Factorial LDA: Sparse Multi-Dimensional Text Models. Latent variable models can be enriched with a multi-dimensional structure to consider the many latent factors in a text corpus, such as topic, author perspective and sentiment. We introduce factorial LDA, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. Our model incorporates structured word priors and learns a sparse product of factors. Experiments on research abstracts show that our model can learn latent factors such as research topic, scientific discipline, and focus (methods vs. applications). Our modeling improvements reduce test perplexity and improve human interpretability of the discovered factors.

Question: What is a multi-dimensional model?


Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns. The knowledge discovery process is supported by data files information gathered from collected data sets, which often contain errors in the form of missing values. Data imputation is the activity aimed at estimating values for missing data items. This study focuses on the development of automated data imputation models, based on artificial neural networks for monotone patterns of missing values. The present work proposes a single imputation approach relying on a multilayer perceptron whose training is conducted with different learning rules, and a multiple imputation approach based on the combination of multilayer perceptron and k-nearest neighbours. Eighteen real and simulated databases were exposed to a perturbation experiment with random generation of monotone missing data pattern. An empirical test ean/mode model ultilayer perceptron egression model was accomplished on these data sets, including both approaches (single and multiple imputations), and three classical single imputation procedures – mean/mode imputation, regression and hot-deck – were also considered. Therefore, the experiments involved five imputation methods. The results, considering different performance measures, demonstrated that, in comparison with traditional tools, both proposals improve the automation level and data quality offering a satisfactory performance.

Question: What is the difference between single imputation and multiple imputation?


NON-UNIFORM TRANSMISSION LINE TRANSFORMERS AND THEIR APPLICATION IN THE DESIGN OF COMPACT MULTI-BAND BAGLEY POWER DIVIDERS WITH HARMONICS SUPPRESSION. In this paper, the application of compact non-uniform transmission line transformers (NTLTs) in suppressing and controlling the odd harmonics of the fundamental frequency is presented. A design example showing the complete suppression of the odd harmonics of the fundamental frequency is given. In addition, several compact NTLTs are designed showing the possibility of controlling the existence of a fundamental frequency's odd harmonics. Moreover, multi-band operation using NTLTs is investigated. Specifically, a design example of a miniaturized triple-frequency NTLT is introduced. Based on these compact NTLTs, a 3-way triple-frequency modified Bagley power divider (BPD) with a size reduction of 50%, and a 5-way modified BPD with harmonics suppression and size reduction of 34%, are designed. For verification purposes, both dividers are simulated using the two full-wave simulators IE3D and HFSS. Moreover, the modified 5-way BPD with harmonics suppression is fabricated and measured. Both the simulation and measurement results validate the design approach.

Question: What is a triple-frequency modified Bagley power divider?


Practical byzantine fault tolerance and proactive recovery. Our growing reliance on online services accessible on the Internet demands highly available systems that provide correct service without interruptions. Software bugs, operator mistakes, and malicious attacks are a major cause of service interruptions and they can cause arbitrary behavior, that is, Byzantine faults. This article describes a new replication algorithm, BFT, that can be used to build highly available systems that tolerate Byzantine faults. BFT can be used in practice to implement real services: it performs well, it is safe in asynchronous environments such as the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. BFT has been implemented as a generic program library with a simple interface. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations, the most important of which is the use of symmetric cryptography to authenticate messages. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. This supports our claim that the BFT library can be used to build practical systems that tolerate Byzantine faults.

Question: What is the first Byzantine-fault-tolerant NFS file system?


Modeling, simulation, and development of a robotic dolphin prototype. Abilities of sea animals and efficiency of fish swimming are a few of the impressive solutions of nature. In this paper, design, modeling, simulation and development studies of a robotic dolphin prototype, entirely inspired by the bottlenose dolphin (Tursiops truncatus), are presented. The first section focuses on the design principles and core design features of the prototype. In the second section, modeling and simulation studies which consist of hydrodynamics, kinematics and dynamical analysis of the robotic dolphin are presented. Dynamical simulations of the underwater behavior of the prototype are included in this section. The third section focuses on the general prototype development from mechanical construction to control system structure. Finally in the last section, experimental results obtained through the development of the prototype are discussed.

Question: What is the purpose of the paper?


PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring. Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users' conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.

Question: What is the cost of the PhysioDroid?


Adaptive flow orientation-based feature extraction in fingerprint images. -A reliable method for extracting structural features from fingerprint images is presented. Viewing fingerprint images as a textured image, an orientation flow field is computed. The rest of the stages in the algorithm use the flow field to design adaptive filters for the input image. To accurately locate ridges, a waveform projection-based ridge segmentation algorithm is used. The ridge skeleton image is obtained and smoothed using morphological operators to detect the features. A large number of spurious features from the detected set of minutiae is deleted by a postprocessing stage. The performance of the proposed algorithm has been evaluated by computing a "goodness index" (GI) which compares the results of automatic extraction with manually extracted ground truth. The significance of the observed GI values is determined by comparing the index for a set of fingerprints against the GI values obtained under a baseline distribution. The detected features are observed to be reliable and accurate. Fingerprints Feature extraction Texture Flow orientation Minutiae Segmentation Skeleton

Question: What is a good fingerprint image?


Deep Learning with Limited Numerical Precision. Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.

Question: How can we train a deep neural network using limited numerical precision?


Evaluation of the Wireless M-Bus standard for future smart water grids. The most recent Wireless Sensor Networks technologies can provide viable solutions to perform automatic monitoring of the water grid, and smart metering of water consumptions. However, sensor nodes located along water pipes cannot access power grid facilities, to get the necessary energy imposed by their working conditions. In this sense, it is of basic importance to design the network architecture in such a way as to require the minimum possible power. This paper investigates the suitability of the Wireless Metering Bus protocol for possible adoption in future smart water grids, by evaluating its transmission performance, through simulations and experimental tests executed by means of prototype sensor nodes.

Question: How to evaluate the transmission performance of a wireless sensor network?


Top-down control of visual attention in object detection. Current computational models of visual attention focus on bottom-up information and ignore scene context. However, studies in visual cognition show that humans use context to facilitate object detection in natural scenes by directing their attention or eyes to diagnostic regions. Here we propose a model of attention guidance based on global scene config uration. We show that the statistics of low-level features across the scene image determine where a specific object (e.g. a person) should be located. Human eye movements show that regions chosen by the top-down model agree with regions scrutinized by human observers performing a visual search task for people. The results validate the proposition that top-down information from visual context modulates the saliency of image regions during the task of object de tection. Contextual information provides a shortcut for effi cient object detection systems.

Question: What is the difference between bottom-up and top-down attention?


A Hierarchical Thread Scheduler and Register File for Energy-Efficient Throughput Processors. Modern graphics processing units (GPUs) employ a large number of hardware threads to hide both function unit and memory access latency. Extreme multithreading requires a complex thread scheduler as well as a large register file, which is expensive to access both in terms of energy and latency. We present two complementary techniques for reducing energy on massively-threaded processors such as GPUs. First, we investigate a two-level thread scheduler that maintains a small set of active threads to hide ALU and local memory access latency and a larger set of pending threads to hide main memory latency. Reducing the number of threads that the scheduler must consider each cycle improves the scheduler's energy efficiency. Second, we propose replacing the monolithic register file found on modern designs with a hierarchical register file. We explore various trade-offs for the hierarchy including the number of levels in the hierarchy and the number of entries at each level. We consider both a hardware-managed caching scheme and a software-managed scheme, where the compiler is responsible for orchestrating all data movement within the register file hierarchy. Combined with a hierarchical register file, our two-level thread scheduler provides a further reduction in energy by only allocating entries in the upper levels of the register file hierarchy for active threads. Averaging across a variety of real world graphics and compute workloads, the active thread count can be reduced by a factor of 4 with minimal impact on performance and our most efficient three-level software-managed register file hierarchy reduces register file energy by 54%.

Question: How many threads does a GPU have?


The challenge problem for automated detection of 101 semantic concepts in multimedia. We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that affect performance of multimedia analysis methods, while at the same time fostering repeatability of experiments. To arrive at a challenge problem, we provide a general scheme for the systematic examination of automated concept detection methods, by decomposing the generic video indexing problem into 2 unimodal analysis experiments, 2 multimodal analysis experiments, and 1 combined analysis experiment. For each experiment, we evaluate generic video indexing performance on 85 hours of international broadcast news data, from the TRECVID 2005/2006 benchmark, using a lexicon of 101 semantic concepts. By establishing a minimum performance on each experiment, the challenge problem allows for component-based optimization of the generic indexing issue, while simultaneously offering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that inuence video indexing performance, the challenge offers to the research community a manually annotated concept lexicon, pre-computed low-level multimedia features, trained classifier models, and five experiments together with baseline performance, which are all available at http://www.mediamill.nl/challenge/.

Question: What is the challenge problem for generic video indexing?


Wavelet support vector machine. An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis. Computer simulations show the feasibility and validity of wavelet support vector machines (WSVMs) in regression and pattern recognition.

Question: What is a wavelet kernel?


In vivo 3-dimensional analysis of scapular kinematics: comparison of dominant and nondominant shoulders.. BACKGROUND Alterations in scapular motion frequently are seen in association with various shoulder disorders. It is common clinically to compare the pathological shoulder with the contralateral shoulder, in spite of arm dominance, to characterize the disorder. However, there have been few articles that test the underlying assumption that dominant and nondominant shoulders exhibit comparable dynamic kinematics. The purpose of this study was to compare the 3-dimensional (3-D) scapular kinematics of dominant and nondominant shoulders during dynamic scapular plane elevation using 3-D-2-D (2-dimensional) registration techniques.   MATERIALS AND METHODS Twelve healthy males with a mean age of 32 years (range, 27-36) were enrolled in this study. Bilateral fluoroscopic images during scapular plane elevation and lowering were taken, and CT-derived 3-D bone models were matched with the silhouette of the bones in the fluoroscopic images using 3-D-2-D registration techniques. Angular values of the scapula and scapulohumeral rhythm were compared between dominant and nondominant shoulders with statistical analysis.   RESULTS There was a significant difference in upward rotation angles between paired shoulders (P < .001), while significant differences were not found in the other angular values and scapulohumeral rhythm. The dominant scapulae were 10° more downwardly rotated at rest and 4° more upwardly rotated during elevation compared to the nondominant scapulae.   DISCUSSION/CONCLUSION Scapular motion was not the same between dominant and nondominant arms in healthy subjects. The dominant scapula was rotated further downward at rest and reached greater upward rotation with abduction. These differences should be considered in clinical assessment of shoulder pathology.

Question: What is the difference between dominant and nondominant shoulders?


CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases. Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domainindependent framework, called COTYPE, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. COTYPE, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of COTYPE across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.

Question: What is the canadian military?


Polymorphic Encryption and Pseudonymisation for Personalised Healthcare. Polymorphic encryption and Pseudonymisation, abbreviated as PEP, form a novel approach for the management of sensitive personal data, especially in health care. Traditional encryption is rather rigid: once encrypted, only one key can be used to decrypt the data. This rigidity is becoming an every greater problem in the context of big data analytics, where different parties who wish to investigate part of an encrypted data set all need the one key for decryption. Polymorphic encryption is a new cryptographic technique that solves these problems. Together with the associated technique of polymorphic pseudonymi-sation new security and privacy guarantees can be given which are essential in areas such as (personalised) healthcare, medical data collection via self-measurement apps, and more generally in privacy-friendly identity management and data analytics. The key ideas of polymorphic encryption are: 1. Directly after generation, data can be encrypted in a 'polymorphic' manner and stored at a (cloud) storage facility in such a way that the storage provider cannot get access. Crucially, there is no need to a priori fix who gets to see the data, so that the data can immediately be protected. For instance a PEP-enabled self-measurement device will store all its measurement data in polymorphically encrypted form in a back-end data base. 2. Later on it can be decided who can decrypt the data. This decision will be made on the basis of a policy, in which the data subject should play a key role. The user of the PEP-enabled device can, for instance, decide that doctors X, Y, Z may at some stage decrypt to use the data in their diagnosis, or medical researcher groups A, B, C may use it for their investigations, or third parties U, V, W may use it for additional services, etc. 3. This 'tweaking' of the encrypted data to make it decryptable by a specific party can be done in a blind manner. It will have to b

Question: What is the difference between polymorphic encryption and pseudonymisation?


When is it biased?: assessing the representativeness of twitter's streaming API. Twitter shares a free 1% sample of its tweets through the "Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.

Question: How to find bias in twitter's streaming API?


Information-Based Objective Functions for Active Data Selection. Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.

Question: What is the difference between the three criteria for data selection?


A Survey on Security for Mobile Devices. Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research field is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach.

Question: What is the state of the art on security solutions for mobile devices?


A Systematic Analysis of XSS Sanitization in Web Application Frameworks. While most research on XSS defense has focused on techniques for securing existing applications and re-architecting browser mechanisms, sanitization remains the industry-standard defense mechanism. By streamlining and automating XSS sanitization, web application frameworks stand in a good position to stop XSS but have received little research attention. In order to drive research on web frameworks, we systematically study the security of the XSS sanitization abstractions frameworks provide. We develop a novel model of the web browserions frameworks provide. We develop a novel model of the web browser and characterize the challenges of XSS sanitization. Based on the model, we systematically evaluate the XSS abstractions in 14 major commercially-used web frameworks. We find that frameworks often do not address critical parts of the XSS conundrum. We perform an empirical analysis of 8 large web applications to extract the requirements of sanitization primitives from the perspective of realworld applications. Our study shows that there is a wide gap between the abstractions provided by frameworks and the requirements of applications.

Question: What is the difference between XSS and SQL injection?


Exploring Semantic Properties of Sentence Embeddings. Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In this paper, we assess to what extent prominent sentence embedding methods exhibit select semantic properties. We propose a framework that generate triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their sentence embeddings.

Question: What is the semantic properties of sentence embeddings?


Design of planar substrate integrated waveguide (SIW) phase shifter using air holes. In this paper, a design of Substrate Intergrated Waveguide (SIW) Phase Shifter is designed by placing multiple rows of air holes in a single substrate which modifies effective dielectric constant of the material and as a result, the phase of the propagating wave is modified while maintaining insertion loss less than 0.8 dB. The proposed design operates in X band (8-12 GHz) having reflection coefficient better than -15 dB within a band of 3 GHz (31.5%) and a phase shift of 24.8°±4.7° throughout the operating band.

Question: What is the design of a phase shifter?


BLIS: A Framework for Rapidly Instantiating BLAS Functionality. The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in ISO C99 so that it can be reused and/or reparameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL).  Replicated Computational Results Certified: This article was subjected to an optional supplementary review process in which the computational results leading to the main conclusions of the paper were independently replicated. A separate report with the results of this process is published in this issue (see: http://dx.doi.org/10.1145/2738033). The certification process is described in Michael Heroux's editorial: http://dx.doi.org/10.1145/2743015.

Question: What is the BLAS-like Library Instantiation Software (BLIS)?


Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. The efforts to increasing the quantity and quality of rice production are obstructed by the paddy disease. This research attempted to identify the four major paddy diseases in Indonesia (leaf blast, brown spot, bacterial leaf blight, and tungro) using fractal descriptors to analyze the texture of the lesions. The lesion images were extracted manually. The descriptors of `S' component of each lesion images then used in classification process using probabilistic neural networks. This techniques achieved at least 83.00% accuracy when identifying the diseases. This method has a potential to be used as one of the feature if it combined with other features, especially when two diseases with relatively same color involved.

Question: What is the paddy disease?


A distributed ADMM approach for mobile data offloading in software defined network. Mobile data offloading has been introduced to alleviate the congestion of cellular networks and to improve the quality of service for mobile end users. This paper presents a distributed mechanism for mobile data offloading in software defined network (SDN) at the network edge. In SDN, the data traffic of base stations (BSs) can be dynamically offloaded to access points (APs), which is enabled by the SDN controller. The SDN controller formulates a revenue maximization problem to optimize the data offloading decision, and solves the problem in a fully distributed fashion. The proposed mechanism is based on the proximal Jacobian multi-block alternating direction method of multipliers (ADMM). BSs and APs perform the offloading decision update concurrently, and are coordinated by the SDN controller through dual variables to reach a consensus on the offloading demand and supply. Numerical simulations validate the effectiveness of the proposed algorithm.

Question: What is a distributed ADMM approach for mobile data offloading in software defined network?


Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Measures of an athlete's heart rate variability (HRV) have shown potential to be of use in the prescription of training. However, little data exists on elite athletes who are regularly exposed to high training loads. This case study monitored daily HRV in two elite triathletes (one male: 22 year, $$ \dot{V}$$ O2max 72.5 ml kg min−1; one female: 20 year, $$ \dot{V}$$ O2max 68.2 ml kg min−1) training 23 ± 2 h per week, over a 77-day period. During this period, one athlete performed poorly in a key triathlon event, was diagnosed as non-functionally over-reached (NFOR) and subsequently reactivated the dormant virus herpes zoster (shingles). The 7-day rolling average of the log-transformed square root of the mean sum of the squared differences between R–R intervals (Ln rMSSD), declined towards the day of triathlon event (slope = −0.17 ms/week; r 2 = −0.88) in the NFOR athlete, remaining stable in the control (slope = 0.01 ms/week; r 2 = 0.12). Furthermore, in the NFOR athlete, coefficient of variation of HRV (CV of Ln rMSSD 7-day rolling average) revealed large linear reductions towards NFOR (i.e., linear regression of HRV variables versus day number towards NFOR: −0.65%/week and r 2 = −0.48), while these variables remained stable for the control athlete (slope = 0.04%/week). These data suggest that trends in both absolute HRV values and day-to-day variations may be useful measurements indicative of the progression towards mal-adaptation or non-functional over-reaching.

Question: What is the difference between a non-functional over-reaching and a mal-adaptation?


Dead Code Detection Method Based on Program Slicing. Malicious code writers often insert dead code into the source code to prevent reverse engineer analysis. There are few methods for detecting dead code used in conjunction with opaque predicates. In this paper, a method based on program slicing for dead code detection is presented, which combines static slicing analysis and dynamic slicing analysis. Further, we design a dead code detection framework in the LLVM compiler infrastructure. The proposed method is used to detect the dead code inserted in some benchmark programs. The experimental results show that the proposed method has high detection rate.

Question: How to detect dead code in the source code?


Differentiating anxiety forms and their role in academic performance from primary to secondary school. INTRODUCTION Individuals with high levels of mathematics anxiety are more likely to have other forms of anxiety, such as general anxiety and test anxiety, and tend to have some math performance decrement compared to those with low math anxiety. However, it is unclear how the anxiety forms cluster in individuals, or how the presence of other anxiety forms influences the relationship between math anxiety and math performance.   METHOD We measured math anxiety, test anxiety, general anxiety and mathematics and reading performance in 1720 UK students (year 4, aged 8-9, and years 7 and 8, aged 11-13). We conducted latent profile analysis of students' anxiety scores in order to examine the developmental change in anxiety profiles, the demographics of each anxiety profile and the relationship between profiles and academic performance.   RESULTS Anxiety profiles appeared to change in specificity between the two age groups studied. Only in the older students did clusters emerge with specifically elevated general anxiety or academic anxiety (test and math anxiety). Our findings suggest that boys are slightly more likely than girls to have elevated academic anxieties relative to their general anxiety. Year 7/8 students with specifically academic anxiety show lower academic performance than those who also have elevated general anxiety.   CONCLUSIONS There may be a developmental change in the specificity of anxiety and gender seems to play a strong role in determining one's anxiety profile. The anxiety profiles present in our year 7/8 sample, and their relationships with math performance, suggest a bidirectional relationship between math anxiety and math performance.

Question: How do you differentiate between anxiety forms?


Distributed active transformer-a new power-combining andimpedance-transformation technique. In this paper, we compare the performance of the newly introduced distributed active transformer (DAT) structure to that of conventional on-chip impedance-transformations methods. Their fundamental power-efficiency limitations in the design of high-power fully integrated amplifiers in standard silicon process technologies are analyzed. The DAT is demonstrated to be an efficient impedance-transformation and power-combining method, which combines several low-voltage push-pull amplifiers in series by magnetic coupling. To demonstrate the validity of the new concept, a 2.4-GHz 1.9-W 2-V fully integrated power-amplifier achieving a power-added efficiency of 41% with 50input and output matching has been fabricated using 0.35-μm CMOS transistors Item Type: Article Additional Information: © Copyright 2002 IEEE. Reprinted with permission. Manuscript received May 27, 2001. [Posted online: 2002-08-07] This work was supported by the Intel Corporation, the Army Research Office, the Jet Propulsion Laboratory, Infinion, and the National Science Foundation. The authors thank Conexant Systems for chip fabrication, particularly R. Magoon, F. In'tveld, J. Powell, A. Vo, and K. Moye. K. Potter, D. Ham, and H.Wu, all of the California Institute of Technology (Caltech), Pasadena, deserve special thanks for their assistance. The technical support for CAD tools from Agilent Technologies and Sonnet Software Inc., Liverpool, NY, are also appreciated. "Special Issue on Silicon-Based RF and Microwave Integrated Circuits", IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 1, part 2 Subject

Question: What is the difference between a distributed active transformer and a distributed impedance transformer?


Semantic Labeling of Aerial and Satellite Imagery. Inspired by the recent success of deep convolutional neural networks (CNNs) and feature aggregation in the field of computer vision and machine learning, we propose an effective approach to semantic pixel labeling of aerial and satellite imagery using both CNN features and hand-crafted features. Both CNN and hand-crafted features are applied to dense image patches to produce per-pixel class probabilities. Conditional random fields (CRFs) are applied as a postprocessing step. The CRF infers a labeling that smooths regions while respecting the edges present in the imagery. The combination of these factors leads to a semantic labeling framework which outperforms all existing algorithms on the International Society of Photogrammetry and Remote Sensing (ISPRS) two-dimensional Semantic Labeling Challenge dataset. We advance state-of-the-art results by improving the overall accuracy to 88% on the ISPRS Semantic Labeling Contest. In this paper, we also explore the possibility of applying the proposed framework to other types of data. Our experimental results demonstrate the generalization capability of our approach and its ability to produce accurate results.

Question: What is the best semantic labeling algorithm?


Algorithms for time series knowledge mining. Temporal patterns composed of symbolic intervals are commonly formulated with Allen's interval relations originating in temporal reasoning. This representation has severe disadvantages for knowledge discovery. The Time Series Knowledge Representation (TSKR) is a new hierarchical language for interval patterns expressing the temporal concepts of coincidence and partial order. We present effective and efficient mining algorithms for such patterns based on itemset techniques. A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms. On a real data set a concise set of TSKR patterns can explain the underlying temporal phenomena, whereas the patterns found with Allen's relations are far more numerous yet only explain fragments of the data.

Question: What is the difference between the time series knowledge representation and the time series knowledge representation?


Real Time Drowsy Driver Identification Using Eye Blink Detection. HUMAN COMPUTER INTERFACE (HCI) systems are designed for use in assisting people in various aspects. Driving support systems such as navigating systems are getting commoner by the day. The capability of driving support systems to detect the level of driver's alertness is very important in ensuring road safety. By observation of blink pattern and eye movements, driver fatigue can be detected early enough to prevent collisions caused by drowsiness. The analysis of face image are widely used in security systems, face recognition, criminal focusing etc. In this specific study a non recursive system have been designed to detect shutting of eyes of the person driving an automobile. Real time detection of driver's eyes is processed using image processing in MATLAB to detect whether the eye remains closed more than the fixed duration thus indicating condition of fatigue and raise an alarm which could prevent a collision. The driving support systems have been found lacking in detecting the influence of drug or alcohol causing great degree of risks to the commuters. This study has found that eye blink patterns are starkly different for persons under the influence of drugs and can be easily detected by the system designed by us.

Question: How can we detect the influence of drugs on the driver?


A Space Vector Modulation Scheme of the Quasi-Z-Source Three-Level T-Type Inverter for Common-Mode Voltage Reduction. The conventional three-level inverter suffers the limitation of voltage buck operation. In order to give both voltage buck and boost operation capability, the quasi-Z-source three-level T-type inverter (3LT<inline-formula> <tex-math notation="LaTeX">$^2$</tex-math></inline-formula>I) has been proposed. This paper further proposes a space vector modulation (SVM) scheme for the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX">$^2$</tex-math> </inline-formula>I to reduce the magnitude and slew rate of common-mode voltage (CMV). By properly selecting the shoot-through phase, the shoot-through states are inserted within zero vector in order not to affect the active states and output voltage. Doing so, the CMV generated by the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX"> $^2$</tex-math></inline-formula>I is restricted within one-sixth of dc-link voltage, and voltage boosting and CMV reduction can be simultaneously realized. In addition, high dc-link voltage utilization can be maintained. The proposed scheme has been verified in both simulations and experiments. Comparisons are conducted with the conventional SVM method and the phase-shifted sinusoidal PWM method.

Question: What is the difference between the quasi-Z-source three-level T-type inverter and the conventional three-level inverter?


Quality-of-service in cloud computing: modeling techniques and their applications. Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

Question: What is the state of the art of QoS modeling approaches suitable for cloud systems?


Direction of Arrival Estimation of GNSS Signals Based on Synthetic Antenna Array. Jammer and interference are sources of errors in positions estimated by GNSS receivers. The interfering signals reduce signal-to-noise ratio and cause receiver failure to correctly detect satellite signals. Because of the robustness of beamforming techniques to jamming and multipath mitigation by placing nulls in direction of interference signals, an antenna array with a set of multi-channel receivers can be used to improve GNSS signal reception. Spatial reference beam forming uses the information in the Direction Of Arrival (DOA) of desired and interference signals for this purpose. However, using a multi-channel receiver is not applicable in many applications for estimating the Angle Of Arrival (AOA) of the signal (hardware limitations or portability issues). This paper proposes a new method for DOA estimation of jammer and interference signals based on a synthetic antenna array. In this case, the motion of a single antenna can be used to estimate the AOA of the interfering signals.

Question: How to estimate the direction of arrival of a signal?


ViBe: A Disruptive Method for Background Subtraction. The proliferation of the video surveillance cameras, which accounts for the vast majority of cameras worldwide, has resulted in the need to nd methods and algorithms for dealing with the huge amount of information that is gathered every second. This encompasses processing tasks, such as raising an alarm or detouring moving objects, as well as some semantic tasks like event monitoring, trajectory or ow analysis, counting people, etc.

Question: What is the name of the algorithm?


Analyzing and labeling telecom communities using structural properties. Social network analysis and mining has been highly influenced by the online social web sites, telecom consumer data and instant messaging systems and has widely analyzed the presence of dense communities using graph theory and machine learning techniques. Mobile social network analysis is the mapping and measuring of interactions and flows between people, groups, and organizations based on the usage of their mobile communication services. Community identification and mining is one of the recent major directions in social network analysis. In this paper we find the communities in the network based on a modularity factor. Then we propose a graph theory-based algorithm for further split of communities resulting in smaller sized and closely knit sub-units, to drill down and understand consumer behavior in a comprehensive manner. These sub-units are then analyzed and labeled based on their group behavior pattern. The analysis is done using two approaches:—rule-based, and cluster-based, for comparison and the able usage of information for suitable labeling of groups. Moreover, we measured and analyzed the uniqueness of the structural properties for each small unit; it is another quick and dynamic way to assign suitable labels for each distinct group. We have mapped the behavior-based labeling with unique structural properties of each group. It reduces considerably the time taken for processing and identifying smaller sub-communities for effective targeted marketing. The efficiency of the employed algorithms was evaluated on a large telecom dataset in three different stages of our work.

Question: What is the difference between a telecom community and a social network?


Relationship Between Awareness, Knowledge and Attitudes Towards Environmental Education Among Secondary School Students in Malaysia. The importance of environmental education (EE) is well known globally among societies. Environmental education is gradually promoted as a sustainable tool in protection of the environment Environmental education is found across school curriculums in Malaysia. The objectives of the curriculum are environmental attitude, knowledge and awareness (AKA) where has been investigated in the current study. The study was conducted to identify the relationship between environmental awareness, knowledge and attitude among secondary school students. The survey was conducted on 470 respondents who were in Form Four (16 years old) in Kajang city, Selangor, Malaysia. An instrument which included (48 questions) was employed to investigate the relationship between awareness, knowledge and attitude. The results of Person Correlation showed a significant but weak relationship between awareness and knowledge on environmental issues while there was high relationship observed between awareness and attitudes among respondents. Moreover, the statistical test showed a negligible relationship between knowledge and attitude among students about environment. The study concluded that a high level of awareness and knowledge plus positive attitude of students may come have been achieved from the families of respondents, teachers, media, private reading and school curriculums regarding the environment that increases the environmental view among students as well as overall in the society. The study recommended that environmental education subject necessarily might be considered as an independent syllabus in Malaysian education system.

Question: What is the relationship between awareness, knowledge and attitude among secondary school students in Malaysia?


Failure Trends in a Large Disk Drive Population. It is estimated that over 90% of all new information produced in the world is being stored on magnetic media, most of it on hard disk drives. Despite their importance, there is relatively little published work on the failure patterns of disk drives, and the key factors that affect their lifetime. Most available data are either based on extrapolation from accelerated aging experiments or from relatively modest sized field studies. Moreover, larger population studies rarely have the infrastructure in place to collect health signals from components in operation, which is critical information for detailed failure analysis. We present data collected from detailed observations of a large disk drive population in a production Internet services deployment. The population observed is many times larger than that of previous studies. In addition to presenting failure statistics, we analyze the correlation between failures and several parameters generally believed to impact longevity. Our analysis identifies several parameters from the drive's self monitoring facility (SMART) that correlate highly with failures. Despite this high correlation, we conclude that models based on SMART parameters alone are unlikely to be useful for predicting individual drive failures. Surprisingly, we found that temperature and activity levels were much less correlated with drive failures than previously reported.

Question: What is the failure rate of disk drives?


Unequal Wilkinson Power Divider With Reduced Arm Length For Size Miniaturization. This paper presents a compact unequal Wilkinson power divider (WPD) with a reduced arm length. The unequal power divider consists of four arms and the isolation elements, including a compensating capacitor and isolation resistor. Compared with the conventional WPD, the electric length θ of the arms between the input port and the isolation elements is much shorter, and thus, compact size can be obtained. A theoretical analysis is carried out, and design equations are obtained. In addition, a study is carried out to characterize the isolation bandwidth and output port return loss bandwidth with different values of θ. For validation, two power dividers operating at 1.5 GHz are implemented with various size and isolation bandwidths. Around 50% size reduction and good performance are obtained.

Question: How to design a power divider?


Cross-Domain Ranking via Latent Space Learning. We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.

Question: What is the cross-domain ranking?


Satellite Imagery Multiscale Rapid Detection with Windowed Networks. Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses over 64 km and over 250 million pixels. Another challenge is that objects of interest are often minuscule (∼ 10 pixels in extent even for the highest resolution imagery), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resolution at a rate of ≥ 0.2 km/s. Building upon the tensorflow object detection API paper [9], this pipeline offers a unified approach to multiple object detection frameworks that can run inference on images of arbitrary size. The SIMRDWN pipeline includes a modified version of YOLO (known as YOLT [25]), along with the models in [9]: SSD [14], Faster R-CNN [22], and R-FCN [3]. The proposed approach allows comparison of the performance of these four frameworks, and can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. For objects of very different scales (e.g. airplanes versus airports) we find that using two different detectors at different scales is very effective with negligible runtime cost. We evaluate large test images at native resolution and find mAP scores of 0.2 to 0.8 for vehicle localization, with the YOLT architecture achieving both the highest mAP and fastest inference speed.

Question: What is the fastest object detection framework?


Estimating Crowd Density in an RF-Based Dynamic Environment. Crowd density estimating is a crucial service in many applications (e.g., smart guide, crowd control, etc.), which is often conducted using pattern recognition technologies based on video surveillance. However, these kinds of methods are high cost, and cannot work well in low-light environments. Radio frequency based technologies are adopted more and more in indoor application, since radio signal strength (RSS) can be easily obtained by various wireless devices without additional cost. In this paper, we introduce a low cost crowd density estimating method using wireless sensor networks. The proposed approach is a device-free crowd counting approach without objects carrying any assistive device. It is hard to count objects based on RSS measurement, since different number of mobile people at different positions often generates different RSS due to the multipath phenomenon. This paper utilizes the space-time relativity of crowd distribution to reduce the estimation errors. The proposed approach is an iterative process, which contains three phases: the training phase, the monitoring phase, and the calibrating phase. Our experiments are implemented based on TelosB sensor platform. We also do some large-scale simulations to verify the feasibility and the effectiveness of our crowd density estimating approach.

Question: How to estimate crowd density in an RF-based dynamic environment?


How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks. Maybe the single most important goal of representation learning is making subsequent learning faster. Surprisingly, this fact is not well reflected in the way embeddings are evaluated. In addition, recent practice in word embeddings points towards importance of learning specialized representations. We argue that focus of word representation evaluation should reflect those trends and shift towards evaluating what useful information is easily accessible. Specifically, we propose that evaluation should focus on data efficiency and simple supervised tasks, where the amount of available data is varied and scores of a supervised model are reported for each subset (as commonly done in transfer learning). In order to illustrate significance of such analysis, a comprehensive evaluation of selected word embeddings is presented. Proposed approach yields a more complete picture and brings new insight into performance characteristics, for instance information about word similarity or analogy tends to be non–linearly encoded in the embedding space, which questions the cosine–based, unsupervised, evaluation methods. All results and analysis scripts are available online.

Question: How to evaluate word embeddings?


A Simple Flood Forecasting Scheme Using Wireless Sensor Networks. This paper presents a forecasting model designed using WSNs( Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.

Question: What is a polynomial?


Accessibility Evaluation of Classroom Captions. Real-time captioning enables deaf and hard of hearing (DHH) people to follow classroom lectures and other aural speech by converting it into visual text with less than a five second delay. Keeping the delay short allows end-users to follow and participate in conversations. This article focuses on the fundamental problem that makes real-time captioning difficult: sequential keyboard typing is much slower than speaking. We first surveyed the audio characteristics of 240 one-hour-long captioned lectures on YouTube, such as speed and duration of speaking bursts. We then analyzed how these characteristics impact caption generation and readability, considering specifically our human-powered collaborative captioning approach. We note that most of these characteristics are also present in more general domains. For our caption comparison evaluation, we transcribed a classroom lecture in real-time using all three captioning approaches. We recruited 48 participants (24 DHH) to watch these classroom transcripts in an eye-tracking laboratory. We presented these captions in a randomized, balanced order. We show that both hearing and DHH participants preferred and followed collaborative captions better than those generated by automatic speech recognition (ASR) or professionals due to the more consistent flow of the resulting captions. These results show the potential to reliably capture speech even during sudden bursts of speed, as well as for generating "enhanced" captions, unlike other human-powered captioning approaches.

Question: How to improve the quality of captions?


Forecasting market demand for new telecommunications services: an introduction. The marketing team of a new telecommunications company is usually tasked with producing forecasts for diverse stakeholders with different needs. Consequently, those outside marketing often realize neither the many reasons for developing forecasts nor the marketing theory used and the challenges involved in doing so. Based on our three decades of experience working with telecommunications operators around the world we seek to redress this situation by presenting a discussion of the issues involved in demand forecasting for new communications services. 2002 Elsevier Science Ltd. All rights reserved.

Question: What is the difference between demand forecasting and demand management?


Cross-layer secure cyber-physical control system design for networked 3D printers. Due to the high costs of 3D-printing infrastructure, outsourcing the production to third parties specializing in 3D-printing process becomes necessary. The integration of a 3D-printing system with networked communications constitutes a cyber-physical system, bringing new security challenges. Adversaries can explore the vulnerabilities of networks to damage the physical parts of the system. In this paper, we explore the vulnerabilities of 3D-printing systems, and design a cross-layer approach for the system. At the physical layer, we use a Markov jump system to model the system and develop a robust control policy to deal with uncertainties. At the cyber-layer, we apply FlipIt game to model the contention between the defender and attacker for the control of the 3D-printing system. To connect these two layers, we develop a Stackelberg framework to capture the interactions between cyber-layer attacker and defender game and the physical-layer controller and disturbance game, and define a new equilibrium concept that captures interdependence of the zero-sum and FlipIt games. We present numerical examples to better understand the equilibria and design defense strategies for 3D printers as a tradeoff between security and robustness.

Question: What is a Markov jump system?


Contact-less Transfer of Energy by means of a Rotating Transformer. This paper examines the electrical properties of a rotating transformer used for contact-less transfer of energy to rotating equipment. Two winding layouts are analysed theoretically and experimentally. The reluctance modelling provides a deep understanding of how the geometry of the core and windings affect the electrical behaviour of the component. Theoretical calculations, measured results and finite element analysis are used to compare the proposed layouts. Basic design guidelines are given to adjust the leakage and magnetising inductances. The selection of a phaseshifted full bridge converter is suggested for applications at the power level of 1kW.

Question: How to design a contact-less transformer?


The Bluetooth radio system. A few years ago it was recognized that the vision of a truly low-cost, low-power radio-based cable replacement was feasible. Such a ubiquitous link would provide the basis for portable devices to communicate together in an ad hoc fashion by creating personal area networks which have similar advantages to their office environment counterpart, the local area network. Bluetooth/sup TM/ is an effort by a consortium of companies to design a royalty-free technology specification enabling this vision. This article describes the radio system behind the Bluetooth concept. Designing an ad hoc radio system for worldwide usage poses several challenges. The article describes the critical system characteristics and motivates the design choices that have been made.

Question: What is the design of the bluetooth radio system?


Joint Haar-like features for face detection. In this paper, we propose a new distinctive feature, called joint Haar-like feature, for detecting faces in images. This is based on co-occurrence of multiple Haar-like features. Feature co-occurrence, which captures the structural similarities within the face class, makes it possible to construct an effective classifier. The joint Haar-like feature can be calculated very fast and has robustness against addition of noise and change in illumination. A face detector is learned by stagewise selection of the joint Haar-like features using AdaBoost. A small number of distinctive features achieve both computational efficiency and accuracy. Experimental results with 5, 676 face images and 30,000 nonface images show that our detector yields higher classification performance than Viola and Jones' detector; which uses a single feature for each weak classifier. Given the same number of features, our method reduces the error by 37%. Our detector is 2.6 times as fast as Viola and Jones' detector to achieve the same performance

Question: How fast is the detector?


Deep learning with support vector data description. One of the most critical problems for machine learning methods is overfitting. The overfitting problem is a phenomenon in which the accuracy of the model on unseen data is poor whereas the training accuracy is nearly perfect. This problem is particularly severe in complex models that have a large set of parameters. In this paper, we propose a deep learning neural network model that adopts the support vector data description (SVDD). The SVDD is a variant of the support vector machine, which has high generalization performance by acquiring a maximal margin in one-class classification problems. The proposed model strives to obtain the representational power of deep learning. Generalization performance is maintained using the SVDD. The experimental results showed that the proposed model can learn multiclass data without severe overfitting problems.

Question: What is the difference between deep learning and support vector machine?


A Regularization Method with Inference of Trust and Distrust in Recommender Systems. In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users' preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users' preferences are sparse, but also users' social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users' explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends' trust and foes' distrust degrees on users' preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users' and items' latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users' preferences and social relationships, significantly outperforming several state-of-the-art methods.

Question: What is the regularization method?


Dynamic time warping constraint learning for large margin nearest neighbor classification. Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method. ! 2011 Elsevier Inc. All rights reserved.

Question: How to learn the DTW constraints?


Snowball: extracting relations from large plain-text collections. Text documents often contain valuable structured data that is hidden Yin regular English sentences. This data is best exploited infavailable as arelational table that we could use for answering precise queries or running data mining tasks.We explore a technique for extracting such tables from document collections that requires only a handful of training examples from users. These examples are used to generate extraction patterns, that in turn result in new tuples being extracted from the document collection.We build on this idea and present our Snowball system. Snowball introduces novel strategies for generating patterns and extracting tuples from plain-text documents.At each iteration of the extraction process, Snowball evaluates the quality of these patterns and tuples without human intervention,and keeps only the most reliable ones for the next iteration. In this paper we also develop a scalable evaluation methodology and metrics for our task, and present a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents.

Question: What is the largest military in the world?


Efficient deep learning for stereo matching with larger image patches. Stereo matching plays an important role in many applications, such as Advanced Driver Assistance Systems, 3D reconstruction, navigation, etc. However it is still an open problem with many difficult. Most difficult are often occlusions, object boundaries, and low or repetitive textures. In this paper, we propose a method for processing the stereo matching problem. We propose an efficient convolutional neural network to measure how likely the two patches matched or not and use the similarity as their stereo matching cost. Then the cost is refined by stereo methods, such as semiglobal maching, subpixel interpolation, median filter, etc. Our architecture uses large image patches which makes the results more robust to texture-less or repetitive textures areas. We experiment our approach on the KITTI2015 dataset which obtain an error rate of 4.42% and only needs 0.8 second for each image pairs.

Question: How to measure the similarity of two patches?


A framework of traveling companion discovery on trajectory data streams. The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.

Question: What is the canadian military?


Semantic MEDLINE: An advanced information management application for biomedicine. Semantic MEDLINE integrates information retrieval, advanced natural language processing, automatic summarization, and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected graph of semantic relations, with links to the original MEDLINE citations. The ability to connect salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Semantic MEDLINE can make an impact on biomedicine by supporting scientific discovery and the timely translation of insights from basic research into advances in clinical practice and patient care. Marcelo Fiszman has an M.D. from the State University of Rio de Janeiro and a Ph.D. in biomedical informatics from the University of Utah. He was awarded a postdoctoral fellowship in biomedical informatics at the National Library of Medicine (NLM) and is currently a research scientist there. His work focuses on natural language processing algorithms that exploit symbolic, rule-based techniques for semantic interpretation of biomedical text. He is also interested in using extracted semantic information for automatic abstraction summarization and literaturebased discovery. These efforts underpin Semantic MEDLINE, which is currently under development at NLM. This innovative biomedical information management application combines document retrieval, semantic interpretation, automatic summarization, and knowledge visualization into a single application.

Question: What is the name of the application?


Non-invasive optical detection of hand gestures. In this paper we present a novel type of sensing technology for hand and finger gesture recognition that utilizes light in the invisible spectrum to detect changes in position and form of body tissue like tendons and muscles. The proposed system can be easily integrated with existing wearable devices. Our approach not only enables gesture recognition but it could potentially double to perform a variety of health related monitoring tasks (e.g. heart rate, stress).

Question: How does this work?


Multi-Instance Multi-Label Learning with Weak Label. Multi-Instance Multi-Label learning (MIML) deals with data objects that are represented by a bag of instances and associated with a set of class labels simultaneously. Previous studies typically assume that for every training example, all positive labels are tagged whereas the untagged labels are all negative. In many real applications such as image annotation, however, the learning problem often suffers from weak label; that is, users usually tag only a part of positive labels, and the untagged labels are not necessarily negative. In this paper, we propose the MIMLwel approach which works by assuming that highly relevant labels share some common instances, and the underlying class means of bags for each label are with a large margin. Experiments validate the effectiveness of MIMLwel in handling the weak label problem.

Question: What is the MIMLwel approach?


Scope+: a stereoscopic video see-through augmented reality microscope. During the usage of conventional stereo microscope, repetitive head movement are often inevitable for the users to retrieve information away from the microscope. Moreover, risks of surgeons loosing focus and increasing fatigue could also arise during microsurgeries. Therefore, Scope+, a stereoscopic video see-through augmented reality system was created not only to solve the problems mentioned above, but also to improve users stereo microscopic experience.

Question: What is a stereoscopic video see-through augmented reality microscope?


DDoS: Survey of Traceback Methods. The problem of identifying Distributed Denial of Service (DDoS) is one of the hardest threats in the internet security. It is important to protect the resource and trace from the Denial of Service (DoS) attack, but it is difficult to distinguish normal traffic and DoS attack traffic because the DoS generally hide their identities/origins. Especially the attackers often use incorrect or spoofed source IP address, so tracing the source of the denial of service is hardest in internet. Lot of techniques and methodologies are used to trace the DDoS attacks. This paper presents some of the mostly used predicting traceback techniques to solve the problem. The main goal of this paper is appraise the different traceback techniques of the DDoS. This paper evaluates the different traceback methods of the

Question: What is the difference between a denial of service attack and a distributed denial of service attack?


Goal-Oriented Requirements Engineering: A Guided Tour. Goals capture, at different levels of abstraction, the various objectives the system under consideration should achieve. Goal-oriented requirements engineering is concerned with the use of goals for eliciting, elaborating, structuring, specifying, analyzing, negotiating, documenting, and modifying requirements. This area has received increasing attention over the past few years. The paper reviews various research efforts undertaken along this line of research. The arguments in favor of goal orientation are first briefly discussed. The paper then compares the main approaches to goal modeling, goal specification and goal-based reasoning in the many activities of the requirements engineering process. To make the discussion more concrete, a real case study is used to suggest what a goal-oriented requirements engineering method may look like. Experience with such approaches and tool support are briefly discussed as well.

Question: What is goal-oriented requirements engineering?


Scratch: programming for all. "Digital fluency" should mean designing, creating, and remixing, not just browsing, chatting, and interacting.

Question: What is digital fluency?


End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression. When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users , enabling them to point out which features are important for a class, rath er than provide new training instances. We first evaluate our algorithms against other feature labeling algorit hms under idealized conditions using feature labels generated by an oracle . In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling invo lvi g a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best perfo rmers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end u sers in our study. We also perform an analysis to investigate the relative gains of incorporatin g the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by e d users is both viable and effectiv

Question: What is the difference between supervised and semi-supervised learning?


Generative adversarial networks and adversarial methods in biomedical image analysis. Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. We provide an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.

Question: What is a generative adversarial network?


A new linearization method of unbalanced electrical distribution networks. With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. As DN control and operation strategies are mostly based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages, line currents, power loss) and control variables (e.g., power injections, transformer tap positions), efficient and precise calculation of these sensitivity coefficients, i.e. linearization of DN, is of fundamental importance. In this paper, the derivation of the node voltages and power loss as functions of the nodal power injections and transformers' tap-changers positions is presented, and then solved by a Gauss-Seidel method. Compared to other approaches presented in the literature, the proposed method takes into account different load characteristics (e.g., constant PQ, constant impedance, constant current and any combination of above) of a generic multi-phase unbalanced DN and improves the accuracy of linearization. Numerical simulations on both IEEE 13 and 34 nodes test feeders show the efficiency and accuracy of the proposed method.

Question: How to calculate the node voltages and power loss as functions of the nodal power injections and transformers' tap-changers positions?


Activity Recognition from Accelerometer Data. Activity recognition fits within the bigger framework of context awareness. In this paper, we report on our efforts to recognize user activity from accelerometer data. Activity recognition is formulated as a classification problem. Performance of base-level classifiers and meta-level classifiers is compared. Plurality Voting is found to perform consistently well across different settings.

Question: What is the difference between base-level and meta-level classifiers?


Binding mechanism of PicoGreen to DNA characterized by magnetic tweezers and fluorescence spectroscopy. Fluorescent dyes are broadly used in many biotechnological applications to detect and visualize DNA molecules. However, their binding to DNA alters the structural and nanomechanical properties of DNA and, thus, interferes with associated biological processes. In this work we employed magnetic tweezers and fluorescence spectroscopy to investigate the binding of PicoGreen to DNA at room temperature in a concentration-dependent manner. PicoGreen is an ultrasensitive quinolinium nucleic acid stain exhibiting hardly any background signal from unbound dye molecules. By means of stretching and overwinding single, torsionally constrained, nick-free double-stranded DNA molecules, we acquired force-extension and supercoiling curves which allow quantifying DNA contour length, persistence length and other thermodynamical binding parameters, respectively. The results of our magnetic tweezers single-molecule binding study were well supported through analyzing the fluorescent spectra of stained DNA. On the basis of our work, we could identify a concentration-dependent bimodal binding behavior, where, apparently, PicoGreen associates to DNA as an intercalator and minor-groove binder simultaneously.

Question: What is the binding mechanism of PicoGreen to DNA?


Physician well-being: A powerful way to improve the patient experience.. Improving the patient experience—or the patientcenteredness of care—is a key focus of health care organizations. With a shift to reimbursement models that reward higher patient experience scores, increased competition for market share, and cost constraints emphasizing the importance of patient loyalty, many health care organizations consider optimizing the patient experience to be a key strategy for sustaining financial viability. A survey conducted by The Beryl Institute, which supports research on the patient experience, found that hospital executives ranked patient experience as a clear top priority, falling a close second behind quality and safety.1 According to the institute, engagement with employees, including physicians, is the most effective way to improve the patient experience, as more engaged, satisfied staffers provide better service and care to patients.2 Data on physician satisfaction support this supposition: Research has shown that physician career satisfaction closely correlates with patient satisfaction within a geographic region.3 Given the importance of physician satisfaction to the patient experience, it is concerning that dissatisfaction and burnout are on the rise among physicians. In a 2012 online poll of more than 24,000 physicians across the country, only 54 percent would choose medicine again as a career, down from 69 percent in 2011.4 Almost half of the more than 7,000 physicians responding to a recent online survey reported at least one symptom of burnout.5 Physician dissatisfaction and burnout have a profound negative effect on the patient experience of care. Physician leaders can take steps within their organizations to foster physician well-being, improving the care experience for physicians and patients while strengthening the sustainability of their organizations.

Question: What is the most effective way to improve the patient experience?


Applications of RFID in Pharmaceutical Industry. Security and safety are two important features desired in pharmaceutical supply chain and achieving the same is a challenging task. The need to secure and authenticate pharmaceutical products has increased tremendously with the emerging counterfeit product market. The motivation to introduce counterfeit pharmaceutical products in the supply chain could be to gain rapid economic benefits or affecting the reputation of strong brand in the pharmaceutical industry. RFID technology can be used to deter counterfeiting attempts. It can also be used in various other domains in the pharmaceutical industry. The main aim of this paper is to outline all the applications of RFID in the pharmaceutical industry. After explaining the main applications, we discuss how information hiding techniques could be used with RFID to offer efficient expiry date management, pharmaceutical tamper detection, and fraud detection and prevention.

Question: What is the main aim of this paper?


Interactive ramp merging planning in autonomous driving: Multi-merging leading PGM (MML-PGM). Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. The challenge of cooperative driving is estimating other vehicles' intentions. In this paper, we present a novel method to estimate other human-driven vehicles' intentions with the aim of achieving a natural and amenable cooperative driving behavior, without using wireless communication. The new approach allows the autonomous vehicle to cooperate with multiple observable merging vehicles on the ramp with a leading vehicle ahead of the autonomous vehicle in the same lane. To avoid calculating trajectories, simplify computation, and take advantage of mature Level-3 components, the new method reacts to merging cars by determining a following target for an off-the-shelf distance keeping module (ACC) which governs speed control of the autonomous vehicle. We train and evaluate the proposed model using real traffic data. Results show that the new approach has a lower collision rate than previous methods and generates more human driver-like behaviors in terms of trajectory similarity and time-to-collision to leading vehicles.

Question: How to plan a safe and natural cooperative driving behavior?


The study of evasion of packed PE from static detection. Static detection of packed portable executables (PEs) relies primarily on structural properties of the packed PE, and also on anomalies in the packed PE caused by packing tools. This paper outlines weaknesses in this method of detection. We show that these structural properties and anomalies are contingent features of the executable, and can be more or less easily modified to evade static detection.

Question: How can I evade static detection?


Revealing protein–lncRNA interaction. Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

Question: What is the current knowledge about the interaction of protein-lncRNA?


Software Engineering Economics. This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Question: What is the state of the art in software cost estimation?


Forensic Analysis and Anonymisation of Printed Documents. Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.

Question: How can I identify the source device of a printed document?


Performance analysis of MapReduce programs on Hadoop cluster. This paper discusses various MapReduce applications like pi, wordcount, grep, Terasort. We have shown experimental results of these applications on a Hadoop cluster. In this paper, performance of above application has been shown with respect to execution time and number of nodes. We find that as the number of nodes increases the execution time decreases. This paper is basically a research study of above MapReduce applications.

Question: What is the performance of MapReduce applications?


The variational approximation for Bayesian inference. The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century. It was also this article of Laplace's that introduced the mathematical techniques for the asymptotic analysis of posterior distributions that are still employed today. And it was here that the earliest example of optimum estimation can be found, the derivation and characterization of an estimator that minimized a particular measure of posterior expected loss. After more than two centuries, we mathematicians, statisticians cannot only recognize our roots in this masterpiece of our science, we can still learn from it.

Question: What is the variational approximation for Bayesian inference?


A hierarchical framework for evaluating simulation software. In simulation software selection problems, packages are evaluated either on their own merits or in comparison with other packages. In either method, a comprehensive list of criteria for evaluation of simulation software is essential for proper selection. Although various simulation software evaluation checklists do exist, there are di€erences in the lists provided and the terminologies used. This paper presents a hierarchical framework for simulation software evaluation consisting of seven main groups and several subgroups. An explanation for each criterion is provided and an analysis of the usability of the proposed framework is further discussed. Ó 1999 Elsevier Science B.V. All rights reserved.

Question: What is a simulation software evaluation framework?


Action observation activates premotor and parietal areas in a somatotopic manner: an fMRI study.. Functional magnetic resonance imaging (fMRI) was used to localize brain areas that were active during the observation of actions made by another individual. Object- and non-object-related actions made with different effectors (mouth, hand and foot) were presented. Observation of both object- and non-object-related actions determined a somatotopically organized activation of premotor cortex. The somatotopic pattern was similar to that of the classical motor cortex homunculus. During the observation of object-related actions, an activation, also somatotopically organized, was additionally found in the posterior parietal lobe. Thus, when individuals observe an action, an internal replica of that action is automatically generated in their premotor cortex. In the case of object-related actions, a further object-related analysis is performed in the parietal lobe, as if the subjects were indeed using those objects. These results bring the previous concept of an action observation/execution matching system (mirror system) into a broader perspective: this system is not restricted to the ventral premotor cortex, but involves several somatotopically organized motor circuits.

Question: What is the somatotopic organization of the premotor cortex?


Dual-Band Multi-Pole Directional Filter for Microwave Multiplexing Applications. A novel microstrip directional filter for multiplexing applications is presented. This device uses composite right-left-handed transmission lines and resonators to achieve dual-band frequency response. In addition, by cascading two or more stages using dual frequency immitance inverters, multi-pole configurations can be obtained. Simulation and experimental results are presented with good agreement.

Question: What is a dual-band directional filter?


Kodu Game Lab: a programming environment. Kodu Game Lab is a tile-based visual programming tool that enables users to learn programming concepts through making and playing computer games. Kodu is a relatively new programming language designed specifically for young children to learn through independent exploration. It is integrated in a real-time isometric 3D gaming environment that is designed to compete with modern console games in terms of intuitive user interface and graphical production values.

Question: What is a tile-based visual programming tool?


Completed Local Binary Count for Rotation Invariant Texture Classification. In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.

Question: What is the local binary count?


18-year experience in the management of men with a complaint of a small penis.. In many cultures, the erect penis has been a symbol of masculine qualities. Because of this symbolism, a penis that is less than average size can cause insecurity or embarrassment. This series reports the authors' 18-year experience in the management of 60 men with a complaint of a small penis. For 44 of these 60 men, counseling was sufficient; the other 16 had surgery, and of these, 9 were satisfied with the result. Despite limitations, the authors conclude that those men who already achieve a penis length of no less than 7.5 cm (2.95 in) in erection, have only limited benefit from penis-enhancing surgery. This particular patient category should therefore be dissuaded from surgery.

Question: What is the average penis size?


Fast Linear Model for Knowledge Graph Embeddings. This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText1.

Question: What is the fastText library?


Fuzzy logic controller for an inverted pendulum system using quantum genetic optimization. In this paper, we propose a new generalized design methodology of intelligent robust fuzzy control systems based on quantum genetic algorithm (QGA) called quantum fuzzy model that enhance robustness of fuzzy logic controllers. The QGA is adopted because of their capabilities of directed random search for global optimization to find the parameters of the shape and width of membership functions and rule set of the FLC to obtain the optimal fuzzy controller simultaneously. We test the optimal FLC obtained by the quantum computing applied on the control of dynamic balance and motion of cart-pole balancing system. We compare the proposed technique with existing mamdani fuzzy logic controller which is designed through conventional genetic algorithm. Simulation results reveal that QGA performs better than conventional GA in terms of running speed and optimizing capability.

Question: How to design a fuzzy logic controller for an inverted pendulum system?