered Through Gesture Typing on Android Keyboards. We present a new side-channel attack against soft keyboards that support gesture typing on Android smartphones. An application without any special permissions can observe the number and timing of the screen hardware interrupts and system-wide software interrupts generated during user input, and analyze this information to make inferences about the text being entered by the user. System-wide information is usually considered less sensitive than app-specific information, but we provide concrete evidence that this may be mistaken. Our attack applies to all Android versions, including Android M where the SELinux policy is tightened. We present a novel application of a recurrent neural network as our classifier to infer text. We evaluate our attack against the "Google Keyboard" on Nexus 5 phones and use a real-world chat corpus in all our experiments. Our evaluation considers two scenarios. First, we demonstrate that we can correctly detect a set of pre-defined "sentences of interest" (with at least 6 words) with 70% recall and 60% precision. Second, we identify the authors of a set of anonymous messages posted on a messaging board. We find that even if the messages contain the same number of words, we correctly re-identify the author more than 97% of the time for a set of up to 35 sentences. Our study demonstrates a new way in which systemwide resources can be a threat to user privacy. We investigate the effect of rate limiting as a countermeasure but find that determining a proper rate is error-prone and fails in subtle cases. We conclude that real-time interrupt information should be made inaccessible, perhaps via a tighter SELinux policy in the next Android version.

Question: How to detect text entered through gesture typing on Android?


Question: What is evolutionary computing?


Implementation of 3D Obstacle Compliant Mobility Models for UAV Networks in ns-3. UAV networks are envisioned to play a crucial role in the future generations of wireless networks. The mechanical degrees of freedom in the movement of UAVs provides various advantages for tactical and civilian applications. Due to the high cost of failures in system-based tests, initial analysis and refinement of designs and algorithms for UAV applications are performed through rigorous simulations. Current trend of UAV specific simulators is mainly biased towards the mechanical properties of flying. For network-centric simulations, the intended measurements on the performance of protocols in mobile scenarios are conventionally captured from general-purpose network simulators, which are not natively equipped with comprehensive models for 3D movements of UAVs. To facilitate such simulations for UAV systems, this paper presents different mobility models for emulation of the movement of a UAV. Detailed description of three mobility models (random walk, random direction, and Gauss-Markov) are presented, and their associated movement patterns are characterized. This characterization is further extended by considering the effect of large obstacles on movement patterns of nodes following the three models. The mobility models are prepared as open-source add-ons for ns-3 network simulator.

Question: How to simulate UAV movement in ns-3?


Learning temporal features using LSTM-CNN architecture for face anti-spoofing. Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93% vs. 7.34%), and hand-crafted features (5.93% vs. 10.00%) on CASIA dataset.

Question: What is the best architecture for face anti-spoofing?


An exploration of crowdsourcing citation screening for systematic reviews. Systematic reviews are increasingly used to inform health care decisions, but are expensive to produce. We explore the use of crowdsourcing (distributing tasks to untrained workers via the web) to reduce the cost of screening citations. We used Amazon Mechanical Turk as our platform and 4 previously conducted systematic reviews as examples. For each citation, workers answered 4 or 5 questions that were equivalent to the eligibility criteria. We aggregated responses from multiple workers into an overall decision to include or exclude the citation using 1 of 9 algorithms and compared the performance of these algorithms to the corresponding decisions of trained experts. The most inclusive algorithm (designating a citation as relevant if any worker did) identified 95% to 99% of the citations that were ultimately included in the reviews while excluding 68% to 82% of irrelevant citations. Other algorithms increased the fraction of irrelevant articles excluded at some cost to the inclusion of relevant studies. Crowdworkers completed screening in 4 to 17 days, costing $460 to $2220, a cost reduction of up to 88% compared to trained experts. Crowdsourcing may represent a useful approach to reducing the cost of identifying literature for systematic reviews.

Question: What is crowdsourcing?


Syntactic Clustering of the Web. We have developed an efficient way to determine the syntactic similarity of files and have applied it to every document on the World Wide Web. Using this mechanism, we built a clustering of all the documents that are syntactically similar. Possible applications include a "Lost and Found" service, filtering the results of Web searches, updating widely distributed web-pages, and identifying violations of intellectual property rights.

Question: How does the web work?


Method engineering: engineering of information systems development methods and tools. This paper proposes the term method engineering for the research field of the construction of information systems development methods and tools. Some research issues in method engineering are identified. One major research topic in method engineering is discussed in depth: situational methods, i.e. the configuration of a project approach that is tuned to the project at hand. A language and support tool for the engineering of situational methods are discussed.

Question: What is method engineering?


Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis. Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our WarpingGAN that significantly outperforms all existing methods on two large datasets.

Question: What is the soft-gated warping-block?


Chip design of a 12-bit 5MS/s fully differential SAR ADC with resistor-capacitor array DAC technique for wireless application. A 1.8-V 12-bit 5MS/s successive approximation register (SAR) analog-to-digital converter (ADC) implemented in TSMC 0.18-um CMOS process is presented. To reduce DAC switching energy and chip area, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch designs. With 1.8 V supply voltage and 5.0 MHz sampling rate, measured results achieve -0.55/0.72 LSB (Least Significant Bit) of DNL (differential nonlinearity) and -0.78/0.92 LSB of integral nonlinearity (INL) respectively, and 10.76 bits of an effective number of bits (ENOB) at 1MHz input frequency. The chip area is 0.83 mm2 including pads and the power consumption is 490μW for optical and wireless communications.

Question: What is the chip area of the ADC?


Advanced Data Mining Techniques. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. I dedicate this book to my grandchildren. Preface The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncertain description or other complexities that cause difficulty for the conventional approaches of logistic regression, neural network models, and decision trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Our intent is to cover the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided. Chapter 2 presents the data mining process in more detail. It demonstrates this process with a typical set of data. Visualization o

Question: What is the data mining process?


Effective Defense Schemes for Phishing Attacks on Mobile Computing Platforms. Recent years have witnessed the increasing threat of phishing attacks on mobile computing platforms. In fact, mobile phishing is particularly dangerous due to the hardware limitations of mobile devices and the habits of mobile users. In this paper, we did a comprehensive study on the security vulnerabilities caused by mobile phishing attacks, including web page phishing attacks, application phishing attacks, and account registry phishing attacks. Existing schemes designed for web phishing attacks on personal computers (PCs) cannot effectively address the various phishing attacks on mobile devices. Hence, we propose MobiFish, which is a novel automated lightweight antiphishing scheme for mobile platforms. MobiFish verifies the validity of web pages, applications, and persistent accounts by comparing the actual identity to the claimed identity. MobiFish has been implemented on a Nexus 4 smartphone running the Android 4.2 operating system. We experimentally evaluate the performance of MobiFish with 100 phishing URLs and corresponding legitimate URLs, as well as phishing apps. The results show that MobiFish is very effective in detecting phishing attacks on mobile phones.

Question: What is the difference between a phishing attack and a phishing scheme?


Improving Comprehension of Measurements Using Concrete Re-expression Strategies. It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete re-expression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Crowdsourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re- expressions help them to better estimate new measurements.

Question: How many people are in the Canadian military?


Communities' Detection in Social Networks: State of the art and perspectives. Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities and clustering is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature, such as the algorithms of Newman and Girvan. However, most solutions have common problems: the stability and the community structures quality. In this paper, we give a state of the art, from 1970 until 2018 on the approaches as well as the algorithms of communities' detection in social networks. This study aims to gives a comparison between the different proposed architectures;

Question: What is the state of the art of communities' detection in social networks?


An Experimental Evaluation of Apple Siri and Google Speech Recognition. We perform an experimental evaluation of two popular cloud-based speech recognition systems. Cloudbased speech recognition systems enhances Web surfing, transportation, health care, etc. Using voice commands helps drivers stay connected to the Internet by avoiding traffic safety risks. The performance of these type of applications should be robust under difficult network conditions. User frustration with network traffic problems can affect the usability of these applications. We evaluate the performance of two popular cloud-based speech recognition applications, Apple Siri and Google Speech Recognition (GSR) under various network conditions. We evaluate transcription delay and accuracy of transcription of each application under different packet loss and jitter values. Results of our study show that performance of cloud-based speech recognition systems can be affected by jitter and packet loss; which are commonly occurring over WiFi and cellular network connections. keywords: Cloud Speech Recognition, Quality of Experience, Software Measurement, Streaming Media, Real-time Systems.

Question: What is the difference between cloud-based speech recognition and cloud-based speech recognition?


Self-Oscillating Fluxgate-Based Quasi-Digital Sensor for DC High-Current Measurement. A quasi-digital dc current sensor with rated current of ±600 A while overload current of about ±750 A is proposed in this paper. The new sensor is based on the open-loop self-oscillating fluxgate technology, but its originality is using a microcontroller unit to detect the duty cycle of the exciting voltage of the fluxgate. Compared with the published similar method, the whole signal chain of the new sensor is quasi-digital and without low-pass filter and analog-to-digital converter required when connected to digital systems. A precisely theoretical equation with respect to the linear dependence between the duty cycle and the current to be measured is established. Based on the equation, factors affecting the sensor sensitivity, accuracy, and resolution are determined, which constitutes the theoretical basis on the optimization design of the new sensor. The sensor linearity is improved using the least-squares polynomial fitting method. Some key specifications including the linearity, repeatability, and power supply effect of the sensor are characterized. The measurement results show that the linearity of the new sensor with the theoretical equation is better than 1.7% in the full scale of ±600 A, and can be improved to 0.3% when the fifth-order polynomial fitting method is used.

Question: What is the difference between a quasi-digital and a digital sensor?


Development of Computer-Aided Diagnostic (CADx) System for Distinguishing Neoplastic from Nonneoplastic Lesions in CT Colonography (CTC): Toward CTC beyond Detection. Half of the polyps surgically removed during conventional colonoscopy are benign with no malignant potential. Our purpose was to develop a CADx system for distinction between neoplastic and non-neoplastic lesions in CTC to reduce "unnecessary" colonoscopic polypectomy. Although computer aided detection (CADe) systems have been developed, less attention was given to the development of CADx systems. Our CADx system consists of shape-index-based coarse segmentation of lesions, 3D volume growing and sub-voxel refinement for fine segmentation of lesions, morphologic and texture feature analysis, Wilks' lambda-based stepwise feature selection, linear discriminant analysis for providing an integrated imaging biomarker for diagnosis of neoplastic lesions. Our database contained biopsy-confirmed 54 neoplastic lesions in 29 patients and 14 non-neoplastic lesions in 10 patients. Our CADx system integrating the selected features was able to determine an accurate likelihood of being a neoplasm and distinguish 87% (47/54) neoplastic lesions from 57% (8/14) non-neoplastic lesions correctly only using computed tomography (CT) images, achieving an area under the receiver operating characteristic curve (AUC) of 0.82. This study showed the potential of the use of CTC as a diagnostic tool beyond already accepted detection, thus, CTC with CADx would be potentially useful for reducing "unnecessary" polypectomy.

Question: What is the purpose of the CADx system?


A 2.6GHz band 537W peak power GaN HEMT asymmetric Doherty amplifier with 48% drain efficiency at 7dB. A 537W saturation output power (Psat) asymmetric Doherty amplifier for 2.6GHz band was successfully developed. The main and peak amplifiers were implemented with Psat of 210W and 320W GaN HEMTs. The newly developed 320W GaN HEMT consists of a single GaN die, both input and output partial match networks and a compact package. Its output matching network was tuned to inverse class F and a single-ended 320W GaN HEMT achieved higher than 61.8% drain efficiency from 2.4GHz to 2.7GHz. The 210W and 320W GaN HEMTs asymmetric Doherty amplifier exhibited 57.3dBm (537W) Psat and 48% drain efficiency with −50.6dBc ACLR at 50.3dBm (107W) average output power using a 4-carrier W-CDMA signal and commercially available digital pre-distortion system. These excellent performances show the good suitability for 2.6GHz band basestations.

Question: What is the peak power of a GaN HEMT?


Temporal Convolutional Neural Networks for Diagnosis from Lab Tests. Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both an imputed version of the data and a binary observation matrix. For imputing the temporal sparse observations, we develop a flexible, fast to train method for differentiable multivariate kernel regression. Our experiments on data from 298K individuals over 8 years, 18 common lab measurements, and 171 diseases show that the temporal signatures learned via convolution are significantly more predictive than baselines commonly used for early disease diagnosis.

Question: What is a temporal convolutional neural network?


SLAM-based automatic extrinsic calibration of a multi-camera rig. Cameras are often a good choice as the primary outward-looking sensor for mobile robots, and a wide field of view is usually desirable for responsive and accurate navigation, SLAMand relocalisation. While this can potentially be provided by a single omnidirectional camera, it can also be flexibly achieved by multiple cameras with standard optics mounted around the robot. However, such setups are difficult to calibrate. Here we present a general method for fully automatic extrinsic auto-calibration of a fixed multi camera rig, with no requirement for calibration patterns or other infrastructure, which works even in the case where the cameras have completely non-overlapping views. The robot is placed in a natural environment and makes a set of programmed movements including a full horizontal rotation and captures a synchronized image sequence from each camera. These sequences are processed individually with a monocular visual SLAM algorithm. The resulting maps are matched and fused robustly based on corresponding invariant features, and then all estimates are optimised full joint bundle adjustment, where we constrain the relative poses of the cameras to be fixed. We present results showing accurate performance of the method for various two and four camera configurations.

Question: How to calibrate a multi-camera rig?


Cloud Automation: Precomputing Roadmaps for Flexible Manipulation. The goal of this article is to highlight the benefits of cloud automation for industrial adopters and some of the research challenges that must be addressed in this process. The focus is on the use of cloud computing for efficiently planning the motion of new robot manipulators designed for flexible manufacturing floors. In particular, different ways that a robot can interact with a computing cloud are considered, where an architecture that splits computation between the remote cloud and the robot appears advantageous. Given this synergistic robot-cloud architecture, this article describes how solutions from the recent literature can be employed on the cloud during a periodically updated preprocessing phase to efficiently answer manipulation queries on the robot given changes in the workspace. In this setup, interesting tradeoffs arise between path quality and computational efficiency, which are evaluated through simulation. These tradeoffs motivate further research on how motion planning should be executed given access to a computing cloud.

Question: What is the preprocessing phase?


Mobile application for Indonesian medicinal plants identification using Fuzzy Local Binary Pattern and Fuzzy Color Histogram. This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.

Question: What is the name of the application?


The Optimality of Naive Bayes. Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, is rarely true in realworld applications. An open question is: what is the true reason for the surprisingly good performance of naive Bayes in classification? In this paper, we propose a novel explanation on the superb classification performance of naive Bayes. We show that, essentially, the dependence distribution; i.e., how the local dependence of a node distributes in each class, evenly or unevenly, and how the local dependencies of all nodes work together, consistently (supporting a certain classification) or inconsistently (canceling each other out), plays a crucial role. Therefore, no matter how strong the dependences among attributes are, naive Bayes can still be optimal if the dependences distribute evenly in classes, or if the dependences cancel each other out. We propose and prove a sufficient and necessary conditions for the optimality of naive Bayes. Further, we investigate the optimality of naive Bayes under the Gaussian distribution. We present and prove a sufficient condition for the optimality of naive Bayes, in which the dependence between attributes do exist. This provides evidence that dependence among attributes may cancel out each other. In addition, we explore when naive Bayes works well. Naive Bayes and Augmented Naive Bayes Classification is a fundamental issue in machine learning and data mining. In classification, the goal of a learning algorithm is to construct a classifier given a set of training examples with class labels. Typically, an example E is represented by a tuple of attribute values (x1, x2, , · · · , xn), where xi is the value of attribute Xi. Let C represent the classification variable, and let c be the value of C. In this paper, we assume that th

Question: What is the difference between naive Bayes and augmented naive Bayes?


Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images. In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.

Question: What is the difference between the two methods?


Monte-Carlo Tree Search in Settlers of Catan. Games are considered important benchmark tasks of artificial intelligence research. Modern strategic board games can typically be played by three or more people, which makes them suitable test beds for investigating multi-player strategic decision making. Monte-Carlo Tree Search (MCTS) is a recently published family of algorithms that achieved successful results with classical, two-player, perfect-information games such as Go. In this paper we apply MCTS to the multi-player, non-deterministic board game Settlers of Catan. We implemented an agent that is able to play against computer-controlled and human players. We show that MCTS can be adapted successfully to multi-agent environments, and present two approaches of providing the agent with a limited amount of domain knowledge. Our results show that the agent has considerable playing strength when compared to existing heuristics for the game. We conclude that MCTS is suitable for implementing a strong Settlers of Catan player.

Question: What is a family of algorithms?


Food-related illness and death in the United States.. Letters Letters Letters Letters Letters correctly identified E. faecium and E. faecalis to the species level, most (4 of 5) did not correctly identify E. gallinarum (three misidentified it as E. casseliflavus and one as E. faecalis). The results of this study are consistent with those of previous studies in the United States (4,5), South America (6), Spain (7), and Mexico (8). Although in countries like Chile, disk diffusion is practical and reliable for most susceptibility testing, detecting low-level vanco-mycin resistance in enterocci is difficult without supplementary testing. In Chile, as in other countries, strategies should be implemented to improve detection of these strains, including improvement of phenotypical and genotypical methods for VRE detection and species identification. Documentation of proficiency in detecting VRE is important for improving laboratory performance, detecting clinical isolates, and accurate and reliable reporting to local, national, and international surveillance systems. A, Febre N, et al. Epidemiologic analysis for acquisition of vancomycin-resistant enterococcus (VRE) in an intensive care unit in Brazil. Altschuler M, et al. Study to determine the ability of clinical laboratories to detect antimicrobial-resistant To the Editor: Dr. Mead and colleagues should be commended for attempting to estimate the prevalence of foodborne disease in the United States (1). Their study provides more complete estimates than previous studies in terms of the number of foodborne pathogens included; for example, it includes the first realistic estimate of the number of cases of disease due to Norwalk-like caliciviruses. However, the publication of these estimates raises some important issues. Even though " accurate estimates of disease burden are the foundation of sound public health policy " (2), most of these estimates (in particular, the assumption that unknown agents are transmitted by food

Question: What is the first realistic estimate of the number of cases of disease due to Norwalk-like caliciviruses?


An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this an introduction to support vector machines and other kernel based learning methods, how can you bargain with the thing that has many benefits for you?

Question: What is the best book to read?


Embedded image coding using zerotrees of wavelet coefficients. The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. The embedded code represents a sequence of binary decisions that distinguish a n image from the "null" image. Using a n embedded coding algorithm, a n encoder can terminate the encoding a t any point thereby allowing a target rate or target distortion metric to be met exactly. Also, given a bit stream, the decoder can cease decoding a t any point in the bit stream and still produce exactly the same image that would have been encoded a t the bit rate corresponding to the truncated bit stream. In addition to producing a fully embedded bit stream, EZW consistently produces compression results that a re competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: 1) a discrete wavelet transform or hierarchical subband decomposition, 2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, 3) entropy-coded successive-approximation quantization, and 4) universal lossless data compression which is achieved via adaptive arithmetic coding.

Question: What is the embedded zerotree wavelet algorithm?


Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector. Available online 18 May 2011

Question: What is BPR?


Mobile applications for weight management: theory-based content analysis.. BACKGROUND The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized.   PURPOSE The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change.   METHODS A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0-100) and the other on the Fogg Behavioral Model (score range: 0-6). Data collection and analysis occurred in November 2012.   RESULTS Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc.   CONCLUSIONS All apps received low overall scores for inclusion of behavioral theory-based strategies.

Question: What is the best weight loss app?


Multiobjective Optimization of Ultraflat Magnetic Components With PCB-Integrated Core. In future applications, e.g., in ultra-flat OLED lamp drivers or flat screen power supplies, ultra-flat ac/dc and dc/dc converter systems are highly demanded. Therefore, the design and implementation of a printed circuit board (PCB)-integrated flyback transformer for a 1-mm-thin single-phase power factor correction rectifier is under investigation. In this paper, first an overview on several integration methods is given. It is shown that the PCB integration of magnetic cores allows us to achieve the required thickness of 1 mm and a high energy density. In a next step, the design and the realization of ultra-flat magnetic components with PCB-integrated cores are discussed in detail. The presented multi-objective design procedure determines the inductor and/or transformer setup optimal with respect to minimal losses and/or minimal footprint area; for this purpose, all required electrical, magnetic, and geometrical parameters of the magnetic component are considered in the design process. Furthermore, all specific implications entailed by the PCB-integrated core, e.g., the core setup, anisotropic core losses, the interleaving of windings, or an accurate reluctance model are treated. Finally, experimental results are used to verify the design procedure.

Question: What is the design procedure of a printed circuit board integrated magnetic component?


Issues in Implementing IT Governance in Small and Medium Enterprises. Nowadays, almost all the enterprises in the world fall in Small Medium Enterprises (SMEs) category even though SMEs has different definitions in different countries. At the same time, there is no exemplar standard and /or framework for information technology (IT) governance for SMEs. This paper presents the main issues in implementing IT governance in SMEs. Firstly, it explains the definition of SMEs and their characteristics, secondly it discusses IT governance definition and its framework. Finally, some issues and approaches for ITG implementation in SMEs are described, and end with conclusion and future work.

Question: What is the definition of SMEs?


End-to-End Learning of Task-Oriented Dialogs. In this thesis proposal, we address the limitations of conventional pipeline design of taskoriented dialog systems and propose end-toend learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learningfrom-simulation approaches to improve the online interactive learning efficiency.

Question: What is the difference between a dialog system and a chatbot?


An analogue approach to the travelling salesman problem using an elastic net method. The travelling salesman problem1 is a classical problem in the field of combinatorial optimization, concerned with efficient methods for maximizing or minimizing a function of many independent variables. Given the positions of N cities, which in the simplest case lie in the plane, what is the shortest closed tour in which each city can be visited once? We describe how a parallel analogue algorithm, derived from a formal model2–3 for the establishment of topographically ordered projections in the brain4–10, can be applied to the travelling salesman problem1,11,12. Using an iterative procedure, a circular closed path is gradually elongated non-uniformly until it eventually passes sufficiently near to all the cities to define a tour. This produces shorter tour lengths than another recent parallel analogue algorithm13, scales well with the size of the problem, and is naturally extendable to a large class of optimization problems involving topographic mappings between geometrical structures14.

Question: What is the shortest closed tour in the plane?


Imbalance in the cloud: An analysis on Alibaba cluster trace. To improve resource efficiency and design intelligent scheduler for clouds, it is necessary to understand the workload characteristics and machine utilization in large-scale cloud data centers. In this paper, we perform a deep analysis on a newly released trace dataset by Alibaba in September 2017, consists of detail statistics of 11089 online service jobs and 12951 batch jobs co-locating on 1300 machines over 12 hours. To the best of our knowledge, this is one of the first work to analyze the Alibaba public trace. Our analysis reveals several important insights about different types of imbalance in the Alibaba cloud. Such imbalances exacerbate the complexity and challenge of cloud resource management, which might incur severe wastes of resources and low cluster utilization. 1) Spatial Imbalance: heterogeneous resource utilization across machines and workloads. 2) Temporal Imbalance: greatly time-varying resource usages per workload and machine. 3) Imbalanced proportion of multi-dimensional resources (CPU and memory) utilization per workload. 4) Imbalanced resource demands and runtime statistics (duration and task number) between online service and offline batch jobs. We argue accommodating such imbalances during resource allocation is critical to improve cluster efficiency, and will motivate the emergence of new resource managers and schedulers.

Question: What is the imbalance in the cloud?


Start Later, Sleep Later: School Start Times and Adolescent Sleep in Homeschool Versus Public/Private School Students.. Homeschooled students provide a naturalistic comparison group for later/flexible school start times. This study compared sleep patterns and sleep hygiene for homeschooled students and public/private school students (grades 6-12). Public/private school students (n = 245) and homeschooled students (n = 162) completed a survey about sleep patterns and sleep hygiene. Significant school group differences were found for weekday bedtime, wake time, and total sleep time, with homeschooled students waking later and obtaining more sleep. Homeschooled students had later school start times, waking at the same time that public/private school students were starting school. Public/private school students had poorer sleep hygiene practices, reporting more homework and use of technology in the hour before bed. Regardless of school type, technology in the bedroom was associated with shorter sleep duration. Later school start times may be a potential countermeasure for insufficient sleep in adolescents. Future studies should further examine the relationship between school start times and daytime outcomes, including academic performance, mood, and health.

Question: What is the difference between public and private school students?


Integration Trends in Monolithic Power ICs: Application and Technology Challenges. This paper highlights the general trend towards further monolithic integration in power applications by enabling power management and interfacing solutions in advanced CMOS nodes. The need to combine high-density digital circuits, power-management circuits, and robust interfaces in a single technology platform requires the development of additional process options on top of baseline CMOS. Examples include high-voltage devices, devices to enable area-efficient ESD protection, and integrated capacitors and inductors with high quality factors. The use of bipolar devices in these technologies for protection and control purposes in power applications is also addressed.

Question: What is the general trend towards further monolithic integration in power applications?


Multilevel Converters-A New Breed of Power Converters. AbstructMultilevel voltage source converters are emerging as a new breed of power converter options for high-power applications. The multilevel voltage source converters typically synthesize the staircase voltage wave from several levels of dc capacitor voltages. One of the major limitations of the multilevel converters is the voltage unbalance between different levels. The techniques to balance the voltage between different levels normally involve voltage clamping or capacitor charge control. There are several ways of implementing voltage balance in multilevel converters. Without considering the traditional magnetic coupled converters, this paper presents three recently developed multilevel voltage source converters: 1) diode-clamp, 2) flyingcapacitors, and 3) cascaded-inverters with separate dc sources. The operating principle, features, constraints, and potential applications of these converters will be discussed.

Question: What is a multilevel converter?


Application of electrostatic separation to the recycling of plastic wastes: separation of PVC, PET, and ABS.. Plastics are widely used in everyday life as a useful material, and thus their consumption is growing at a rate of about 5% per year in Korea. However, the constant generation of plastic wastes and their disposal generates environmental problems along with economic loss. In particular, mixed waste plastics are difficult to recycle because of their inferior characteristics. A laboratory-scale triboelectrostatic separator unit has been designed and assembled for this study. On the basis of the control of electrostatic charge, the separation of three kinds of mixed plastics, polyvinyl chloride (PVC), poly(ethylene terephthalate) (PET), and acrylonitrile-butadiene-styrene (ABS), in a range of similar gravities has been performed through a two-stage separation process. Polypropylene (PP) and high-impact polystyrene (HIPS) were found to be the most effective materials for a tribo-charger in the separation of PVC, PET, and ABS. The charge-to-mass ratio (nC/g) of plastics increased with increasing air velocity in the tribo charger. In the first stage, using the PP cyclone charger, the separation efficiency of particles considerably depended on the air velocity (10 m/s), the relative humidity (< 30%), the electrode potential (> 20 kV), and the splitter position (+2 cm from the center) in the triboelelctrostatic separator unit. At this time, a PVC grade of 99.40% and a recovery of 98.10% have successfully been achieved. In the second stage, using the HIPS cyclone charger, a PET grade of 97.80% and a recovery of 95.12% could be obtained under conditions of 10 m/s, over 25 kV, a central splitter position, and less than 40% relative humidity. In order to obtain 99.9% PVC grade and 99.3% PET grade, their recoveries should be sacrificed by 20.9% and 27%, respectively, with moving the splitter from the center to a (+)6 cm position.

Question: What is the difference between a triboelectrostatic separator and a cyclone separator?


The iCub humanoid robot: an open platform for research in embodied cognition. We report about the iCub, a humanoid robot for research in embodied cognition. At 104 cm tall, the iCub has the size of a three and half year old child. It will be able to crawl on all fours and sit up to manipulate objects. Its hands have been designed to support sophisticate manipulation skills. The iCub is distributed as Open Source following the GPL/FDL licenses. The entire design is available for download from the project homepage and repository (http://www.robotcub.org). In the following, we will concentrate on the description of the hardware and software systems. The scientific objectives of the project and its philosophical underpinning are described extensively elsewhere [1].

Question: What is the size of the robot?


3D-Printing Spatially Varying BRDFs. A new method fabricates custom surface reflectance and spatially varying bidirectional reflectance distribution functions (svBRDFs). Researchers optimize a microgeometry for a range of normal distribution functions and simulate the resulting surface's effective reflectance. Using the simulation's results, they reproduce an input svBRDF's appearance by distributing the microgeometry on the printed material's surface. This method lets people print svBRDFs on planar samples with current 3D printing technology, even with a limited set of printing materials. It extends naturally to printing svBRDFs on arbitrary shapes.

Question: How do you make a custom surface reflectance?


Variational Bayes with synthetic likelihood. Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the parameters, is available. The synthetic likelihood method derives an approximate likelihood function from a plug-in normal density estimate for the summary statistic, with plug-in mean and covariance matrix obtained by Monte Carlo simulation from the model. In this article, we develop alternatives to Markov chain Monte Carlo implementations of Bayesian synthetic likelihoods with reduced computational overheads. Our approach uses stochastic gradient variational inference methods for posterior approximation in the synthetic likelihood context, employing unbiased estimates of the log likelihood. We compare the new method with a related likelihood free variational inference technique in the literature, while at the same time improving the implementation of that approach in a number of ways. These new algorithms are feasible to implement in situations which are challenging for conventional approximate Bayesian computation (ABC) methods, in terms of the dimensionality of the parameter and summary statistic.

Question: Variational Bayes with synthetic likelihood


Application of spiking neural networks and the bees algorithm to control chart pattern recognition. Statistical process control (SPC) is a method for improving the quality o f products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types o f charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation o f artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application o f SNN techniques to control chart pattern recognition. It is designed to present an analysis o f the existing learning algorithms o f SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation o f neural networks. This thesis focuses on the architecture and the learning procedure o f the network. Four new learning algorithms are presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim o f the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architect

Question: What is the difference between spiking neural networks and artificial neural networks?


Introduction to Fillers.. BACKGROUND Over the last few years, injectable soft-tissue fillers have become an integral part of cosmetic therapy, with a wide array of products designed to fill lines and folds and revolumize the face.   METHODS This review describes cosmetic fillers currently approved by the Food and Drug Administration and discusses new agents under investigation for use in the United States.   RESULTS Because of product refinements over the last few years-greater ease of use and longevity, the flexibility of multiple formulations within one line of products, and the ability to reverse poor clinical outcomes-practitioners have gravitated toward the use of biodegradable agents that stimulate neocollagenesis for sustained aesthetic improvements lasting up to a year or more with minimal side effects. Permanent implants provide long-lasting results but are associated with greater potential risk of complications and require the skilled hand of the experienced injector.   CONCLUSIONS A variety of biodegradable and nonbiodegradable filling agents are available or under investigation in the United States. Choice of product depends on injector preference and the area to be filled. Although permanent agents offer significant clinical benefits, modern biodegradable fillers are durable and often reversible in the event of adverse effects.

Question: What are the advantages and disadvantages of fillers?


Three-way substrate integrated waveguide (SIW) power divider design. In this paper, an X-band 1×3 substrate integrated waveguide (SIW) power divider design is presented. The designed SIW power divider provides equal amplitude with uniform phase distribution at the each output port. It has also a satisfactory operating bandwidth and low insertion loss. Moreover, the return loss is approximately 25 dB at the design frequency as shown in the EM simulation results.

Question: What is a substrate integrated waveguide?


Does Music Predictability Improve Spatial-Temporal Reasoning ?. In 1993, it was found that Mozart's music temporarily enhanced performance on spatialtemporal reasoning tasks. This effect later became termed the Mozart Effect. Since then, the mechanisms of the Mozart Effect have been hotly debated among the scientific community. Recent research has called for studying the Mozart Effect by analyzing music as a series of components. The present study took the components of music into account by testing if predictable music could enhance spatial-temporal reasoning compared to non-predictable music. Participants were administered a task designed to test spatial-temporal reasoning after listening to either predictable or non-predictable music. Additionally, as a control condition, they performed the same task after listening to a short story. Listening to music did not affect reasoning performance, regardless of the predictability of the music. The results indicate that predictability in music alone may not be a major element of the Mozart Effect.

Question: Does music predictability improve spatial-temporal reasoning?


Joint tracking and segmentation of multiple targets. Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios [e.g. 40, 53, 55]. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these "dots" over time. An obvious short-coming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain a video segmentation in addition to the classical bounding-box representation in unconstrained, real-world videos. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.

Question: How to track multiple targets in unconstrained scenarios?


Corpus-Guided Sentence Generation of Natural Images. We propose a sentence generation strategy that describes images by predicting the most likely nouns, verbs, scenes and prepositions that make up the core sentence structure. The input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. As predicting actions from still images directly is unreliable, we use a language model trained from the English Gigaword corpus to obtain their estimates; together with probabilities of co-located nouns, scenes and prepositions. We use these estimates as parameters on a HMM that models the sentence generation process, with hidden nodes as sentence components and image detections as the emissions. Experimental results show that our strategy of combining vision and language produces readable and descriptive sentences compared to naive strategies that use vision alone.

Question: How do you generate sentences from images?


CORE: Context-Aware Open Relation Extraction with Factorization Machines. We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating contextual information—such as metadata about extraction sources, lexical context, or type information—significantly improves prediction performance. Open information extractors, for example, may produce extractions that are unspecific or ambiguous when taken out of context. Our experimental study on a large real-world dataset indicates that CORE has significantly better prediction performance than state-ofthe-art approaches when contextual information is available.

Question: What is a matrix factorization model?


Collective entity linking in web text: a graph-based method. Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy.  This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.

Question: What is the collective entity linking in web text?


Automatic Classification for Vietnamese News. This paper proposes an automatic framework to classify Vietnamese news from news sites on the Internet. In this proposed framework, the extracted main content of Vietnamese news is performed automatically by applying the improved performance extraction method from [1]. This information will be classified by using two machine learning methods: Support vector machine and naïve bayesian method. Our experiments implemented with Vietnamese news extracted from some sites showed that the proposed classification framework give acceptable results with a rather high accuracy, leading to applying it to real information systems.

Question: What is the proposed framework?


Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review.. BACKGROUND Systematic literature reviews identify, select, appraise, and synthesize relevant literature on a particular topic. Typically, these reviews examine primary studies based on similar methods, e.g., experimental trials. In contrast, interest in a new form of review, known as mixed studies review (MSR), which includes qualitative, quantitative, and mixed methods studies, is growing. In MSRs, reviewers appraise studies that use different methods allowing them to obtain in-depth answers to complex research questions. However, appraising the quality of studies with different methods remains challenging. To facilitate systematic MSRs, a pilot Mixed Methods Appraisal Tool (MMAT) has been developed at McGill University (a checklist and a tutorial), which can be used to concurrently appraise the methodological quality of qualitative, quantitative, and mixed methods studies.   OBJECTIVES The purpose of the present study is to test the reliability and efficiency of a pilot version of the MMAT.   METHODS The Center for Participatory Research at McGill conducted a systematic MSR on the benefits of Participatory Research (PR). Thirty-two PR evaluation studies were appraised by two independent reviewers using the pilot MMAT. Among these, 11 (34%) involved nurses as researchers or research partners. Appraisal time was measured to assess efficiency. Inter-rater reliability was assessed by calculating a kappa statistic based on dichotomized responses for each criterion. An appraisal score was determined for each study, which allowed the calculation of an overall intra-class correlation.   RESULTS On average, it took 14 min to appraise a study (excluding the initial reading of articles). Agreement between reviewers was moderate to perfect with regards to MMAT criteria, and substantial with respect to the overall quality score of appraised studies.   C

Question: What is the reliability of the pilot MMAT?


Characterizing Data Structures for Volatile Forensics. Volatile memory forensic tools can extract valuable evidence from latent data structures present in memory dumps. However, current techniques are generally limited by a lack of understanding of the underlying data without the use of expert knowledge. In this paper, we characterize the nature of such evidence by using deep analysis techniques to better understand the life-cycle and recoverability of latent program data in memory. We have developed Cafegrind, a tool that can systematically build an object map and track the use of data structures as a program is running. Statistics collected by our tool can show which data structures are the most numerous, which structures are the most frequently accessed and provide summary statistics to guide forensic analysts in the evidence gathering process. As programs grow increasingly complex and numerous, the ability to pinpoint specific evidence in memory dumps will be increasingly helpful. Cafegrind has been tested on a number of real-world applications and we have shown that it can successfully map up to 96% of heap accesses.

Question: What is the life-cycle of a data structure?


A Survey on Data Mining Algorithm for Market Basket Analysis. s Association rule mining identifies the remarkable association or relationship between a large set of data items. With huge quantity of data constantly being obtained and stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis. Keyewords : Association Rule Mining, Apriori Algorithm, Market Basket Analysis GJCST Classification : H.2.8 A Survey on Data Mining Algorithm for Market Basket Analysis Strictly as per the compliance and regulations of: Global Journal of Computer Science and Technology Volume 11 Issue 11 Version 1.0 July 2011 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350

Question: What is the difference between association rule mining and market basket analysis?


Using CMMI together with agile software development: A systematic review. http://dx.doi.org/10.1016/j.infsof.2014.09.012 0950-5849/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: Center of Informatics, Federal University of Pernambuco (CIn/UFPE), Av. Jornalista Anibal Fernandes, S/N, Cidade Universitária, 50. Recife, PE, Brazil. Tel.: +55 81 8250 0470. E-mail addresses: fss4@cin.ufpe.br (F. Selleri Silva), fsfs@cin.ufpe.br (F.S.F. Soares), alp5@cin.ufpe.br (A.L. Peres), ima2@cin.ufpe.br (I.M.d. Azevedo), aplfv@ci (A.P.L.F. Vasconcelos), fkk@cin.ufpe.br (F.K. Kamei), srlm@cin.ufpe.br (S.R.d.L. Meira). Fernando Selleri Silva a,b,⇑, Felipe Santana Furtado Soares , Angela Lima Peres , Ivanildo Monteiro de Azevedo , Ana Paula L.F. Vasconcelos , Fernando Kenji Kamei , Silvio Romero de Lemos Meira a,c

Question: What is the corresponding author at?


Classification ability of single hidden layer feedforward neural networks. Multilayer perceptrons with hard-limiting (signum) activation functions can form complex decision regions. It is well known that a three-layer perceptron (two hidden layers) can form arbitrary disjoint decision regions and a two-layer perceptron (one hidden layer) can form single convex decision regions. This paper further proves that single hidden layer feedforward neural networks (SLFN's) with any continuous bounded nonconstant activation function or any arbitrary bounded (continuous or not continuous) activation function which has unequal limits at infinities (not just perceptrons) can form disjoint decision regions with arbitrary shapes in multidimensional cases. SLFN's with some unbounded activation function can also form disjoint decision regions with arbitrary shapes.

Question: What is a single hidden layer feedforward neural network?


Intrusion Detection Using Data Mining Techniques Intrusion Detection Using Data Mining Techniques. In these days an increasing number of public and commercial services are used through the Internet, so that security of information becomes more important issue in the society information Intrusion Detection System (IDS) used against attacks for protected to the Computer networks. On another way, some data mining techniques also contribute to intrusion detection. Some data mining techniques used for intrusion detection can be classified into two classes: misuse intrusion detection and anomaly intrusion detection. Misuse always refers to known attacks and harmful activities that exploit the known sensitivity of the system. Anomaly generally means a generally activity that is able to indicate an intrusion. In this paper, comparison made between 23 related papers of using data mining techniques for intrusion detection. Our work provide an overview on data mining and soft computing techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS), etc. In this paper comparison shown between IDS data mining techniques and tuples used for intrusion detection. In those 23 related papers, 7 research papers use ANN and 4 ones use SVM, because of ANN and SVM are more reliable than other models and structures. In addition, 8 researches use the DARPA1998 tuples and 13 researches use the KDDCup1999, because the standard tuples are much more credible than others. There is no best intrusion detection model in present time. However, future research directions for intrusion detection should be explored in this paper. Keywords— intrusion detection, data mining, ANN

Question: What is the difference between misuse and anomaly intrusion detection?


Examining the critical success factors in the adoption of enterprise resource planning. This paper presents a literature review of the critical success factors (CSFs) in the implementation of enterprise resource planning (ERP) across 10 different countries/regions. The review covers journals, conference proceedings, doctoral dissertation, and textbooks from these 10 different countries/regions. Through a review of the literature, 18 CSFs were identified, with more than 80 sub-factors, for the successful implementation of ERP. The findings of our study reveal that 'appropriate business and IT legacy systems', 'business plan/vision/goals/justification', 'business process reengineering', 'change management culture and programme', 'communication', 'ERP teamwork and composition', 'monitoring and evaluation of performance', 'project champion', 'project management', 'software/system development, testing and troubleshooting', 'top management support', 'data management', 'ERP strategy and implementation methodology', 'ERP vendor', 'organizational characteristics', 'fit between ERP and business/process', 'national culture' and 'country-related functional requirement' were the commonly extracted factors across these 10 countries/regions. In these 18 CSFs, 'top management support' and 'training and education' were the most frequently cited as the critical factors to the successful implementation of ERP systems. # 2008 Elsevier B.V. All rights reserved.

Question: What are the critical success factors in the adoption of enterprise resource planning?


Estimation of induction motor double-cage model parameters from manufacturer data. This paper presents a numerical method for the estimation of induction motor double-cage model parameters from standard manufacturer data: full load mechanical power (rated power), full load reactive electrical power, breakdown torque, and starting current and torque. A model sensitivity analysis for the various electrical parameters shows that stator resistance is the least significant parameter. The nonlinear equations to be solved for the parameters determination are formulated as a minimization problem with restrictions. The method has been tested with 223 motors from different manufacturers, with an average value of the normalized residual error of 1.39/spl middot/10/sup -2/. The estimated parameters of these motors are graphically represented as a function of the rated power.

Question: How to estimate induction motor parameters from manufacturer data?


Event-Based Mobile Social Networks: Services, Technologies, and Applications. Event-based mobile social networks (MSNs) are a special type of MSN that has an immanently temporal common feature, which allows any smart phone user to create events to share group messaging, locations, photos, and insights among participants. The emergence of Internet of Things and event-based social applications integrated with context-awareness ability can be helpful in planning and organizing social events like meetings, conferences, and tradeshows. This paper first provides review of the event-based social networks and the basic principles and architecture of event-based MSNs. Next, event-based MSNs with smartphone contained technology elements, such as context-aware mobility and multimedia sharing, are presented. By combining the feature of context-aware mobility with multimedia sharing in event-based MSNs, event organizers, and planners with the service providers optimize their capability to recognize value for the multimedia services they deliver. The unique features of the current event-based MSNs give rise to the major technology trends to watch for designing applications. These mobile applications and their main features are described. At the end, discussions on the evaluation of the event-based mobile applications based on their main features are presented. Some open research issues and challenges in this important area of research are also outlined.

Question: What is the difference between event-based mobile social networks and event-based social networks?


10 user interface elements for mobile learning application development. Mobile learning application is a new fashionable trend. More than a thousand learning applications have been designed and developed. Therefore, the focus on mobile learning applications needs to be refined and improved in order to facilitate users' interaction and response towards the learning process. To develop an effective mobile learning application, one should start from the User Interface (UI) because an effective UI can play a role in constantly making the user focus on an object and subject. The purpose of this study is to investigate and identify the best UI elements to use in mobile learning application development. Four existing UI guidelines and 12 selected learning applications were analysed, and we identified 10 UI Elements in order to develop the next mobile learning applications. All the 10 elements are described accordingly and they have implications for those designing and implementing UI in mobile learning applications.

Question: What are the 10 user interface elements for mobile learning application development?


A motivational theory of life-span development.. This article had four goals. First, the authors identified a set of general challenges and questions that a life-span theory of development should address. Second, they presented a comprehensive account of their Motivational Theory of Life-Span Development. They integrated the model of optimization in primary and secondary control and the action-phase model of developmental regulation with their original life-span theory of control to present a comprehensive theory of development. Third, they reviewed the relevant empirical literature testing key propositions of the Motivational Theory of Life-Span Development. Finally, because the conceptual reach of their theory goes far beyond the current empirical base, they pointed out areas that deserve further and more focused empirical inquiry.

Question: What is the motivation theory of life-span development?


Runtime Verification of Ethereum Smart Contracts. The notion of smart contracts in distributed ledger systems have been hailed as a safe way of enforcing contracts between participating parties. However, unlike legal contracts, which talk about ideal behaviour and consequences of not adhering to such behaviour, smart contracts are by their very nature executable code, giving explicit instructions on how to achieve compliance. Executable specification languages, particularly Turing complete ones, are notoriously known for the difficulty of ensuring correctness, and recent incidents which led to huge financial losses due to bugs in smart contracts, have highlighted this issue. In this paper we show how standard techniques from runtime verification can be used in the domain of smart contracts, including a novel stake-based instrumentation technique which ensures that the violating party provides insurance for correct behaviour. The techniques we describe have been partially implemented in a proof-of-concept tool ContractLarva, which we discuss in this paper.

Question: What is a smart contract?


Semantic Match Consistency for Long-Term Visual Localization. Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots. Traditional featurebased methods often struggle in these conditions due to the significant number of erroneous matches between the image and the 3D model. In this paper, we present a method for scoring the individual correspondences by exploiting semantic information about the query image and the scene. In this way, erroneous correspondences tend to get a low semantic consistency score, whereas correct correspondences tend to get a high score. By incorporating this information in a standard localization pipeline, we show that the localization performance can be significantly improved compared to the state-of-the-art, as evaluated on two challenging long-term localization benchmarks.

Question: How to improve the localization performance?


Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design. Residential and commercial buildings are responsible for about 40% of primary energy consumption in the US. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model to replace expensive energy simulations during the optimization. We propose a method, suitable for expensive multiobjective optimization in very large search spaces. In particular, we use a Gaussian Process (GP) model for the prediction and devise an active learning scheme in a multi-objective genetic algorithm to preferentially simulate only solutions that are very informative to the model's predictions for the current generation. We develop a comprehensive and publicly available benchmark for building design optimization. We show that the GP model is highly competitive as a surrogate for building energy simulations, in addition to being well-suited for the active learning setting. Our results show that our approach clearly outperforms surrogate-based optimization, and produces solutions close in hypervolume to simulation optimization, while using only a fraction of the simulations and time.

Question: What is the energy consumption of a building?


Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates. Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying participants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.

Question: How to crowd source traffic updates?


Neurologic foundations of spinal cord fusion (GEMINI).. Cephalosomatic anastomosis has been carried out in both monkeys and mice with preservation of brain function. Nonetheless the spinal cord was not reconstructed, leaving the animals unable to move voluntarily. Here we review the details of the GEMINI spinal cord fusion protocol, which aims at restoring electrophysiologic conduction across an acutely transected spinal cord. The existence of the cortico-truncoreticulo-propriospinal pathway, a little-known anatomic entity, is described, and its importance concerning spinal cord fusion emphasized. The use of fusogens and electrical stimulation as adjuvants for nerve fusion is addressed. The possibility of achieving cephalosomatic anastomosis in humans has become reality in principle.

Question: What is the importance of the cortico-truncoreticulo-propriospinal pathway?


A passivity based Cartesian impedance controller for flexible joint robots - part I: torque feedback and gravity compensation. In this paper a novel approach to the Cartesian impedance control problem for robots with flexible joints is presented. The proposed controller structure is based on simple physical considerations, which are motivating the extension of classical position feedback by an additional feedback of the joint torques. The torque feedback action can be interpreted as a scaling of the apparent motor inertia. Furthermore the problem of gravity compensation is addressed. Finally, it is shown that the closed loop system can be seen as a feedback interconnection of passive systems. Based on this passivity property a proof of asymptotic stability is presented.

Question: What is a passivity based Cartesian impedance controller for flexible joint robots?


Visuo-acoustic stimulation that helps you to relax: A virtual reality setup for patients in the intensive care unit. After prolonged stay in an intensive care unit (ICU) patients often complain about cognitive impairments that affect health-related quality of life after discharge. The aim of this proof-of-concept study was to test the feasibility and effects of controlled visual and acoustic stimulation in a virtual reality (VR) setup in the ICU. The VR setup consisted of a head-mounted display in combination with an eye tracker and sensors to assess vital signs. The stimulation consisted of videos featuring natural scenes and was tested in 37 healthy participants in the ICU. The VR stimulation led to a reduction of heart rate (p = 0. 049) and blood pressure (p = 0.044). Fixation/saccade ratio (p < 0.001) was increased when a visual target was presented superimposed on the videos (reduced search activity), reflecting enhanced visual processing. Overall, the VR stimulation had a relaxing effect as shown in vital markers of physical stress and participants explored less when attending the target. Our study indicates that VR stimulation in ICU settings is feasible and beneficial for critically ill patients.

Question: How does a virtual reality setup help patients in the intensive care unit?


Area-efficient high-speed hybrid 1-bit full adder circuit using modified XNOR gate. A hybrid 1-bit full adder design is presented here using modified 3T-XNOR gate to improve the area and speed performance. The design is implemented for 1-bit full adder and then is scaled to 32-bit adder. Combination of CMOS and transmission gate logic is used to enhance the performance in terms of area, delay and power. The performance of the proposed design is evaluated through the simulation analysis in 90-nm technology with 1.2v supply voltage. The effect of scaling on the overall performance is also analyzed through the performance evaluation of 1-bit and 32-bit adder. The performance of proposed design is also compared with conventional design to verify the effectiveness in terms of area, power, delay.

Question: What is the area-efficient high-speed hybrid 1-bit full adder circuit using modified XNOR gate?


On the journey to continuous deployment: Technical and social challenges along the way. Context: Continuous deployment (CD) is an emerging software development process with organisations such as Facebook, Microsoft, and IBM successfully implementing and using the process. The CD process aims to immediately deploy software to customers as soon as new code is developed, and can result in a number of benefits for organizations, such as: new business opportunities, reduced risk for each release, and prevent development of wasted software. There is little academic literature on the challenges organisations face when adopting the CD process, however there are many anecdotal challenges that organisations have voiced on their online blogs. Objective: The aim of this research is to examine the challenges faced by organisations when adopting CD as well as the strategies to mitigate these challenges. Method: An explorative case study technique that involves in-depth interviews with software practitioners in an organisation that has adopted CD was conducted to identify these challenges. Results: This study found a total of 20 technical and social adoption challenges that organisations may face when adopting the CD process. The results are discussed to gain a deeper understanding of the strategies employed by organisations to mitigate the impacts of these challenges. Conclusion: While a number of individual technical and social adoption challenges were uncovered by the case study in this research, most challenges were not faced in isolation. The severity of these challenges were reduced by a number of mitigation strategies employed by the case study organization. It is concluded that organisations need to be well prepared to handle technical and social adoption challenges with their existing expertise, processes and tools before adopting the CD

Question: What are the challenges faced by organisations when adopting continuous deployment?


Image super-resolution using gradient profile prior. In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.

Question: How to super-resolve an image?


Entity Resolution: Theory, Practice & Open Challenges. This tutorial brings together perspectives on ER from a variety of fields, including databases, machine learning, natural language processing and information retrieval, to provide, in one setting, a survey of a large body of work. We discuss both the practical aspects and theoretical underpinnings of ER. We describe existing solutions, current challenges, and open research problems.

Question: Entity Resolution: Theory, Practice & Open Challenges


Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonsmooth part is convex. Surprisingly, unlike the smooth case, our knowledge of this fundamental problem is very limited. For example, it is not known whether the proximal stochastic gradient method with constant minibatch converges to a stationary point. To tackle this issue, we develop fast stochastic algorithms that provably converge to a stationary point for constant minibatches. Furthermore, using a variant of these algorithms, we obtain provably faster convergence than batch proximal gradient descent. Our results are based on the recent variance reduction techniques for convex optimization but with a novel analysis for handling nonconvex and nonsmooth functions. We also prove global linear convergence rate for an interesting subclass of nonsmooth nonconvex functions, which subsumes several recent works.

Question: What is the difference between a convex and a nonconvex optimization problem?


Frontal fibrosing alopecia – A Case report * Alopecia frontal fibrosante-Relato de caso. Frontal fibrosing alopecia is a kind of progressive and frequently irreversible cicatricial alopecia marked by a lichenoid infiltrate in histology. Since its first description, in 1994, in Australia, some cases have been documented all over the world. The article reports, for the second time in the medical literature, a Brazilian case and reviews the main aspects of this dermatosis.

Question: What is the main aspects of this dermatosis?


Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information not only about task-related activity, but also about the connectivity (functional or effective) among regions and the influences of behavioral or physiologic states on that connectivity. Similar analyses have been performed in other imaging modalities, such as positron emission tomography. However, fMRI is unique because the information about the underlying neuronal activity is filtered or convolved with a hemodynamic response function. Previous studies of regional connectivity in fMRI have overlooked this convolution and have assumed that the observed hemodynamic response approximates the neuronal response. In this article, this assumption is revisited using estimates of underlying neuronal activity. These estimates use a parametric empirical Bayes formulation for hemodynamic deconvolution.

Question: What is the importance of hemodynamic deconvolution?


Improving recommendation lists through topic diversification. In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.

Question: How to improve recommendation lists?


Intrusion-Tolerant Autonomous Driving. Fully autonomous driving is one if not the killer application for the upcoming decade of real-time systems. However, in the presence of increasingly sophisticated attacks by highly skilled and well equipped adversarial teams, autonomous driving must not only guarantee timeliness and hence safety. It must also consider the dependability of the software concerning these properties while the system is facing attacks. For distributed systems, fault-and-intrusion tolerance toolboxes already offer a few solutions to tolerate partial compromise of the system behind a majority of healthy components operating in consensus. In this paper, we present a concept of an intrusion-tolerant architecture for autonomous driving. In such a scenario, predictability and recovery challenges arise from the inclusion of increasingly more complex software on increasingly less predictable hardware. We highlight how an intrusion tolerant design can help solve these issues by allowing timeliness to emerge from a majority of complex components being fast enough, often enough while preserving safety under attack through pre-computed fail safes.

Question: What is an intrusion tolerant architecture?


Gradient-based learning applied to document recognition.

Question: What is gradient-based learning?


Plasmacytoid dendritic cells sense self-DNA coupled with antimicrobial peptide. Plasmacytoid dendritic cells (pDCs) sense viral and microbial DNA through endosomal Toll-like receptors to produce type 1 interferons. pDCs do not normally respond to self-DNA, but this restriction seems to break down in human autoimmune disease by an as yet poorly understood mechanism. Here we identify the antimicrobial peptide LL37 (also known as CAMP) as the key factor that mediates pDC activation in psoriasis, a common autoimmune disease of the skin. LL37 converts inert self-DNA into a potent trigger of interferon production by binding the DNA to form aggregated and condensed structures that are delivered to and retained within early endocytic compartments in pDCs to trigger Toll-like receptor 9. Thus, our data uncover a fundamental role of an endogenous antimicrobial peptide in breaking innate tolerance to self-DNA and suggest that this pathway may drive autoimmunity in psoriasis.

Question: What is the role of LL37 in psoriasis?


Compatibility Is Not Transparency: VMM Detection Myths and Realities. Recent work on applications ranging from realistic honeypots to stealthier rootkits has speculated about building transparent VMMs – VMMs that are indistinguishable from native hardware, even to a dedicated adversary. We survey anomalies between real and virtual hardware and consider methods for detecting such anomalies, as well as possible countermeasures. We conclude that building a transparent VMM is fundamentally infeasible, as well as impractical from a performance and engineering standpoint.

Question: What is a transparent VMM?


Learning From Positive and Unlabeled Data: A Survey. Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

Question: What is the state of the art in PU learning?


Scrum Metrics for Hyperproductive Teams: How They Fly like Fighter Aircraft. Scrum Teams use lightweight tools like Story Points, the Burn down chart, and Team Velocity. While essential, these tools alone provide insufficient information to maintain a high energy state that yields Hyper productivity. More data is required, but data collection itself can slow Teams. This effect must be avoided when productivity is the primary marker of success. Here we describe nine metrics that can develop and sustain Hyper productive Teams -- Velocity, Work Capacity, Focus Factor, Percentage of Adopted Work, Percentage of Found Work, Accuracy of Estimation, Accuracy of Forecast, Targeted Value Increase, Success at Scale, and the Win/Loss Record of the Team. The unique contribution of this paper is to demonstrate how a light touch and lightweight strategy can be used to compare Teams with different Story Point reference scales.

Question: How to measure velocity?


B2C web site quality and emotions during online shopping episodes: An empirical study. This paper explores the impact of the quality of a web site on the cognitive process leading to consumers' emotions—considered as direct antecedents to shopping behaviors and operationalized as mental states of readiness arising from the appraisal of events. A parsimonious theoretical model was defined and tested with data collected from 215 web-shopping episodes during which consumers were shopping for low-touch products. Analysis of the results showed that web site quality had a positive impact on the cognitive appraisal of situational state, which in turn influenced five of the six emotions of the proposed model: liking, joy, pride, dislike, and frustration. Results also showed that a substantial number of shoppers experienced intensively the emotions of liking and joy. Moreover, this paper highlights several implications that could help managers and webmasters improve the quality of their web sites. # 2006 Elsevier B.V. All rights reserved.

Question: What is the impact of the quality of a web site on the cognitive process leading to consumers' emotions?


Evaluation of nutritional assessment techniques in elderly people newly admitted to municipal care. Objectives: To evaluate the Subjective Global Assessment (SGA) and the Mini Nutritional Assessment (MNA) with regard to validity using a combination of anthropometric and serum-protein measurements as standard criteria to assess protein-energy malnutrition (PEM).Design: Cross-sectional study with consecutive selection of residents aged ≥65 y.Setting: A municipality in the south of Sweden.Subjects: During a year, starting in October 1996, 148 females and 113 males, aged ≥65–104 y of age, newly admitted to special types of housing for the elderly, were included in the study.Results: According to SGA, 53% were assessed as malnourished or moderately malnourished on admission. The corresponding figure from MNA was 79% malnourished or at risk of malnutrition. Both tools indicated that anthropometric values and serum proteins were significantly lower in residents classified as being malnourished (P<0.05). Sensitivity in detecting PEM was in SGA 0.93 and in MNA 0.96 and specificity was 0.61 and 0.26, respectively. Using regression analysis, weight index and serum albumin were the best objective nutritional parameters in predicting the SGA- and MNA classifications. Item 'muscle wasting' in SGA and 'self-experienced health status' in MNA showed most predictive power concerning the odds of being assessed as malnourished.Conclusions: SGA was shown to be the more useful tool in detecting residents with established malnutrition and MNA in detecting residents who need preventive nutritional measures.

Question: What is the best nutritional assessment tool for elderly people?