Untangling Text Data Mining. The possibilities for data mining from large text collections are virtually untapped. Text expresses a vast, rich range of information, but encodes this information in a form that is difficult to decipher automatically. Perhaps for this reason, there has been little work in text data mining to date, and most people who have talked about it have either conflated it with information access or have not made use of text directly to discover heretofore unknown information. In this paper I will first define data mining, information access, and corpus-based computational linguistics, and then discuss the relationship of these to text data mining. The intent behind these contrasts is to draw attention to exciting new kinds of problems for computational linguists. I describe examples of what I consider to be real text data mining efforts and briefly outline recent ideas about how to pursue exploratory data analysis over text.

Question: What is text data mining?


Towards Building Forensics Enabled Cloud Through Secure Logging-as-a-Service. Collection and analysis of various logs (e.g., process logs, network logs) are fundamental activities in computer forensics. Ensuring the security of the activity logs is therefore crucial to ensure reliable forensics investigations. However, because of the black-box nature of clouds and the volatility and co-mingling of cloud data, providing the cloud logs to investigators while preserving users' privacy and the integrity of logs is challenging. The current secure logging schemes, which consider the logger as trusted cannot be applied in clouds since there is a chance that cloud providers (logger) collude with malicious users or investigators to alter the logs. In this paper, we analyze the threats on cloud users' activity logs considering the collusion between cloud users, providers, and investigators. Based on the threat model, we propose Secure-Logging-as-a-Service ( SecLaaS), which preserves various logs generated for the activity of virtual machines running in clouds and ensures the confidentiality and integrity of such logs. Investigators or the court authority can only access these logs by the RESTful APIs provided by SecLaaS, which ensures confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and proofs of past logs published periodically by the cloud providers. In prior research, we used two accumulator schemes Bloom filter and RSA accumulator to build the proofs of past logs. In this paper, we propose a new accumulator scheme - Bloom-Tree, which performs better than the other two accumulators in terms of time and space requirement.

Question: What is the difference between a hash-chain and a Bloom-Tree?


Visualizing Translation Variation: Shakespeare's Othello. Recognized as great works of world literature, Shakespeare's poems and plays have been translated into dozens of languages for over 300 years. Also, there are many re-translations into the same language, for example, there are more than 60 translations of Othello into German. Every translation is a different interpretation of the play. These large quantities of translations reflect changing culture and express individual thought by the authors. They demonstrate wide connections between different world regions today, and reveal a retrospective view of their cultural, intercultural, and linguistic histories. Researchers from Arts and Humanities at Swansea University are collecting a large number of translations of William Shakespeare's Othello. In this paper, we have developed an interactive visualization system to present, analyze and explore the variations among these different translations. Our system is composed of two parts: the structure-aware Treemap for document selection and meta data analysis, and Focus + Context parallel coordinates for in-depth document comparison and exploration. In particular, we want to learn more about which content varies highly with each translation, and which content remains stable. We also want to form hypotheses as to the implications behind these variations. Our visualization is evaluated by the domain experts from Arts and Humanities.

Question: What is the difference between a translation and a re-translation?


Facilitating Object Detection and Recognition through Eye Gaze. When compared to image recognition, object detection is a much more challenging task because it requires the accurate real-time localization of an object in the target image. In interaction scenarios, this pipeline can be simplified by incorporating the users' point of regard. Wearable eye trackers can estimate the gaze direction, but lack own processing capabilities. We enable mobile gaze-aware applications by developing an open-source platform which supports mobile eye tracking based on the Pupil headset and a smartphone running Android OS. Through our platform, we offer researchers and developers a rapid prototyping environment for gaze-enabled applications. We describe the concept, our current progress, and research implications. ACM Classification

Question: What is the ACM classification?


Gource: visualizing software version control history. This film demonstrates a tool for visualizing the development history of software projects as an interactive animation, showing developers working on the hierarchical file-directory structure of a project over the course of its development.

Question: What is gource?


Google App Engine. Cloud computing becomes more popular day by day. The "dream" of users to "to get rid" of administrators, huge investments on expensive hardware and human resources becomes true. The goal of this paper is to present a general overview of the new coming service offered by Google company Google App Engine. "What is it for?", "What can I do with the system?" and "How does it work?" are some of the questions on which this paper will try to give an answer. In addition, brief overview and description of "cloud computing" and comparison of Google App Engine with other similar products will be provided to the reader as well.

Question: What is cloud computing?


Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data. We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets.

Question: What is a dimensionality reduction with dependence distance for high-dimensional data?


Malicious PDF detection using metadata and structural features. Owed to their versatile functionality and widespread adoption, PDF documents have become a popular avenue for user exploitation ranging from large-scale phishing attacks to targeted attacks. In this paper, we present a framework for robust detection of malicious documents through machine learning. Our approach is based on features extracted from document metadata and structure. Using real-world datasets, we demonstrate the the adequacy of these document properties for malware detection and the durability of these features across new malware variants. Our analysis shows that the Random Forests classification method, an ensemble classifier that randomly selects features for each individual classification tree, yields the best detection rates, even on previously unseen malware.  Indeed, using multiple datasets containing an aggregate of over 5,000 unique malicious documents and over 100,000 benign ones, our classification rates remain well above 99% while maintaining low false positives of 0.2% or less for different classification parameters and experimental scenarios. Moreover, the classifier has the ability to detect documents crafted for targeted attacks and separate them from broadly distributed malicious PDF documents. Remarkably, we also discovered that by artificially reducing the influence of the top features in the classifier, we can still achieve a high rate of detection in an adversarial setting where the attacker is aware of both the top features utilized in the classifier and our normality model. Thus, the classifier is resilient against mimicry attacks even with knowledge of the document features, classification method, and training set.

Question: What is the difference between a malicious and benign PDF document?


Rise and fall patterns of information diffusion: model and implications. The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law?  In this paper, we propose SpikeM, a concise yet flexible analytical model for the rise and fall patterns of influence propagation. Our model has the following advantages: (a) unification power: it generalizes and explains earlier theoretical models and empirical observations; (b) practicality: it matches the observed behavior of diverse sets of real data; (c) parsimony: it requires only a handful of parameters; and (d) usefulness: it enables further analytics tasks such as fore- casting, spotting anomalies, and interpretation by reverse- engineering the system parameters of interest (e.g. quality of news, count of interested bloggers, etc.).  Using SpikeM, we analyzed 7.2GB of real data, most of which were collected from the public domain. We have shown that our SpikeM model accurately and succinctly describes all the patterns of the rise-and-fall spikes in these real datasets.

Question: What is the rise and fall patterns of information diffusion?


Emulation of Software Defined Networks Using Mininet in Different Simulation Environments. In this paper an evaluation of an SDN emulation tool called Mininet is conducted. Tests were conducted to study Mininnet limitations related to the simulation environment, resource capabilities. To evaluate the later, the scalability of Mininet in term of creating many topologies is tested with varying number of nodes and two different environment scenarios. Results show that the simulation environment has a remarkable effect on the required time for building a topology, for instance, the powerful resources scenario needed only 0.19 sec, whereas, 5.611 sec were needed when the resources were less. However, the required time were increased in both scenarios when the number of nodes was increased into 242.842 and 3718.117 sec for the powerful and less capabilities resources respectively.

Question: How to emulate software defined networks?


Efficient adaptive density estimation per image pixel for the task of background subtraction. We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms. 2005 Elsevier B.V. All rights reserved.

Question: What is the background subtraction?


A Bayesian Nonparametric Approach for Multi-label Classification. Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for prediction. Traditional classification methods have attempted to reveal these correlations in different ways. However, existing methods demand expensive computation complexity for finding such correlation structures. Furthermore, these approaches can not identify the suitable number of label-feature correlation patterns. In this paper, we propose a Bayesian nonparametric (BNP) framework for multi-label classification that can automatically learn and exploit the unknown number of multi-label correlation. We utilize the recent techniques in stochastic inference to derive the cheap (but efficient) posterior inference algorithm for the model. In addition, our model can naturally exploit the useful information from missing label samples. Furthermore, we extend the model to update parameters in an online fashion that highlights the flexibility of our model against the existing approaches. We compare our method with the state-of-the-art multi-label classification algorithms on real-world datasets using both complete and missing label settings. Our model achieves better classification accuracy while our running time is consistently much faster than the baselines in an order of magnitude.

Question: What is a Bayesian nonparametric approach for multi-label classification?


Database Security. Computer security is concerned with the ability of a computer system to enforce a security policy governing the disclosure, modification, or destruction of information. The security policy may be specific to the organization, or may be generic. For example, the DoD mandatory security (or multilevel security) poficies restrict access to classified information to cleared personnel. Discretionary security policies, on the other hand, define access restrictions based on the identity of users (or groups), the type of access (e.g., select, update, insert, delete), the specific object being accessed, and perhaps other factors (time of day, which application program is being used, etc.). Different types of users (system managers, database administrators, and ordinary users) may have different access rights to the data in the system. The access controls commonly found in most database systems are examples of discretionary access controls. This paper discusses discretionary security and mandatory security for database systems. We outline the current state of research in database security and briefly discuss some open research issues.

Question: What is database security?


Convolutional networks for images, speech, and time series.

Question: What is a convolutional network?


A Hybrid Symmetrical Voltage Multiplier. Voltage multiplier circuits are widely used in many high-voltage/low-current applications. A conventional symmetrical voltage multiplier (SVM) has much better performance, when compared with its half-wave counterpart. However, it requires a high-voltage transformer (HVT) with center-tapped secondary to perform its push-pull kind of operation. The design of an HVT with center-tapped secondary is relatively complex. This paper proposes a hybrid SVM (HSVM) for dc high-voltage applications. The multiplier is formed by cascading a diode-bridge rectifier and an SVM with diode-bridge rectifier as the first stage of multiplier. The proposed topology saves two high-voltage capacitors and requires only one secondary winding of HVT. Besides, it has lesser voltage drop and faster transient response at start-up, when compared with conventional SVM. The feasibility of the proposed HSVM is validated both by simulation and experimental results of a laboratory scaled-down prototype.

Question: What is a hybrid symmetrical voltage multiplier?


Comparative study of Hough Transform methods for circle finding. A variety of circle detection methods which are based on variations of the Hough Transform are investigated. The ,five methods considered are the standard Hough Trans,form, the Fast Hough Transform of Li et al.', a two stage Hough method, and two space saving approaches based on the method devised by Gerig and Klein2. The performance of each of the methods has been compared on synthetic imagery and real images from a metallurgical application. Figures and comments are presented concerning the accuracy, reliability, computational efficiency and storage requirements of each of the methods.

Question: What is the Hough Transform?


Comparison of Measurement-Based Call Admission Control Algorithms for Controlled-Load Service. We compare the performance of four admission control algorithms—one parameter-based and three measurementbased—for controlled-load service. The parameter-based admission control ensures that the sum of reserved resources is bounded by capacity. The three measurementbased algorithms are based on measured bandwidth, acceptance region [9], and equivalent bandwidth [7]. We use simulationon several network scenarios to evaluate the link utilization and adherence to service commitment achieved by these four algorithms.

Question: What is the difference between the parameter-based and the measurement-based algorithms?


3D head pose estimation with convolutional neural network trained on synthetic images. In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering techniques, in which we consider the variation of gender, age, race and expression. Our dataset includes 74000 head poses rendered from 37 head models. For each head pose, RGB image and annotated pose parameters are given. We evaluate our method on both synthetic and real data. The experiments show that our method improves the accuracy of head pose estimation.

Question: How to estimate head pose with convolutional neural network?


Dynamical Approximation by Hierarchical Tucker and Tensor-Train Tensors. We extend results on the dynamical low-rank approximation for the treatment of time-dependent matrices and tensors (Koch and Lubich; see [SIAM J. Matrix Anal. Appl., 29 (2007), pp. 434–454], [SIAM J. Matrix Anal. Appl., 31 (2010), pp. 2360–2375]) to the recently proposed hierarchical Tucker (HT) tensor format (Hackbusch and Kühn; see [J. Fourier Anal. Appl., 15 (2009), pp. 706–722]) and the tensor train (TT) format (Oseledets; see [SIAM J. Sci. Comput., 33 (2011), pp. 2295–2317]), which are closely related to tensor decomposition methods used in quantum physics and chemistry. In this dynamical approximation approach, the time derivative of the tensor to be approximated is projected onto the time-dependent tangent space of the approximation manifold along the solution trajectory. This approach can be used to approximate the solutions to tensor differential equations in the HT or TT format and to compute updates in optimization algorithms within these reduced tensor formats. By deriving and analyzing the tangent space projector for the manifold of HT/TT tensors of fixed rank, we obtain curvature estimates, which allow us to obtain quasi-best approximation properties for the dynamical approximation, showing that the prospects and limitations of the ansatz are similar to those of the dynamical low rank approximation for matrices. Our results are exemplified by numerical experiments.

Question: What is the dynamical approximation?


A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation. Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1 the acoustic impulse response model, 2 the spatial filter design criterion, 3 the parameter estimation algorithm, and 4 optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.

Question: What is the difference between speech enhancement and separation?


Solid state circuit breaker protection devices for DC microgrid in review. The advent of alternating current (AC) is due to its ability to transmit power over long distances from transformers, does not mark the end of utilizing direct current (DC). The development of renewable energy sources is because of high usage of DC because the generated power is directed to the main grid for conversion into electric power and the source of energy cannot tolerate high voltage. Fault detection is a major problem that arises when an excess amount of current flows in the circuit leading to the necessity of having circuit breakers. Circuit breakers are made of light materials, which creates a problem for dealing with high voltage DC applications with the protection of circuit breakers have become popularized, and different papers have outlined various studies on the effectiveness of Solid State Circuit Breakers (SSCBs) protection for a direct current microgrid. This paper reviews the benefits and shortfalls of The Wide bandgap (WBG) SSCBs and its application with photovoltaic (PV) generators.

Question: What is a microgrid?


Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO. Heterogeneous feature representations are widely used in machine learning and pattern recognition, especially for multimedia analysis. The multi-modal, often also high- dimensional , features may contain redundant and irrelevant information that can deteriorate the performance of modeling in classification. It is a challenging problem to select the informative features for a given task from the redundant and heterogeneous feature groups. In this paper, we propose a novel framework to address this problem. This framework is composed of two modules, namely, multi-modal deep neural networks and feature selection with sparse group LASSO. Given diverse groups of discriminative features, the proposed technique first converts the multi-modal data into a unified representation with different branches of the multi-modal deep neural networks. Then, through solving a sparse group LASSO problem, the feature selection component is used to derive a weight vector to indicate the importance of the feature groups. Finally, the feature groups with large weights are considered more relevant and hence are selected. We evaluate our framework on three image classification datasets. Experimental results show that the proposed approach is effective in selecting the relevant feature groups and achieves competitive classification performance as compared with several recent baseline methods.

Question: How to select the relevant feature groups?


Abacus: A Processor Family for Education. We present the Abacus processor family and its compiler framework for the MiniC language that we have developed for teaching processor architectures. Besides typical RISC instructions, Abacus also offers instructions for vector processing and thread synchronization, but it is still small enough to be discussed completely in a class. With reasonable effort, students can therefore modify given implementations of micro-architectures and code generators to deepen their understanding of the theoretical concepts. Moreover, using benchmark examples, they can explore the quantitative aspects of their optimizations. In contrast to commercial and other educational processors, we provide many micro-architectures that are based on a pure concept only rather than on a combination of concepts, and we provide code generators which contain the core ideas of some architectures.

Question: What is the Abacus processor family?


Modeling and yield estimation of SRAM sub-system for different capacities subjected to parametric variations. Process variations have become a major challenge with the advancement in CMOS technologies. The performance of memory sub-systems such as Static Random Access Memory (SRAMs) is heavily dependent on these variations. Also, the VLSI industry requires the SRAM bit cell to qualify in the order of less than 0.1ppb to achieve higher Yield (Y). This paper proposes an efficient qualitative statistical analysis and Yield estimation method of SRAM sub-system which considers deviations due to variations in process parameters in bit line differential and input offset of sense amplifier (SA) all together. The Yield of SRAM is predicted for different capacities of SRAM array by developing a statistical model of memory sub-system in 65nm bulk CMOS technology. For the sub-system with 64 bit cells, it is estimated that the probability of failure is 4.802 ∗ 10−13 in a read cycle of frequency 1GHz. Furthermore, the probability of failure for 8MB capacity is 5.035 ∗ 10−7 while for 2GB capacity it increases to 1.289 ∗ 10−5. It is also observed that as the load on one SA per column is doubled, the probability of failure of memory slice increases by 70%. The proposed technique estimates the Yield(Y) for SRAM array to be more than 99.9999.

Question: What is the probability of failure of SRAM?


Simulation of wide area measurement system with optimal phasor measurement unit location. Traditional supervisory control and data acquisition (SCADA) measurements have been providing information regarding different power system parameters for decades. These measurements are typically taken once every 4 to 10 seconds offering a steady state view of the power system behaviour. However, for monitoring and control of large grid only steady state information may not be sufficient. Phasor measurement units (PMUs) provide dynamic view of the grid. Since PMUs are costly device, optimal placement of PMUs is a thrust areas of research in power system planning. This has been already investigated in previous research findings by different optimization technique. This paper extensively corroborates a simulation model of a wide area network with optimal PMU location. The purpose is to experimentally determine the ability of PMU to observe the entire network from its optimal location. The proposed methodology is implemented and investigated on IEEE six bus system using MATLAB/Simulink.

Question: How to simulate a wide area measurement system with optimal phasor measurement unit location?


A Reinforcement Learning Approach to job-shop Scheduling. We apply reinforce merit learning methods to learn domain-specific heuristics for job shop scheduling A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule The tem poral difference algorithm is applied to tram a neural network to learn a heuristic evaluation function over s ta tes This evaluation function is used by a one-step lookahead search procedure to find good solutions to new scheduling problems We evaluate this approach on synthetic problems and on problems from a NASA space shuttle pay load processing task The evaluation function is trained on problems involving a small number of jobs and then tested on larger proble ms The TD sclied uler performs better than the best known existing algorithm for this task—Zwehen s iterative repair method based on simulated annealing The results suggest that reinforcement l<arn mg can provide a new method for constructing high-performance scheduling systems

Question: What is a reinforcement learning approach to job-shop scheduling?


Detecting social media mobile botnets using user activity correlation and artificial immune system. With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.

Question: How to detect social media mobile botnets using user activity correlation and artificial immune system.


A new ultra-wideband, ultra-short monocycle pulse generator with reduced ringing. We introduce a new ultra-wideband (UWB), ultra-short, step recovery diode monocycle pulse generator. This pulse generator uses a simple RC high-pass filter as a differentiator to generate the monocycle pulse directly. The pulse-shaping network employs a resistive circuit to achieve UWB matching and substantial removal of the pulse ringing, and rectifying and switching diodes to further suppress the ringing. An ultra-short monocycle pulse of 300-ps pulse duration, -17-dB ringing level, and good symmetry has been demonstrated. Good agreement between the measured and calculated results was achieved.

Question: What is a monocycle pulse?


Scaling Similarity Joins over Tree-Structured Data. Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-ofthe-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this paper, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. In order to scale up the join, the computed subgraphs are managed in a two-layer index. Our experimental results on real and synthetic data collections show that our approach outperforms the state-of-the-art methods by up to an order of magnitude.

Question: What is the state-of-the-art similarity join approach?


Applying machine learning to software fault-proneness prediction. The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module's fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA's Metrics Data Program data repository. 2007 Elsevier Inc. All rights reserved.

Question: What is the importance of software testing to quality assurance?


Neural network controller based on PID using an extended Kalman filter algorithm for multi-variable non-linear control system. The Proportional Integral Derivative (PID) controller is widely used in the industrial control application, which is only suitable for the single input/single output (SISO) with known-parameters of the linear system. However, many researchers have been proposed the neural network controller based on PID (NNPID) to apply for both of the single and multi-variable control system but the NNPID controller that uses the conventional gradient descent-learning algorithm has many disadvantages such as a low speed of the convergent stability, difficult to set initial values, especially, restriction of the degree of system complexity. Therefore, this paper presents an improvement of recurrent neural network controller based on PID, including a controller structure improvement and a modified extended Kalman filter (EKF) learning algorithm for weight update rule, called ENNPID controller. We apply the proposed controller to the dynamic system including inverted pendulum, and DC motor system by the MATLAB simulation. From our experimental results, it shows that the performance of the proposed controller is higher than the other PID-like controllers in terms of fast convergence and fault tolerance that are highly required.

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


Evidence of Time-Dependent Vertical Breakdown in GaN-on-Si HEMTs. This paper demonstrates and investigates the time-dependent vertical breakdown of GaN-on-Si power transistors. The study is based on electrical characterization, dc stress tests and electroluminescence measurements. We demonstrate the following original results: 1) when submitted to two-terminal (drain-to-substrate) stress, the AlGaN/GaN transistors show a time-dependent degradation process, which leads to the catastrophic failure of the devices; 2) time-to-failure follows a Weibull distribution and is exponentially dependent on stress voltage; 3) the degradation mechanism is strongly field dependent and weakly thermally activated, with an activation energy of 0.25 eV; and 4) emission microscopy suggests that vertical current flows under the whole drain area, possibly through extended defects. The catastrophic failure occurs at random positions under the drain contact. The time-dependent failure is ascribed to a percolation process activated by the high-electric field that leads to the generation of localized shunt paths between drain and substrate.

Question: What is the activation energy of the degradation mechanism?


Human-Assisted Graph Search: It's Okay to Ask Questions. We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form "Is there a target node that is reachable from the current node?". This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowdsourcing platforms such as Mechanical Turk.

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


Let a Thousand Flowers Bloom? An Early Look at Large Numbers of Software App Developers and Patterns of Innovation. It is often presumed that bringing more members on board a multi-sided platform will stimulate value creation. Here I study the thousands of software producers building applications ("apps") on leading handheld computer platforms (1999-2004). Consistent with past theory, I find a lock-step link between numbers of producers and varieties of software titles. The narrow, unchanging scope of producers and a series of other patterns are consistent with a pronounced role of specialization and heterogeneity of producers. I also find that while adding producers making different types of software stimulated investment incentives, consistent with network effects, adding producers making similar software crowdedout innovation incentives. The latter of these two effects dominates in this context. The patterns also indicate nonrandom generation and sorting of producers onto platforms, with later cohorts generating systematically less compelling software than earlier cohorts of entrants. Overall, added producers led innovation to become more dependent on population-level diversity, variation and experimentation—while drawing less on the heroic efforts and investments of any one individual innovator.

Question: How many software developers are there?


Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data. Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.

Question: What is the difference between generative adversarial networks and infinite Gaussian mixture models?


Depression duration but not age predicts hippocampal volume loss in medically healthy women with recurrent major depression.. This study takes advantage of continuing advances in the precision of magnetic resonance imaging (MRI) to quantify hippocampal volumes in a series of human subjects with a history of depression compared with controls. We sought to test the hypothesis that both age and duration of past depression would be inversely and independently correlated with hippocampal volume. A sample of 24 women ranging in age from 23 to 86 years with a history of recurrent major depression, but no medical comorbidity, and 24 case-matched controls underwent MRI scanning. Subjects with a history of depression (post-depressed) had smaller hippocampal volumes bilaterally than controls. Post-depressives also had smaller amygdala core nuclei volumes, and these volumes correlated with hippocampal volumes. In addition, post-depressives scored lower in verbal memory, a neuropsychological measure of hippocampal function, suggesting that the volume loss was related to an aspect of cognitive functioning. In contrast, there was no difference in overall brain size or general intellectual performance. Contrary to our initial hypothesis, there was no significant correlation between hippocampal volume and age in either post-depressive or control subjects, whereas there was a significant correlation with total lifetime duration of depression. This suggests that repeated stress during recurrent depressive episodes may result in cumulative hippocampal injury as reflected in volume loss.

Question: What is the relationship between depression and hippocampal volume?


Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods. The key idea is to modify the original optimization problem by adding K independent Gaussian priors (corresponding to the k-means objective) over the network parameters to achieve parameter quantization, as well as an `1 penalty to achieve pruning. Unlike many existing quantization-based methods, our method uses hard clustering assignments of network parameters, which adds minimal change or overhead to standard network training. We also demonstrate experimentally that tying neural network parameters provides less gain in generalization performance than changing network architecture and connectivity patterns entirely.

Question: How to compress neural networks?


Swarm Control of UAVs for Cooperative Hunting with DDDAS. Swarm control is a problem of increasing importance with technological advancements. Recently, governments have begun employing UAVs for reconnaissance, including swarms of drones searching for evasive targets. An agent-based simulation for dynamic cooperative cleaning is augmented with additional behaviors and implemented into a Dynamic Data-Driven Application System (DDDAS) framework for dynamic swarm control.

Question: Swarm control of UAVs for cooperative hunting with DDDAS


Pre- and post-processes for automatic colorization using a fully convolutional network. Automatic colorization is a significant task especially for Anime industry. An original trace image to be colorized contains not only outlines but also boundary contour lines of shadows and highlight areas. Unfortunately, these lines tend to decrease the consistency among all images. Thus, this paper provides a method for a cleaning pre-process of anime dataset to improve the prediction quality of a fully convolutional network, and a refinement post-process to enhance the output of the network.

Question: How to clean anime dataset?


Non-rigid image registration using self-supervised fully convolutional networks without training data. A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different spatial resolutions with deep self-supervision through typical feedforward and backpropagation computation. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.

Question: How to register images?


Personality traits and group-based information behaviour: an exploratory study. Although the importance of individual characteristics and psychological factors has been conceptualized in many models of information seeking behaviour (e.g., Case 2007; Wilson 1999) research into personality issues has only recently attracted attention in information science. This may be explained by the work by Heinström (2002; 2003; 2006a; 2006b; 2006c), but may also be explained by the emerging interest and research into affective dimensions of information behaviour (Nahl and Bilal 2007). Hitherto, the research focus in information science VOL. 14 NO. 2, JUNE, 2009

Question: What is the importance of individual characteristics and psychological factors?


Detection of ulcerative colitis severity in colonoscopy video frames. Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. The therapeutic goals of UC are to first induce and then maintain disease remission. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms associated with UC, and large variations in their patterns. To address this, we objectively measure and classify the severity of UC presented in optical colonoscopy video frames based on the image textures. To extract distinct textures, we are using a hybrid approach in which a new proposed feature based on the accumulation of pixel value differences is combined with an existing feature such as LBP (Local Binary Pattern). The experimental results show the hybrid method can achieve more than 90% overall accuracy.

Question: How to classify the severity of ulcerative colitis?


Knowledge tracing: Modeling the acquisition of procedural knowledge. This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has 'mastered' each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.

Question: What is the ideal student model?


Carpenter: finding closed patterns in long biological datasets. The growth of bioinformatics has resulted in datasets with new characteristics. These datasets typically contain a large number of columns and a small number of rows. For example, many gene expression datasets may contain 10,000-100,000 columns but only 100-1000 rows.Such datasets pose a great challenge for existing (closed) frequent pattern discovery algorithms, since they have an exponential dependence on the average row length. In this paper, we describe a new algorithm called CARPENTER that is specially designed to handle datasets having a large number of attributes and relatively small number of rows. Several experiments on real bioinformatics datasets show that CARPENTER is orders of magnitude better than previous closed pattern mining algorithms like CLOSET and CHARM.

Question: What is a closed pattern?


Calibration of a dual-laser triangulation system for assembly line completeness inspection. In controlled industrial environments, laser triangulation is an effective technique for 3D reconstruction, which is increasingly being used for quality inspection and metrology. In this paper, we propose a method for calibrating a dual laser triangulation system - consisting of a camera, two line lasers, and a linear motion platform designed to perform completeness inspection tasks on an assembly line. Calibration of such a system involves the recovery of two sets of parameters - the plane parameters of the two line lasers, and the translational direction of the linear motion platform. First, we address these two calibration problems separately. While many solutions have been given for the former problem, the latter problem has been largely ignored. Next, we highlight an issue specific to the use of multiple lasers - that small errors in the calibration parameters can lead to misalignment between the reconstructed point clouds of the different lasers. We present two different procedures that can eliminate such misalignments by imposing constraints between the two sets of calibration parameters. Our calibration method requires only the use of planar checkerboard patterns, which can be produced easily and inexpensively.

Question: How to calibrate a dual laser triangulation system for assembly line completeness inspection?


Hierarchical Model-Based Motion Estimation. This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information. The key features of the resulting framework (or family of algorithms) a,re a global model that constrains the overall structure of the motion estimated, a local rnodel that is used in the estimation process, and a coa,rse-fine refinement strategy. Four specific motion models: affine flow, planar surface flow, rigid body motion, and general optical flow, are described along with their application to specific examPles.

Question: What is a hierarchical model-based motion estimation framework?


An incremental deployment algorithm for mobile robot teams. This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots' sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another. This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated

Question: What is an incremental deployment algorithm for mobile robot teams?


An extended Doherty amplifier with high efficiency over a wide power range. An extension of the Doherty amplifier architecture which maintains high efficiency over a wide range of output power (>6 dB) is presented. This extended Doherty amplifier is demonstrated experimentally with InGaP-GaAs HBTs at a frequency of 950 MHz. P/sub 1 dB/ is measured at 27.5 dBm with PAE of 46%. PAE of at least 39% is maintained for over an output power range of 12 dB backed-off from P/sub 1 dB/. This is an improvement over the classical Doherty amplifier, where high efficiency is typically obtained up to 5-6 dB backed-off from P/sub 1 dB/. Generalized design equations for the Doherty amplifier are derived to show a careful choice of the output matching circuit and device scaling parameters can improve efficiencies at lower output power.

Question: What is the difference between a Doherty amplifier and a Doherty amplifier?


Optimum auto exposure based on high-dynamic-range histogram. This paper introduces a new approach for optimum auto exposure (AE) based on the high-dynamic-range histogram (HDH) of a scene. The exposure control is optimal in terms of recorded information of the scene. The advantages over AE based on mean values of images will be shown as well as a proof of concept implementation and results. Futhermore possible extensions to this approach are discussed. The consideration of the HDH for auto exposure enables new possibilities for auto exposure control for mutliple-slope cameras. An AE method for multiple-slope cameras is proposed which controls exposure as well as the transition curve parameters in order to record a maximum of information from the scene. It considers controllable dynamic range as well as corse quantization in bright image regions. Key–Words: Optimum Auto Exposure, High Dynamic Range (HDR), multiple-slope camera January 26, 2007

Question: What is the difference between HDR and multiple-slope cameras?


A versatile wavelet domain noise filtration technique for medical imaging. We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.

Question: What is a wavelet domain noise filtration technique for medical imaging?


Ichthyosis congenita, harlequin fetus type: a case report.. Ichthyosis is a very heterogeneous family of skin disorders with harlequin ichthyosis being the most severe genetic form. It is a rare autosomal recessive condition, characterized by dry, severely thickened skin with large plates of hyperkeratotic scale, separated by deep fissures. Infants are very susceptible to metabolic abnormalities and infections. They usually do not survive for very long, but several long term survivals have been noted. The vast majority of affected individuals are homozygous for mutations in the ABCA12 gene, which cause a deficiency of the epidermal lipid transporter, resulting in hyperkeratosis and abnormal barrier function. We report a case of a newborn with harlequin ichthyosis, born to unrelated parents, who had a favorable evolution with topical treatment and intensive care.

Question: What is harlequin ichthyosis?


Context-Dependent Sentiment Analysis in User-Generated Videos. Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability.

Question: What is the state of the art in multimodal sentiment analysis?


Hole-filling Based on Disparity Map and Inpainting for Depth- Image-Based Rendering. Due to the changes in viewpoint, holes may appear in the novel view synthesized by depth-image-based rendering (DIBR). A hole-filling method combining disparity-mapbased hole-filling and inpainting is proposed. The method first eliminates matching errors in destination image according to the disparity map. Then, holes are classified into different types according to their sizes. Finally, a disparity-map-based approach and an improved exemplar-based inpainting algorithm are used to fill different types of holes according to the type of hole. Experimental results show that the artifacts at the edge of foreground objects can be reduced in synthesized view since the matching errors are eliminated before hole-filling. In addition, the proposed method can achieve more natural and satisfactory results in filled areas in comparison with the disparity-mapbased approach and Gautier's inpainting algorithm, though it may result in higher time complexity. Keyword: Depth-image-based Rendering, Hole-filling, Holes, Disparity Map, Inpainting

Question: How to fill holes in depth-image-based rendering?


Understanding the HyperLogLog : a Near-Optimal Cardinality Estimation Algorithm. The HyperLogLog algorithm (HLL) is a method to estimate the number of distinct elements in large datasets i.e. cardinality, in a single pass, and using a very small amount of memory. The HLL algorithm is an optimization of the method presented in 2003 by Durand and Flajolet in the paper LogLog Counting of Large Cardinalities. In this report we analyze the so called cornerstone of Big Data infrastructures, give a detailed description of the algorithm and explain the mathematical intuition behind it.

Question: What is the cardinality of a large dataset?


Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter. We present a novel lane detection and tracking system using a fusion of Maximally Stable Extremal Regions (MSER) and Progressive Probabilistic Hough Transform (PPHT). First, MSER is applied to obtain a set of blobs including noisy pixels (e.g., trees, cars and traffic signs) and the candidate lane markings. A scanning refinement algorithm is then introduced to enhance the results of MSER and filter out noisy data. After that, to achieve the requirements of real-time systems, the PPHT is applied. Compared to Hough transform which returns the parameters ρ and Θ, PPHT returns two end-points of the detected line markings. To track lane markings, two kalman trackers are used to track both end-points. Several experiments are conducted in Ottawa roads to test the performance of our framework. The detection rate of the proposed system averages 92.7% and exceeds 84.9% in poor conditions.

Question: What is the lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter?


Tactile sensing for mechatronics * a state of the art survey. In this paper we examine the state of the art in tactile sensing for mechatronics[ We de_ne a tactile sensor as a device or system that can measure a given property of an object or contact event through physical contact between the sensor and the object[ We consider any property that can be measured through contact\ including the shape of an object\ texture\ temperature\ hardness\ moisture content\ etc[ A comprehensive search of the literature revealed that there was a signi_cant increase in publications on tactile sensing from 0880 onwards[ Considerable e}ort in the 0879s was spent investigating transduction techniques and developing new sensors\ whilst emphasis in more recent research has focused on experiments using tactile sensors to perform a variety of tasks[ This paper reports on progress in tactile sensing in the following areas] cutaneous sensors\ sensing _ngers\ soft materials\ industrial robot grippers\ multi_ngered hands\ probes and whiskers\ analysis of sensing devices\ haptic perception\ processing sensory data and new application areas[ We conclude that the predominant choice of transduction method is piezoelectric\ with arrays using resistive or capacitive sensing[ We found that increased emphasis on understanding tactile sensing and perception issues has opened up potential for new application areas[ The predicted growth in applications in industrial automation has not eventuated[ New applications for tactile sensing including surgery\ rehabilitation and service robotics\ and food processing automation show considerable potential and are now receiving signi_cant levels of research attention[ Þ 0887 Elsevier Science Ltd[ All rights reserved[

Question: What is the state of the art in tactile sensing for mechatronics?


A fully portable robot system for cleaning solar panels. Dust and dirt particles accumulating on PV panels decrease the solar energy reaching the cells, thereby reducing their overall power output. Hence, cleaning the PV panels is a problem of great practical engineering interest in solar PV power generation. In this paper, the problem is reviewed and methods for dust removal are discussed. A portable robotic cleaning device is developed and features a versatile platform which travels the entire length of a panel. An Arduino microcontroller is used to implement the robot's control system. Initial testing of the robot has provided favorable results and shows that such a system is viable. Future improvements on the design are discussed, especially the different methods of transporting the robot from one panel to another. In conclusion, it is found that robotic cleaning solution is practical and can help in maintaining the clean PV panel efficiency.

Question: What is the name of the robot?


From genetic privacy to open consent. Recent advances in high-throughput genomic technologies are showing concrete results in the form of an increasing number of genome-wide association studies and in the publication of comprehensive individual genome–phenome data sets. As a consequence of this flood of information the established concepts of research ethics are stretched to their limits, and issues of privacy, confidentiality and consent for research are being re-examined. Here, we show the feasibility of the co-development of scientific innovation and ethics, using the open-consent framework that was implemented in the Personal Genome Project as an example.

Question: What is the open-consent framework?


On Exploiting Dynamic Execution Patterns for Workload Offloading in Mobile Cloud Applications. Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing users' demand of mobile multimedia applications, by offloading the computational workloads from local devices to the remote cloud. Current MCC research focuses on making offloading decisions over different methods of a MCC application, but may inappropriately increase the energy consumption if having transmitted a large amount of program states over expensive wireless channels. Limited research has been done on avoiding such energy waste by exploiting the dynamic patterns of applications' run-time execution for workload offloading. In this paper, we adaptively offload the local computational workload with respect to the run-time application dynamics. Our basic idea is to formulate the dynamic executions of user applications using a semi-Markov model, and to further make offloading decisions based on probabilistic estimations of the offloading operation's energy saving. Such estimation is motivated by experimental investigations over practical smart phone applications, and then builds on analytical modeling of methods' execution times and offloading expenses. Systematic evaluations show that our scheme significantly improves the efficiency of workload offloading compared to existing schemes over various smart phone applications.

Question: What is the energy saving of workload offloading?


Ternary Residual Networks. Sub-8-bit representation of DNNs incur some noticeable loss of accuracy despite rigorous (re)training at low-precision. Such loss of accuracy essentially makes them equivalent to a much shallower counterpart, diminishing the power of being deep networks. To address this problem of accuracy drop we introduce the notion of residual networks where we add more low-precision edges to sensitive branches of the sub-8-bit network to compensate for the lost accuracy. Further, we present a perturbation theory to identify such sensitive edges. Aided by such an elegant trade-off between accuracy and model size, the 8-2 architecture (8-bit activations, ternary weights), enhanced by residual ternary edges, turns out to be sophisticated enough to achieve similar accuracy as 8-8 representation (∼ 1% drop from our FP-32 baseline), despite ∼ 1.6× reduction in model size, ∼ 26× reduction in number of multiplications , and potentially ∼ 2× inference speed up comparing to 8-8 representation, on the state-of-the-art deep network ResNet-101 pre-trained on ImageNet dataset. Moreover, depending on the varying accuracy requirements in a dynamic environment, the deployed low-precision model can be upgraded/downgraded on-the-fly by partially enabling/disabling residual connections. For example, disabling the least important residual connections in the above enhanced network, the accuracy drop is ∼ 2% (from our FP-32 baseline), despite ∼ 1.9× reduction in model size, ∼ 32× reduction in number of multiplications, and potentially ∼ 2.3× inference speed up comparing to 8-8 representation. Finally, all the ternary connections are sparse in nature, and the residual ternary conversion can be done in a resource-constraint setting without any low-precision (re)training and without accessing the data.

Question: What is the difference between ternary and binary representation?


Leakiness and creepiness in app space: perceptions of privacy and mobile app use. Mobile devices are playing an increasingly intimate role in everyday life. However, users can be surprised when informed of the data collection and distribution activities of apps they install. We report on two studies of smartphone users in western European countries, in which users were confronted with app behaviors and their reactions assessed. Users felt their personal space had been violated in "creepy" ways. Using Altman's notions of personal space and territoriality, and Nissenbaum's theory of contextual integrity, we account for these emotional reactions and suggest that they point to important underlying issues, even when users continue using apps they find creepy.

Question: What is the creepiness of an app?


Time-Varying Graphs and Social Network Analysis: Temporal Indicators and Metrics. Most instruments formalisms, concepts, and metrics for social networks analysis fail to capture their dynamics. Typical systems exhibit different scales of dynamics, ranging from the fine-grain dynamics of interactions (which recently led researchers to consider temporal versions of distance, connectivity, and related indicators), to the evolution of network properties over longer periods of time. This paper proposes a general approach to study that evolution for both atemporal and temporal indicators, based respectively on sequences of static graphs and sequences of time-varying graphs that cover successive time-windows. All the concepts and indicators, some of which are new, are expressed using a time-varying graph formalism recently proposed in [10].

Question: What is a time-varying graph?


Unsupervised Learning of Video Representations using LSTMs. We use multilayer Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed length representation. This representation is decoded using single or multiple decoder LSTMs to perform different tasks, such as reconstructing the input sequence, or predicting the future sequence. We experiment with two kinds of input sequences – patches of image pixels and high-level representations ("percepts") of video frames extracted using a pretrained convolutional net. We explore different design choices such as whether the decoder LSTMs should condition on the generated output. We analyze the outputs of the model qualitatively to see how well the model can extrapolate the learned video representation into the future and into the past. We try to visualize and interpret the learned features. We stress test the model by running it on longer time scales and on out-of-domain data. We further evaluate the representations by finetuning them for a supervised learning problem – human action recognition on the UCF-101 and HMDB-51 datasets. We show that the representations help improve classification accuracy, especially when there are only a few training examples. Even models pretrained on unrelated datasets (300 hours of YouTube videos) can help action recognition performance.

Question: How do you visualize the learned representations?


Adaptive Mobile Robot Navigation and Mapping. The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman lter to incorporate vehicle localization and environmental mapping. We describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. We introduce a metric for adaptive sensing which is de ned in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action which yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for 1) adaptive control of motion and 2) adaptive control of motion and scanning. The vehicle tends to explore selectively di erent objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.

Question: How to build a map of an unknown environment?


The critical importance of retrieval for learning.. Learning is often considered complete when a student can produce the correct answer to a question. In our research, students in one condition learned foreign language vocabulary words in the standard paradigm of repeated study-test trials. In three other conditions, once a student had correctly produced the vocabulary item, it was repeatedly studied but dropped from further testing, repeatedly tested but dropped from further study, or dropped from both study and test. Repeated studying after learning had no effect on delayed recall, but repeated testing produced a large positive effect. In addition, students' predictions of their performance were uncorrelated with actual performance. The results demonstrate the critical role of retrieval practice in consolidating learning and show that even university students seem unaware of this fact.

Question: What is the critical role of retrieval practice in consolidating learning?


Informed Recommender: Basing Recommendations on Consumer Product Reviews. Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.

Question: What is a recommender system?


Listening Comprehension over Argumentative Content. This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.

Question: What is the dataset for argumentative content?


Efficient projections onto the l1-ball for learning in high dimensions. We describe efficient algorithms for projecting a vector onto the l1-ball. We present two methods for projection. The first performs exact projection in O(n) expected time, where n is the dimension of the space. The second works on vectors k of whose elements are perturbed outside the l1-ball, projecting in O(k log(n)) time. This setting is especially useful for online learning in sparse feature spaces such as text categorization applications. We demonstrate the merits and effectiveness of our algorithms in numerous batch and online learning tasks. We show that variants of stochastic gradient projection methods augmented with our efficient projection procedures outperform interior point methods, which are considered state-of-the-art optimization techniques. We also show that in online settings gradient updates with l1 projections outperform the exponentiated gradient algorithm while obtaining models with high degrees of sparsity.

Question: What is the l1-ball?


Blockchain and Artificial Intelligence. It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that "a blockchain is decentralized and therefore no one controls it". But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.

Question: What is the difference between blockchain and smart contract?


Centroidal Voronoi Tessellations: Applications and Algorithms. A centroidal Voronoi tessellation is a Voronoi tessellation whose generating points are the centroids (centers of mass) of the corresponding Voronoi regions. We give some applications of such tessellations to problems in image compression, quadrature, finite difference methods, distribution of resources, cellular biology, statistics, and the territorial behavior of animals. We discuss methods for computing these tessellations, provide some analyses concerning both the tessellations and the methods for their determination, and, finally, present the results of some numerical experiments.

Question: What is a centroidal Voronoi tessellation?


Multi-task Deep Reinforcement Learning with PopArt. The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent's updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy with a single set of weights that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.

Question: How to balance the contribution of each task to the learning dynamics?


PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks. Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

Question: What is the difference between additive aggregation and multiplicative aggregation?


DC Motor Speed Control using PID Controllers. An Implementation of PID controllers for the speed control of a DC motor is given in this report. The motor is modeled as a first order system and its response is studied. The speed control using PI and PID control modes is explained and an implementation of the controller using OP-AMPs is given. The response of the controller to load variations is looked at.

Question: How to implement a PID controller for a DC motor?


Wideband Millimeter-Wave Substrate Integrated Waveguide Cavity-Backed Rectangular Patch Antenna. In this letter, a new type of wideband substrate integrated waveguide (SIW) cavity-backed patch antenna and array for millimeter wave (mmW) are investigated and implemented. The proposed antenna is composed of a rectangular patch with a backed SIW cavity. In order to enhance the bandwidth and radiation efficiency, the cavity is designed to resonate at its TE210 mode. Based on the proposed antenna, a 4 × 4 array is also designed. Both the proposed antenna and array are fabricated with standard printed circuit board (PCB) process, which possess the advantage of easy integration with planar circuits. The measured bandwidth (|S11| ≤ -10 dB) of the antenna element is larger than 15%, and that of the antenna array is about 8.7%. The measured peak gains are 6.5 dBi for the element and 17.8 dBi for the array, and the corresponding simulated radiation efficiencies are 83.9% and 74.9%, respectively. The proposed antenna and array are promising for millimeter-wave applications due to its merits of wide band, high efficiency, low cost, low profile, etc.

Question: What is the difference between a cavity backed patch antenna and a patch antenna?


Model-Based Stabilisation of Deep Reinforcement Learning. Though successful in high-dimensional domains, deep reinforcement learning exhibits high sample complexity and suffers from stability issues as reported by researchers and practitioners in the field. These problems hinder the application of such algorithms in real-world and safety-critical scenarios. In this paper, we take steps towards stable and efficient reinforcement learning by following a model-based approach that is known to reduce agent-environment interactions. Namely, our method augments deep Q-networks (DQNs) with model predictions for transitions, rewards, and termination flags. Having the model at hand, we then conduct a rigorous theoretical study of our algorithm and show, for the first time, convergence to a stationary point. En route, we provide a counterexample showing that 'vanilla' DQNs can diverge confirming practitioners' and researchers' experiences. Our proof is novel in its own right and can be extended to other forms of deep reinforcement learning. In particular, we believe exploiting the relation between reinforcement (with deep function approximators) and online learning can serve as a recipe for future proofs in the domain. Finally, we validate our theoretical results in 20 games from the Atari benchmark. Our results show that following the proposed model-based learning approach not only ensures convergence but leads to a reduction in sample complexity and superior performance. Introduction Model-free deep reinforcement learning methods have recently demonstrated impressive performance on a range of complex learning tasks (Hessel et al. 2018; Lillicrap et al. 2017; Jaderberg et al. 2017). Deep Q-Networks (DQNs) (Mnih et al. 2015), in particular, stand out as a versatile and effective tool for a wide range of applications. DQNs offer an end-to-end solution for many reinforcement learning problems by generalizing tabular Q-learning to highdimensional input spaces. Unfortunately, DQNs

Question: What is a model-free deep reinforcement learning?


RAN: Radical analysis networks for zero-shot learning of Chinese characters. Chinese characters have a huge set of character categories, more than 20,000 and the number is still increasing as more and more novel characters continue being created. However, the enormous characters can be decomposed into a few fundamental structural radicals, only about 500. This paper introduces the Radical Analysis Networks (RAN) that recognize Chinese characters by identifying radicals and analyzing 2D spatial structures between them. The proposed RAN first extracts visual features from Chinese character input by employing convolutional neural networks as an encoder. Then a decoder based on recurrent neural networks is employed, who aims to generate a caption of Chinese character by detecting radicals and 2D structures through a spatial attention mechanism. The manner of treating Chinese character input as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning. We test a simple implementation of RAN on experiments of recognizing printed Chinese characters with seen and unseen classes and RAN simultaneously obtains convincing performance on unseen classes and state-of-the-art performance on seen classes.

Question: What is zero-shot learning?


Neck lift my way: an update.. BACKGROUND The author updates prior descriptions of an approach to the surgical neck lift that aims for a maximum degree of control over the size, shape, and position of every anatomical feature of the neck that is negatively affecting its appearance.   METHODS A 38-year clinical experience guided the development of the operative tactics that define the strategy. Data collected from a records review of 522 consecutive neck lifts performed during the 10-year period 2004 through 2013 further inform the report. The approach has eight features: (1) nearly routine use of open submental access to all tissue layers of the central neck, including a regimen that curbed the problems that may attend an extensive tissue dissection; (2) management of lax neck skin by lateral excision using a specific postauricular incision, or by using the nonexcisional method of redistribution; (3) open lipectomy for precise removal of excess subcutaneous neck and jawline fat; (4) individualized modifications to subplatysmal fat, perihyoid fascia, and anterior digastric muscles; (5) treatment of large, ptotic, or malpositioned submandibular salivary glands by partial excision using a transcutaneous traction suture; (6) the current version of the corset platysmaplasty, which is used to treat static paramedian platysma muscle bands, and to avoid contour imperfections following subplatysmal maneuvers; (7) an approach that facilitates an isolated neck lift; and (8) durable results.   RESULTS Case examples demonstrate outcomes.   CONCLUSION Although the updated approach remains relatively complex and invasive, the author believes that the ends justify the means.

Question: What is the author's approach to the surgical neck lift?


Achieving ICS Resilience and Security through Granular Data Flow Management. Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.

Question: What is the difference between ICS and OT?


Escher sphere construction kit. M.C. Escher created a myriad of amazing planar tessellations, yet only a few three-dimensional ones such as his wooden fish ball and dodecahedral flower. We have developed an interactive program to design and manufacture "Escher Spheres" sets of tiles that can be assembled into spherical balls. The user chooses from a set of predefined symmetry groups and then deforms the boundaries of the basic domain tile; all corresponding points based on the chosen symmetry class move concurrently, instantly showing the overall result. The interior of the tile can be embellished with a bas-relief. Finally the tile is radially extruded and output as a solid model suitable for free-form fabrication.

Question: What is a bas-relief?


EnsembleHMD: Accurate Hardware Malware Detectors with Specialized Ensemble Classifiers. Hardware-based malware detectors (HMDs) are a promising new approach to defend against malware. HMDs collect low-level architectural features and use them to classify malware from normal programs. With simple hardware support, HMDs can be always on, operating as a first line of defense that prioritizes the application of more expensive and more accurate software-detector. In this paper, our goal is to increase the accuracy of HMDs, to improve detection, and reduce overhead. We use specialized detectors targeted towards a specific type of malware to improve the detection of each type. Next, we use ensemble learning techniques to improve the overall accuracy by combining detectors. We explore detectors based on logistic regression (LR) and neural networks (NN). The proposed detectors reduce the false-positive rate by more than half compared to using a single detector, while increasing their sensitivity. We develop metrics to estimate detection overhead; the proposed detectors achieve more than 16.6x overhead reduction during online detection compared to an idealized software-only detector, with an 8x improvement in relative detection time. NN detectors outperform LR detectors in accuracy, overhead (by 40%), and time-to-detection of the hardware component (by 5x). Finally, we characterize the hardware complexity by extending an open-core and synthesizing it on an FPGA platform, showing that the overhead

Question: What is the difference between a hardware and a software malware detector?


The Floyd-Warshall algorithm on graphs with negative cycles. The Floyd-Warshall algorithm is a simple and widely used algorithm to compute shortest paths between all pairs of vertices in an edge weighted directed graph. It can also be used to detect the presence of negative cycles. We will show that for this task many existing implementations of the Floyd-Warshall algorithm will fail because exponentially large numbers can appear during its execution.

Question: What is the Floyd-Warshall algorithm?


Predicting clicks: estimating the click-through rate for new ads. Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.

Question: How to predict clicks?


dbrec - Music Recommendations Using DBpedia. This paper describes the theoretical background and the implementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more than 39,000 bands and solo artists. We discuss the various challenges and lessons learnt while building it, providing relevant insights for people developing applications consuming Linked Data. Furthermore, we provide a user-centric evaluation of the system, notably by comparing it to last.fm.

Question: What is the name of the music recommendation system?


Human Mobility in a Continuum Approach. Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to any arbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxes extracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape.

Question: What is the probability to observe a trip to any arbitrary region?


Question: What is the difference between a framework and a framework of mobile transaction use?


Lattice-based motion planning for a general 2-trailer system. Motion planning for a general 2-trailer system poses a hard problem for any motion planning algorithm and previous methods have lacked any completeness or optimality guarantees. In this work we present a lattice-based motion planning framework for a general 2-trailer system that is resolution complete and resolution optimal. The solution will satisfy both differential and obstacle imposed constraints and is intended either as a part of an autonomous system or as a driver support system to automatically plan complicated maneuvers in backward and forward motion. The proposed framework relies on a precomputing step that is performed offline to generate a finite set of kinematically feasible motion primitives. These motion primitives are then used to create a regular state lattice that can be searched for a solution using standard graph-search algorithms. To make this graph-search problem tractable for real-time applications a novel parametrization of the reachable state space is proposed where each motion primitive moves the system from and to a selected set of circular equilibrium configurations. The approach is evaluated over three different scenarios and impressive real-time performance is achieved.

Question: What is a general 2-trailer system?


A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction. We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. The network is initialized with embeddings that make use of character Ngram information to better suit this task. When evaluated on common benchmark test data sets (CoNLL-2014 and JFLEG), our model substantially outperforms all prior neural approaches on this task as well as strong statistical machine translation-based systems with neural and task-specific features trained on the same data. Our analysis shows the superiority of convolutional neural networks over recurrent neural networks such as long short-term memory (LSTM) networks in capturing the local context via attention, and thereby improving the coverage in correcting grammatical errors. By ensembling multiple models, and incorporating an N-gram language model and edit features via rescoring, our novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach, both in terms of grammaticality and fluency.

Question: What is the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach?


Comparing comprehension measured by multiple-choice and open-ended questions.. This study compared the nature of text comprehension as measured by multiple-choice format and open-ended format questions. Participants read a short text while explaining preselected sentences. After reading the text, participants answered open-ended and multiple-choice versions of the same questions based on their memory of the text content. The results indicated that performance on open-ended questions was correlated with the quality of self-explanations, but performance on multiple-choice questions was correlated with the level of prior knowledge related to the text. These results suggest that open-ended and multiple-choice format questions measure different aspects of comprehension processes. The results are discussed in terms of dual process theories of text comprehension.

Question: How do open-ended and multiple-choice questions measure different aspects of comprehension processes?


Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment. This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phasereversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.

Question: How to control a game using brainwaves?