CLAMS: Bringing Quality to Data Lakes. With the increasing incentive of enterprises to ingest as much data as they can in what is commonly referred to as "data lakes", and with the recent development of multiple technologies to support this "load-first" paradigm, the new environment presents serious data management challenges. Among them, the assessment of data quality and cleaning large volumes of heterogeneous data sources become essential tasks in unveiling the value of big data. The coveted use of unstructured and semi-structured data in large volumes makes current data cleaning tools (primarily designed for relational data) not directly adoptable.  We present CLAMS, a system to discover and enforce expressive integrity constraints from large amounts of lake data with very limited schema information (e.g., represented as RDF triples). This demonstration shows how CLAMS is able to discover the constraints and the schemas they are defined on simultaneously. CLAMS also introduces a scale-out solution to efficiently detect errors in the raw data. CLAMS interacts with human experts to both validate the discovered constraints and to suggest data repairs.  CLAMS has been deployed in a real large-scale enterprise data lake and was experimented with a real data set of 1.2 billion triples. It has been able to spot multiple obscure data inconsistencies and errors early in the data processing stack, providing huge value to the enterprise.

Question: What is a data lake?


An Atomatic Fundus Image Analysis System for Clinical Diagnosis of Glaucoma. Glaucoma is a serious ocular disease and leads blindness if it couldn¡¦t be detected and treated in proper way. The diagnostic criteria for glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defect. The observation of optic nerve head, cup to disc ratio and neural rim configuration are important for early detecting glaucoma in clinical practice. However, the broad range of cup to disc ratio is difficult to identify early changes of optic nerve head, and different ethnic groups possess various features in optic nerve head structures. Hence, it is still important to develop various detection techniques to assist clinicians to diagnose glaucoma at early stages. In this study, we developed an automatic detection system which contains two major phases: the first phase performs a series modules of digital fundus retinal image analysis including vessel detection, vessel in painting, cup to disc ratio calculation, and neuro-retinal rim for ISNT rule, the second phase determines the abnormal status of retinal blood vessels from different aspect of view. In this study, the novel idea of integrating image in painting and active contour model techniques successfully assisted the correct identification of cup and disk regions. Several clinical fundus retinal images containing normal and glaucoma images were applied to the proposed system for demonstration.

Question: What is the cup to disc ratio?


Unified analysis of switched-capacitor resonant converters. A family of switched-capacitor resonant circuits using only two transistors is presented. The circuit operates under zero-current switching and, therefore, the switching loss is zero. It also offers a wide choice of voltage conversions including fractional as well as multiple and inverted voltage conversion ratios.

Question: What is a switched-capacitor resonant converter?


How transferable are features in deep neural networks?. Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task, which was expected, and (2) optimization difficulties related to splitting networks between co-adapted neurons, which was not expected. In an example network trained on ImageNet, we demonstrate that either of these two issues may dominate, depending on whether features are transferred from the bottom, middle, or top of the network. We also document that the transferability of features decreases as the distance between the base task and target task increases, but that transferring features even from distant tasks can be better than using random features. A final surprising result is that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.

Question: How transferable are features in deep neural networks?


Detecting stealth software with Strider GhostBuster. Stealth malware programs that silently infect enterprise and consumer machines are becoming a major threat to the future of the Internet. Resource hiding is a powerful stealth technique commonly used by malware to evade detection by computer users and anti-malware scanners. In this paper, we focus on a subclass of malware, termed "ghostware", which hide files, configuration settings, processes, and loaded modules from the operating system's query and enumeration application programming interfaces (APIs). Instead of targeting individual stealth implementations, we describe a systematic framework for detecting multiple types of hidden resources by leveraging the hiding behavior as a detection mechanism. Specifically, we adopt a cross-view diff-based approach to ghostware detection by comparing a high-level infected scan with a low-level clean scan and alternatively comparing an inside-the-box infected scan with an outside-the-box clean scan. We describe the design and implementation of the Strider GhostBuster tool and demonstrate its efficiency and effectiveness in detecting resources hidden by real-world malware such as rootkits, Trojans, and key-loggers.

Question: How to detect stealth software?


UWB Low-Profile Tightly Coupled Dipole Array With Integrated Balun and Edge Terminations. Tightly coupled phased arrays (TCPAs) provide UWB performance due to their strong inter-element coupling. However, in finite tightly coupled phased array realizations, mutual coupling is reduced near the array edges, causing the edge elements to become narrowband. To address this issue, a nonuniform array excitation scheme, referred to as "characteristic mode (CM) excitation," was recently proposed. A more practical alternative for designing UWB tightly coupled phased arrays is proposed here. In this paper, we present a strategy that employs uniform excitation of the central array elements and short-circuits the periphery elements. We report that at least for medium size arrays, this approach provides up to 3 dB more gain and 50% higher efficiency than typical resistive termination. This concept is demonstrated using a 7 × 7 linearly polarized dipole array, 60.96 cm × 60.96 cm (2' × 2') in size, for operation from 200 MHz-600 MHz. Feeding of the active elements is challenging due to several constraints on the feed design, including balanced to unbalanced transitions, impedance transformations, common mode suppression, compact size, low cost, etc. To address these issues we propose a novel array feed using a compact, ultrawideband balun (with 10:1 bandwidth for VSWR ). Simulated and measured data are provided for broadside and 30 scan in the H-plane.

Question: How to design a UWB array?


Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns. Local binary patterns LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well.

Question: What is a local binary pattern?


Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering. Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions. This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST). Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.

Question: Missing data imputation


Learned Step Size Quantization. We present here Learned Step Size Quantization, a method for training deep networks such that they can run at inference time using low precision integer matrix multipliers, which offer power and space advantages over high precision alternatives. The essence of our approach is to learn the step size parameter of a uniform quantizer by backpropagation of the training loss, applying a scaling factor to its learning rate, and computing its associated loss gradient by ignoring the discontinuity present in the quantizer. This quantization approach can be applied to activations or weights, using different levels of precision as needed for a given system, and requiring only a simple modification of existing training code. As demonstrated on the ImageNet dataset, our approach achieves better accuracy than all previous published methods for creating quantized networks on several ResNet network architectures at 2-, 3and 4-bits of precision.

Question: How to quantize a deep network?


A Process Mining Driven Framework for Clinical Guideline Improvement in Critical Care. This paper presents a framework for process mining in critical care. The framework uses the CRISP-DM model, extended to incorporate temporal and multidimensional aspects (CRISP-TDM), combined with the Patient Journey Modeling Architecture (PaJMa), to provide a structured approach to knowledge discovery of new condition onset pathophysiologies in physiological data streams. The approach is based on temporal abstraction and mining of physiological data streams to develop process flow mappings that can be used to update patient journeys; instantiated in critical care within clinical practice guidelines. We demonstrate the framework within the neonatal intensive care setting, where we are performing clinical research in relation to pathophysiology within physiological streams of patients diagnosed with late onset neonatal sepsis. We present an instantiation of the framework for late onset neonatal sepsis, using CRISP-TDM for the process mining model and PaJMa for the knowledge representation.

Question: What is the process mining framework for clinical guideline improvement in critical care?


Narrative Visualization: Sharing Insights into Complex Data. This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation.

Question: What is the difference between a narrative and a visualization?


Novel Control Method for Multimodule PV Microinverter With Multiple Functions. This paper presents a novel control method for multimodule photovoltaic microinverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push–pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode, i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in a standalone mode to supply uninterruptible power to the load. This paper presents a multiloop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multimodule parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250-W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.

Question: What is the purpose of this paper?


KinectFaceDB: A Kinect Database for Face Recognition. The recent success of emerging RGB-D cameras such as the Kinect sensor depicts a broad prospect of 3-D data-based computer applications. However, due to the lack of a standard testing database, it is difficult to evaluate how the face recognition technology can benefit from this up-to-date imaging sensor. In order to establish the connection between the Kinect and face recognition research, in this paper, we present the first publicly available face database (i.e., KinectFaceDB1) based on the Kinect sensor. The database consists of different data modalities (well-aligned and processed 2-D, 2.5-D, 3-D, and video-based face data) and multiple facial variations. We conducted benchmark evaluations on the proposed database using standard face recognition methods, and demonstrated the gain in performance when integrating the depth data with the RGB data via score-level fusion. We also compared the 3-D images of Kinect (from the KinectFaceDB) with the traditional high-quality 3-D scans (from the FRGC database) in the context of face biometrics, which reveals the imperative needs of the proposed database for face recognition research.

Question: What is the KinectFaceDB?


Impact of social media usage on students academic performance in Saudi Arabia. Social media is a popular method for communication amongst university students in Saudi Arabia. However excessive social media use can raise questions about whether academic performance is affected. This research explores this question by conducting a survey on university students in Saudi Arabia in regards to social media usage and their academic performance. The survey also explored which social network is the most popular amongst Saudi students, what students thought about their social media usage and factors besides social media usage which negatively affect academic performance. The survey received 108 responses and descriptive statistics including normality tests i.e. scatter plots were used to examine the relationship between the average number of hours students spent of social media a week and GPA scores of the students. The results demonstrated that there was no linear relationship between social media usage in a week and GPA score. Students highlighted that besides social media use, time management is a factor which affects students 'studies negatively. The findings of the paper can be used to propose the effective plans for improving the academic performance of the students in such a way that a balance in the leisure, information exchange and academic performance can be maintained. 2014 Elsevier Ltd. All rights reserved.

Question: What is the relationship between social media usage and academic performance?


A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws. As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority.

Question: What is the difference between neural network and deep learning?


Closed-Loop Control of a Three-Phase Neutral-Point-Clamped Inverter Using an Optimized Virtual-Vector-Based Pulsewidth Modulation. This paper presents a closed-loop control scheme for the three-level three-phase neutral-point-clamped dc-ac converter using the optimized nearest three virtual-space-vector pulsewidth modulation, which is a modulation that produces low output-voltage distortion with a significant reduction of the dc-link capacitance. A new specific loop modifying the modulating waveforms is proposed to rapidly control possible perturbations in the neutral-point voltage balance. An online estimation of the load displacement angle and load linear/nonlinear nature is introduced at no extra cost. The remaining part of the control is analogous to the control for a two-level converter with an appropriate interfacing to the selected modulation. The closed-loop control is designed for the case of a renewable-energy source connected to the ac mains, and its performance is analyzed through simulation and experiments.

Question: What is the difference between a two-level and a three-level converter?


Creating SERS hot spots on MoS(2) nanosheets with in situ grown gold nanoparticles.. Herein, a reliable surface-enhanced Raman scattering (SERS)-active substrate has been prepared by synthesizing gold nanoparticles (AuNPs)-decorated MoS2 nanocomposite. The AuNPs grew in situ on the surface of MoS2 nanosheet to form efficient SERS hot spots by a spontaneous redox reaction with tetrachloroauric acid (HAuCl4) without any reducing agent. The morphologies of MoS2 and AuNPs-decorated MoS2 nanosheet were characterized by TEM, HRTEM, and AFM. The formation of hot spots greatly depended on the ratio of MoS2 and HAuCl4. When the concentration of HAuCl4 was 2.4 mM, the as-prepared AuNPs@MoS2-3 nanocomposite exhibited a high-quality SERS activity toward probe molecule due to the generated hot spots. The spot-to-spot SERS signals showed that the relative standard deviation (RSD) in the intensity of the main Raman vibration modes (1362, 1511, and 1652 cm(-1)) of Rhodamine 6G were about 20%, which displayed good uniformity and reproducibility. The AuNPs@MoS2-based substrate was reliable, sensitive, and reproducible, which showed great potential to be an excellent SERS substrate for biological and chemical detection.

Question: How to create SERS hot spots on MoS2 nanosheets with in situ grown gold nanoparticles?


Beautiful Math, Part 2: Aesthetic Patterns Based on Fractal Tilings. A fractal tiling (f-tiling) is a tiling whose boundary is fractal. This article presents two families of rare, infinitely many f-tilings. Each f-tiling is constructed by reducing tiles by a fixed scaling factor, using a single prototile, which is a segment of a regular polygon. The authors designed invariant mappings to automatically produce appealing seamless, colored patterns from such tilings.

Question: What is a fractal tiling?


Cognitive radio: brain-empowered wireless communications. Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

Question: What is the definition of cognitive radio?


Optimizing neighborhood projection with relaxation factor for inextensible cloth simulation. In this paper, we propose a novel method for inextensible cloth simulation. Our method introduces a neighborhood projection optimized with a relaxation factor. The neighborhood projection enforces inextensibility by modifying particle positions with a Jacobi-style iteration, leading to conservation of linear and angular quasi momenta. The relaxation factor is estimated according to the corrections and constraints, and is used to scale the corrections while keeping convergence to a smaller number of iterations. Experimental results demonstrate that our method increases the simulation efficiency, and stably handles inextensible cloth even in overconstrained situations. In addition to the simulation of hanging cloth and draping cloth, the simulated umbrella demonstrates the characters of our method for this type of objects.

Question: How to optimize neighborhood projection with relaxation factor for inextensible cloth simulation?


Improving Business Process Quality through Exception Understanding, Prediction, and Prevention. Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with a high and predictable quality. In particular, it is crucial for organizations to meet the Service Level Agreements (SLAs) stipulated with the customers and to foresee as early as possible the risk of missing SLAs, in order to set the right expectations and to allow for corrective actions. In this paper we focus on a critical issue in business process quality: that of analyzing, predicting and preventing the occurrence of exceptions, i.e., of deviations from the desired or acceptable behavior. We characterize the problem and propose a solution, based on data warehousing and mining techniques. We then describe the architecture and implementation of a tool suite that enables exception analysis, prediction, and prevention. Finally, we show experimental results obtained by using the tool suite to analyze internal HP processes.

Question: How to improve business process quality?


Adaptive generic learning for face recognition from a single sample per person. Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

Question: How to solve the single sample per person problem?


Emotional Integration and Advertising Effectiveness : Case of Perfumes Advertising. This paper examines emotions in advertising, its effects and functioning. Using an interview-based experiment on 256 participants, we found that emotions perceived during an advertising exposure could play an important role in eliciting responses towards the ad and the brand. However, this process is true provided that consumers associate the perceived emotion to the exposed brand or its consumption experience. Furthermore, we have identified efficiency differences between magazine ads, depending on how they visually describe emotions. In particular, we study emotional integration in advertising, i.e. salience of emotions expressed towards the ad, the presence of core brand information and clarity of the advertising message about the hedonic attributes of consumption. Interestingly, the impact of the staging process of emotions is moderated by respondents-specific variables, including their need for emotion, tolerance for ambiguity, their need for structure and need for cognition. KeywordsEmotional Reactions ;Emotional Integration; Need for Emotion; Tolerance for Ambiguity; Need for Structure; Need for Cognition; Advertising Effectiveness.

Question: What is emotional integration in advertising?


Ensuring Data Integrity in Cloud Computing. Cloud computing provides convenient on-demand network access to a shared pool of configurable computing resources. The resources can be rapidly deployed with great efficiency and minimal management overhead. Cloud is an insecure computing platform from the view point of the cloud users, the system must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect users from malicious behaviours by enabling the validation of the computation result. In this paper, we propose a new data encoding scheme called layered interleaving, designed for time-sensitive packet recovery in the presence of bursty loss. It is high-speed data recovery scheme with minimal loss probability and using a forward error correction scheme to handle bursty loss. The proposed approach is highly efficient in recovering the singleton losses almost immediately and from bursty data losses.

Question: How to ensure data integrity in cloud computing?


Normative rational agents-A BDI approach. This paper proposes an approach on how to accommodate norms to an already existing architecture of rational agents. Starting from the famous BDI model, an extension of the BDI execution loop will be presented; it will address such issues as norm instantiation and norm internalization, with a particular emphasis on the problem of norm consistency. A proposal for the resolution of conflicts between newly occurring norms, on one side, and already existing norms or mental states, on the other side, will be described. While it is fairly difficult to imagine an evaluation for the proposed architecture, a challenging scenario inspired form the science-fiction literature will be used to give the reader an intuition of how the proposed approach will deal with situations of normative conflicts.

Question: How to deal with norms in a BDI architecture?


A combined transmembrane topology and signal peptide prediction method.. An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at

Question: What is the difference between a transmembrane protein and a signal peptide?


Haplogroup Prediction from Y-STR Values Using an Allele-Frequency Approach. A new approach to predicting the Y-chromosome haplogroup from a set of Y-STR marker values is presented and compared to other approaches. The method has been implemented in an Excel-based program, where an arbitrary number of STR markers may be input and a "goodness of fit" score for 10 haplogroups (E3a, E3b, G, I1a, I1b, I1c, J2, N3, R1a, and R1b) is returned. This method has been applied to 101 R1b haplotypes and 50 I1a haplotypes (all having 37 STR markers available), and the distribution of results is presented. In the case of I1a, the results are compared with the predictions of another method.

Question: What is the distribution of haplogroups in the canadian military?


Cloudlet-based just-in-time indexing of IoT video. As video cameras proliferate, the ability to scalably capture and search their data becomes important. Scalability is improved by performing video analytics on cloudlets at the edge of the Internet, and only shipping extracted index information and meta-data to the cloud. In this setting, we describe interactive data exploration (IDE), which refers to human-in-the-loop content-based retrospective search using predicates that may not have been part of any prior indexing. We also describe a new technique called just-in-time indexing (JITI) that improves response times in IDE.

Question: What is cloudlet-based just-in-time indexing of IoT video?


A 0.65 THz Focal-Plane Array in a Quarter-Micron CMOS Process Technology. A focal-plane array (FPA) for room-temperature detection of 0.65-THz radiation has been fully integrated in a low-cost 0.25 mum CMOS process technology. The circuit architecture is based on the principle of distributed resistive self-mixing and facilitates broadband direct detection well beyond the cutoff frequency of the technology. The 3 timesZ 5 pixel array consists of differential on-chip patch antennas, NMOS direct detectors, and integrated 43-dB voltage amplifiers. At 0.65 THz the FPA achieves a responsivity (Rv) of 80 kV/W and a noise equivalent power (NEP) of 300 pW/ radic{Hz}. Active multi-pixel imaging of postal envelopes demonstrates the FPAs potential for future cost-effective terahertz imaging solutions.

Question: What is the responsivity of a 0.65 THz focal-plane array?


Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization. Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

Question: What is the difference between style transfer and style interpolation?


Bayesian Nonparametric Methods for Learning Markov Switching Processes. In this article, we explored a Bayesian nonparametric approach to learning Markov switching processes. This framework requires one to make fewer assumptions about the underlying dynamics, and thereby allows the data to drive the complexity of the inferred model. We began by examining a Bayesian nonparametric HMM, the sticky HDPHMM, that uses a hierarchical DP prior to regularize an unbounded mode space. We then considered extensions to Markov switching processes with richer, conditionally linear dynamics, including the HDP-AR-HMM and HDP-SLDS. We concluded by considering methods for transferring knowledge among multiple related time series. We argued that a featural representation is more appropriate than a rigid global clustering, as it encourages sharing of behaviors among objects while still allowing sequence-specific variability. In this context, the beta process provides an appealing alternative to the DP.

Question: What is a Bayesian nonparametric approach to learning Markov switching processes?


Smart Cars on Smart Roads: An IEEE Intelligent Transportation Systems Society Update. To promote tighter collaboration between the IEEE Intelligent Transportation Systems Society and the pervasive computing research community, the authors introduce the ITS Society and present several pervasive computing-related research topics that ITS Society researchers are working on. This department is part of a special issue on Intelligent Transportation.

Question: What is the IEEE Intelligent Transportation Systems Society?


Slotless Bearingless Disk Drive for High-Speed and High-Purity Applications. In this paper, a bearingless drive for high-speed applications with high purity and special chemical demands is introduced. To achieve high rotational speeds with low losses, a slotless bearingless disk drive with toroidal windings is used. We present the working principle of the bearingless drive as well as a model for calculating the achievable drive torque. An advantageous winding system for independent force and torque generation is proposed, which can be realized with standard inverter technology. Additionally, the winding inductances are examined to evaluate the dynamic properties of bearing and drive. The findings are verified with simulation results and the system performance is successfully demonstrated on an experimental prototype, which runs up to 20 000 rpm and is designed for an output power of 1 kW.

Question: What is the working principle of the bearingless drive?


Comparative genomic analysis identifies structural features of CRISPR-Cas systems in Riemerella anatipestifer. Riemerella anatipestifer infection is a contagious disease that has resulted in major economic losses in the duck industry worldwide. This study attempted to characterize CRISPR-Cas systems in the disease-causing agent, Riemerella anatipestifer (R. anatipestifer). The CRISPR-Cas system provides adaptive immunity against foreign genetic elements in prokaryotes and CRISPR-cas loci extensively exist in the genomes of archaea and bacteria. However, the structure characteristics of R. anatipestifer CRISPR-Cas systems remains to be elucidated due to the limited availability of genomic data. To identify the structure and components associated with CRISPR-Cas systems in R. anatipestifer, we performed comparative genomic analysis of CRISPR-Cas systems in 25 R. anatipestifer strains using high-throughput sequencing. The results showed that most of the R. anatipestifer strains (20/25) that were analyzed have two CRISPR loci (CRISPR1 and CRISPR2). CRISPR1 was shown to be flanked on one side by cas genes, while CRISPR2 was designated as an orphan. The other analyzed strains harbored only one locus, either CRISPR1 or CRISPR2. The length and content of consensus direct repeat sequences, as well as the length of spacer sequences associated with the two loci, differed from each other. Only three cas genes (cas1, cas2 and cas9) were located upstream of CRISPR1. CRISPR1 was also shown to be flanked by a 107 bp-long putative leader sequence and a 16 nt-long anti-repeat sequence. Combined with analysis of spacer organization similarity and phylogenetic tree of the R. anatipestifer strains, CRISPR arrays can be divided into different subgroups. The diversity of spacer organization was observed in the same subgroup. In general, spacer organization in CRISPR1 was more divergent than that in CRISPR2. Additionally, only 8 % of spacers (13/153) were homologous with phage or plasmid s

Question: What is the difference between CRISPR1 and CRISPR2?


Crowdsourcing based spatial mining of urban emergency events using social media. With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of emergency management systems through modern data processing techniques. The past decade has witnessed the tremendous technical advances in Sensor Networks, Internet/Web of Things, Cloud Computing, Mobile/Embedded Computing, Spatial/Temporal Data Processing, and Big Data, and these technologies have provided new opportunities and solutions to emergency management. GIS models and simulation capabilities are used to exercise response and recovery plans during non-disaster times. They help the decision-makers understand near real-time possibilities during an event. In this paper, a crowdsourcing based model for mining spatial information of urban emergency events is introduced. Firstly, basic definitions of the proposed method are given. Secondly, positive samples are selected to mine the spatial information of urban emergency events. Thirdly, location and GIS information are extracted from positive samples. At last, the real spatial information is determined based on address and GIS information. At last, a case study on an urban emergency event is given.

Question: What is crowdsourcing?


Brain of the African elephant (Loxodonta africana): neuroanatomy from magnetic resonance images.. We acquired magnetic resonance images of the brain of an adult African elephant, Loxodonta africana, in the axial and parasagittal planes and produced anatomically labeled images. We quantified the volume of the whole brain (3,886.7 cm3) and of the neocortical and cerebellar gray and white matter. The white matter-to-gray matter ratio in the elephant neocortex and cerebellum is in keeping with that expected for a brain of this size. The ratio of neocortical gray matter volume to corpus callosum cross-sectional area is similar in the elephant and human brains (108 and 93.7, respectively), emphasizing the difference between terrestrial mammals and cetaceans, which have a very small corpus callosum relative to the volume of neocortical gray matter (ratio of 181-287 in our sample). Finally, the elephant has an unusually large and convoluted hippocampus compared to primates and especially to cetaceans. This may be related to the extremely long social and chemical memory of elephants.

Question: What is the volume of the brain of the African elephant?


The language of mental health problems in social media. Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.

Question: What is the language of mental health problems in social media?


Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems. We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such <i>independent </i> agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.

Question: What is reinforcement learning?


Abstraction and reformulation in artificial intelligence.. This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.

Question: What is the difference between abstraction and generalization?


Dual broadband high gain SIW slot array antenna. A novel design of dual broadband high gain substrate integrated waveguide (SIW) cavity backed slot array antenna is described. The SIW cavity is design in dominant mode TE10. The proposed antenna is design by two pair of λg/2-resonators cavity backed slot on broad wall of single layer substrate integrated waveguide. To place two pair unequal slot length in stair nature on broad-wall of SIW cavity are responsible to broadening the bandwidth at two different frequency band and also due to arraying nature of slot enhanced the gain. The proposed antenna have Lower frequency bandwidth 4.4% (9.58 GHz–10.02 GHz) and higher frequency bandwidth is 9.49% (10.84 GHz–11.92 GHz) with 10 dB gain at lower band and 9.4 dB gain at higher band. The proposed antenna has high gain, directive radiation field with high FTBR.

Question: What is a slot array antenna?


p-Cycle-based node failure protection for survivable virtual network embedding. Network Virtualization offers to Physical Infrastructure Provider (PIP) the possibility to smoothly roll out multiple isolated networks on top of his infrastructure. A major challenge in this respect is the embedding problem which deals with the mapping of Virtual Networks (VNs) resources on PIP network. In literature, a number of research proposals have been focused on providing heuristic approaches to solve this NP-hard problem while most often assuming that PIP infrastructure is operational at all time. In virtualization environment a single physical node failure can result in single/multi logical link failure(s) as it effects all VNs with a mapping that spans over. Setup a dedicated backup for each VN embedding, i.e., no sharing, is inefficient in terms of resource usage. To address these concerns, we propose in this work an approach for VN mapping with combined physical node and multiple logical links protection mechanism (VNM-CNLP) that: (a) uses a large scale optimization technique, developed in our previous work, to calculate in a first stage a cost-efficient VN mapping while minimizing the effects of a single node failure in VN layer, and (b) proposes in a second stage link p-Cycle based protection techniques that minimize the backup resources while providing a full VN protection scheme against a single physical node failure and a multiple logical links failure. Our simulations show a clear advantage of VNM-CNLP approach over benchmark in terms of VN backup cost and resources utilization.

Question: What is the difference between a single node failure and a multiple logical links failure?


QUOTA: The Quantile Option Architecture for Reinforcement Learning. In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL). In QUOTA, decision making is based on quantiles of a value distribution, not only the mean. QUOTA provides a new dimension for exploration via making use of both optimism and pessimism of a value distribution. We demonstrate the performance advantage of QUOTA in both challenging video games and physical robot simulators.

Question: What is the quantile option architecture?


Knowledge Engineering: Principles and Methods. This paper gives an overview about the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in the last years we describe three modeling frameworks: CommonKADS, MIKE, and PROTÉGÉ-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods, and ontologies. We conclude with outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

Question: What is the relationship between knowledge engineering and software engineering?


Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras. An important aspect of collision avoidance and driver assistance systems, as well as autonomous vehicles, is the tracking of vehicle taillights and the detection of alert signals (turns and brakes). In this paper, we present the design and implementation of a robust and computationally lightweight algorithm for a real-time vision system, capable of detecting and tracking vehicle taillights, recognizing common alert signals using a vehicle-mounted embedded smart camera, and counting the cars passing on both sides of the vehicle. The system is low-power and processes scenes entirely on the microprocessor of an embedded smart camera. In contrast to most existing work that addresses either daytime or nighttime detection, the presented system provides the ability to track vehicle taillights and detect alert signals regardless of lighting conditions. The mobile vision system has been tested in actual traffic scenes and the results obtained demonstrate the performance and the lightweight nature of the algorithm.

Question: How to detect and track vehicle taillights?


Computer generated animation of faces. This paper describes the representation, animation and data collection techniques that have been used to produce "realistic" computer generated half-tone animated sequences of a human face changing expression. It was determined that approximating the surface of a face with a polygonal skin containing approximately 250 polygons defined by about 400 vertices is sufficient to achieve a realistic face. Animation was accomplished using a cosine interpolation scheme to fill in the intermediate frames between expressions. This approach is good enough to produce realistic facial motion. The three-dimensional data used to describe the expressions of the face was obtained photogrammetrically using pairs of photographs.

Question: How realistic is the animation?


Weed Detection Using Fractal-Based Low Cost Commodity Hardware Raspberry Pi. Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.

Question: What is the fractal dimension of a weed?


IEEE 802 . 11 ah : A Long Range 802 . 11 WLAN at Sub 1 GHz. IEEE 802.11ah is an emerging Wireless LAN (WLAN) standard that defines a WLAN system operating at sub 1 GHz license-exempt bands. Thanks to the favorable propagation characteristics of the low frequency spectra, 802.11ah can provide much improved transmission range compared with the conventional 802.11 WLANs operating at 2.4 GHz and 5 GHz bands. 802.11ah can be used for various purposes including large scale sensor networks, extended range hotspot, and outdoor Wi-Fi for cellular traffic offloading, whereas the available bandwidth is relatively narrow. In this paper, we give a technical overview of 802.11ah Physical (PHY) layer and Medium Access Control (MAC) layer. For the 802.11ah PHY, which is designed based on the down-clocked operation of IEEE 802.11ac's PHY layer, we describe its channelization and transmission modes. Besides, 802.11ah MAC layer has adopted some enhancements to fulfill the expected system requirements. These enhancements include the improvement of power saving features, support of large number of stations, efficient medium access mechanisms and throughput enhancements by greater compactness of various frame formats. Through the numerical analysis, we evaluate the transmission range for indoor and outdoor environments and the theoretical throughput with newly defined channel access mechanisms.

Question: What is the transmission range of 802.11ah?


Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers. The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is proposed. In highway scenarios, our approach is highly suitable to determine the correct number of all existing lanes on the road. Additionally, the output of the laserscannner based lane detection is fused with a production-available vision based system. It is shown that both sensor systems perfectly complement each other to increase the robustness of a lane tracking system. The achieved accuracy of the fusion system, the laserscannner and video based system is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control applications.

Question: How many lanes are on the road?


A Double-Tail Latch-Type Voltage Sense Amplifier with 18ps Setup+Hold Time. A latch-type voltage sense amplifier in 90nm CMOS is designed with a separated input and cross-coupled stage. This separation enables fast operation over a wide common-mode and supply voltage range. With a 1-sigma offset of 8mV, the circuit consumes 92fJ/decision with a 1.2V supply. It has an input equivalent noise of 1.5mV and requires 18ps setup-plus-hold time

Question: What is a latch-type voltage sense amplifier?


Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior.

Question: How to dehaze a single image?


Metacognitive Control of the Spacing of Study Repetitions. Rememberers play an active role in learning, not only by committing material more or less faithfully to memory, but also by selecting judicious study strategies (or not). In three experiments, subjects chose whether to mass or space the second presentation of to-be-learned paired-associate terms that were either normatively difficult or easy to remember, under the constraint that subjects needed to space exactly half of the items (and mass the other half). In contrast with recent findings that implemented no such constraint (Son, 2004), subjects chose to space more of the difficult pairs (in Experiments 1 and 2). Reduction in exposure time eliminated but did not reverse this effect (Experiment 3). Subjects who spaced more of the difficult pairs were more likely to exhibit superior memory performance, but, because subjects who made spacing selections that had no effect on the actual scheduling of items also showed this effect (Experiment 2), that enhancement in performance is more likely to reflect subject selection than strategy efficacy. Overall, these results suggest that choice constraints strongly elicit a discrepancy-reduction approach (Dunlosky & Hertzog, 1998) to strategic decisionmaking, but that reduced study time can eliminate this effect. Metacognitive control of spacing 3 The question of how people use their metacognitive knowledge to regulate their behaviors has been of much interest in recent years, particularly with regard to the implementation of study strategies. Metacognition plays an integral role in tasks such as selfdirected learning (Koriat & Goldsmith, 1996; Nelson & Narens, 1990), and understanding the means by which metacognitions guide learning processes is essential to facilitate and optimize the learning process itself (see Bjork, 1994). This article pursues that question by examining the strategies that subjects employ in the scheduling of learning events. Recent evidence reveale

Question: How do you regulate your study?


EVE: explainable vector based embedding technique using Wikipedia. We present an unsupervised explainable vector embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a concept using human-readable labels, thereby it is readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed model, we consider its usefulness in three fundamental tasks: 1) intruder detection—to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster—to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first—to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.

Question: What is the unsupervised explainable vector embedding technique?


A LSTM-based method for stock returns prediction: A case study of China stock market. The presented paper modeled and predicted China stock returns using LSTM. The historical data of China stock market were transformed into 30-days-long sequences with 10 learning features and 3-day earning rate labeling. The model was fitted by training on 900000 sequences and tested using the other 311361 sequences. Compared with random prediction method, our LSTM model improved the accuracy of stock returns prediction from 14.3% to 27.2%. The efforts demonstrated the power of LSTM in stock market prediction in China, which is mechanical yet much more unpredictable.

Question: What is the power of LSTM in stock market prediction?


Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation. Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can translate between multiple languages and are also capable of performing zero-shot translation. However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zeroshot multilinguality in other NLP tasks. In this paper, we demonstrate a simple framework, a multilingual EncoderClassifier, for cross-lingual transfer learning by reusing the encoder from a multilingual NMT system and stitching it with a task-specific classifier component. Our proposed model achieves significant improvements in the English setup on three benchmark tasks Amazon Reviews, SST and SNLI. Further, our system can perform classification in a new language for which no classification data was seen during training, showing that zero-shot classification is possible and remarkably competitive. In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks.

Question: What is the zero-shot cross-lingual classification?


Iterative localization refinement in convolutional neural networks for improved object detection. Accurate region proposals are of importance to facilitate object localization in the existing convolutional neural network (CNN)-based object detection methods. This paper presents a novel iterative localization refinement (ILR) method which, undertaken at a mid-layer of a CNN architecture, iteratively refines region proposals in order to match as much ground-truth as possible. The search for the desired bounding box in each iteration is first formulated as a statistical hypothesis testing problem and then solved by a divide-and-conquer paradigm. The proposed ILR is not only data-driven, free of learning, but also compatible with a variety of CNNs. Furthermore, to reduce complexity, an approximate variant based on a refined sampling strategy using linear interpolation is addressed. Simulations show that the proposed method improves the main state-of-the-art works on the PASCAL VOC 2007 dataset.

Question: What is iterative localization refinement?


Big Data and Transformational Government. The big data phenomenon is growing throughout private and public sector domains. Profit motives make it urgent for companies in the private sector to learn how to leverage big data. However, in the public sector, government services could also be greatly improved through the use of big data. Here, the authors describe some drivers, barriers, and best practices affecting the use of big data and associated analytics in the government domain. They present a model that illustrates how big data can result in transformational government through increased efficiency and effectiveness in the delivery of services. Their empirical basis for this model uses a case vignette from the US Department of Veterans Affairs, while the theoretical basis is a balanced view of big data that takes into account the continuous growth and use of such data. This article is part of a special issue on big data and business analytics.

Question: What is the big data phenomenon?


The Information Systems Identity Crisis: Focusing on High-Visibility and High-Impact Research. This paper presents an alternative view of the Information Systems identity crisis described recently by Benbasat and Zmud (2003). We agree with many of their observations, but we are concerned with their prescription for IS research. We critique their discussion of errors of inclusion and exclusion in IS research and highlight the potential misinterpretations that are possible from a literal Ron Weber was the accepting senior editor for this paper. Iris Vessey and Rudy Hirschheim served as reviewers. reading of their comments. Our conclusion is that following Benbasat and Zmud's nomological net will result in a micro focus for IS research. The results of such a focus are potentially dangerous for the field. They could result in the elimination of IS from many academic programs. We present an alternative set of heuristics that can be used to assess what lies within the domain of IS scholarship. We argue that the IS community has a powerful story to tell about the transformational impact of information technology. We believe that a significant portion of our research should be macro studies of the impact of IT. It is important for academic colleagues, deans, and managers to understand the transformational power of the technology. As IS researchers with deep knowledge of the underlying artifact, we are best positioned to do such research.

Question: What is the difference between a macro and micro study?


Dynamic visual attention: searching for coding length increments. A visual attention system should respond placidly when comm n stimuli are presented, while at the same time keep alert to anomalous visual inputs. In this paper, a dynamic visual attention model based on the rarity of featu r s is proposed. We introduce the Incremental Coding Length (ICL) to measure th e perspective entropy gain of each feature. The objective of our model is to ma xi ize the entropy of the sampled visual features. In order to optimize energy c onsumption, the limit amount of energy of the system is re-distributed among st features according to their Incremental Coding Length. By selecting featur es with large coding length increments, the computational system can achieve at tention selectivity in both static and dynamic scenes. We demonstrate that the prop osed model achieves superior accuracy in comparison to mainstream approaches i n stat c saliency map generation. Moreover, we also show that our model captures s everal less-reported dynamic visual search behaviors, such as attentional swing and inhibition of return.

Question: How to measure the coding length of a feature?


A lifelong learning perspective for mobile robot control. Abstmct-Designing robots that learn by themselves to perform complex real-world tasks is a still-open challenge for the field of Robotics and Artificial Intelligence. In this paper we present the robot learning problem as a lifelong problem, in which a robot faces a collection of tasks over its entire lifetime. Such a scenario provides the opportunity to gather general-purpose knowledge that transfers across tasks. We illustrate a particular learning mechanism, explanation-based neural network learning, that transfers knowledge between related tasks via neural network action models. The learning approach is illustrated using a mobile robot, equipped with visual, ultrasonic and laser sensors. In less than 10 minutes operation time, the robot is able to learn to navigate to a marked target object in a natural office environment.

Question: How to learn to navigate in a natural office environment?


Facebook C2C social commerce: A study of online impulse buying. Facebook users are increasingly using the site to conduct commercial activities, by posting advertisements in groups and then buying or selling items from each other. This type of group is called as a C2C Facebook "buy and sell" group in the current work. Drawing from Latent State Trait theory, Heuristic Information Processing, and Observational Learning, we conducted an online field experiment to empirically investigate the effect of the information quality of the advertisement, the trait of the impulsiveness, and the number of "Likes" it receives on consumers' urge to buy impulsively. The findings and implications of our study are discussed in the

Question: What is the effect of information quality on consumers' urge to buy impulsively?


A Temporal Attentional Model for Rumor Stance Classification. Rumor stance classification is the task of determining the stance towards a rumor in text. This is the first step in effective rumor tracking on social media which is an increasingly important task. In this work, we analyze Twitter users' stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor. We propose a deep attentional CNN-LSTM approach, which takes the sequence of tweets in a thread of conversation as the input. We use neighboring tweets in the timeline as context vectors to capture the temporal dynamism in users' stance evolution. In addition, we use extra features such as friendship, to leverage useful relational features that are readily available in social media. Our model achieves the state-of-the-art results on rumor stance classification on a recent SemEval dataset, improving accuracy and F1 score by 3.6% and 4.2% respectively.

Question: What is the state-of-the-art in rumor stance classification?


Evidence for physiotherapy practice: a survey of the Physiotherapy Evidence Database (PEDro).. Evidence-based practice involves the use of evidence from systematic reviews and randomised controlled trials, but the extent of this evidence in physiotherapy has not previously been surveyed. The aim of this survey is to describe the quantity and quality of randomised controlled trials and the quantity of systematic reviews relevant to physiotherapy. The Physiotherapy Evidence Database (PEDro) was searched. The quality of trials was assessed with the PEDro scale. The search identified a total of 2,376 randomised controlled trials and 332 systematic reviews. The first trial was published in 1955 and the first review was published in 1982. Since that time, the number of trials and reviews has grown exponentially. The mean PEDro quality score has increased from 2.8 in trials published between 1955 and 1959 to 5.0 for trials published between 1995 and 1999. There is a substantial body of evidence about the effects of physiotherapy. However, there remains scope for improvements in the quality of the conduct and reporting of clinical trials.

Question: What is the quality of the evidence for physiotherapy?


Conditional Dynamic Mutual Information-Based Feature Selection. With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset can not exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on 1194 H. Liu, Y. Mo, J. Zhao conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other wellestablished feature selection algorithms in most cases.

Question: What is the difference between conditional mutual information and mutual information?


Designing and Packaging Wide-Band PAs: Wideband PA and Packaging, History, and Recent Advances: Part 1. In current applications such as communication, aerospace and defense, electronic warfare (EW), electromagnetic compatibility (EMC), and sensing, among others, there is an ever-growing demand for more linear power with increasingly greater bandwidth and efficiency. Critical for these applications are the design and packaging of wide-band, high-power amplifiers that are both compact in size and low in cost [1]. Most such applications, including EW, radar, and EMC testers, require high 1-dB compression point (P1dB) power with good linearity across a wide band (multi-octave to decade bandwidth) [2]. In addition to linear power, wide bandwidth is essential for high-data-rate communication and high resolution in radar and active imagers [3]. In modern electronics equipment such as automated vehicles, rapidly increasing complexity imposes strict EMC regulations for human safety and security [4]. This often requires challenging specifications for the power amplifier (PA), such as very high P1dB power [to kilowatt, continuous wave (CW)] across approximately a decade bandwidth with high linearity, reliability, and long life, even for 100% load mismatch [5].

Question: What is the difference between a wideband PA and a wideband PA?


Underwater electric field measurement and analysis. The University of Idaho (UI) is developing electromagnetic field sensor systems that are attachable to autonomous underwater vehicles with the intent of taking survey measurements in underwater ocean environments. This paper presents the testing of these sensors and compares measurements to predictions. Testing was conducted off the coast of Florida with a moving artificial electric field source and an electric field sensor equipped AUV. At the closest pass, the peak value of the AUV-acquired electric field was consistent with the predicted field of the artificial source when the accuracy of AUV position was known to within ∼1 m.

Question: What is an underwater electric field?


Anatomy of the Facial Danger Zones: Maximizing Safety during Soft-Tissue Filler Injections.. With limited downtime and immediate results, facial filler injections are becoming an ever more popular alternative to surgical rejuvenation of the face. The results, and the complications, can be impressive. To maximize safety during injections, the authors have outlined general injection principles followed by pertinent anatomy within six different facial danger zones. Bearing in mind the depth and the location of the vasculature within each zone, practitioners can tailor their injection techniques to prevent vessel injury and avoid cannulation.

Question: What is the anatomy of the facial danger zones?


Analysis of the Electromagnetic Signature of Reinforced Concrete Structures for Nondestructive Evaluation of Corrosion Damage. This paper presents a nondestructive corrosion damage detection method for reinforced concrete structures based on the analysis of the electromagnetic signature of the steel rebar corrosion. The signature of the corrosion on the scattered field upon microwave illumination is first numerically analyzed. First-order quality factor parameters, the energy and the mean propagation delay, are proposed to quantify the corrosion amount in the structure. To validate the model, low-profile ultra-wide-band antennas (3-12 GHz) are fabricated and measured. Measurements on 12 reinforced concrete samples with induced corrosion are performed, using three different antenna setups. The experiments demonstrate a good correlation between an increase in the corrosion amount with a decrease in the energy and an increase in the time delay of the propagated signal.

Question: What is the electromagnetic signature of reinforced concrete?


Pose estimation of anime/manga characters: a case for synthetic data. 2D articulated pose estimation is the task of locating the body joint positions of a human figure in an image. A pose estimator that works on anime/manga images could be an important component of an automatic system to create 3D animation from existing manga or anime, which could significantly the lower cost of media production. To create an accurate pose estimator, however, a sizable and high-quality dataset is needed, and such a dataset can be expensive to create.  To alleviate data scarcity, we propose using a database of 3D character models and poses to generate synthetic training data for 2D pose estimators of anime/manga characters based on convolutional neural networks (CNN). We demonstrate that a high-performing estimator can be obtained by pretraining a network on a large synthetic dataset and then fine-tuning it on a small dataset of drawings. We also show that the approach yields a pose estimator competitive with many previous works when applied to a photograph-based dataset, establishing our synthetic data's usefulness beyond the intended domain.

Question: How to create a dataset of anime/manga characters?


Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?. The growth of detection datasets and the multiple directions of object detection research provide both an unprecedented need and a great opportunity for a thorough evaluation of the current state of the field of categorical object detection. In this paper we strive to answer two key questions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors? Inspired by the recent work of Hoiem et al. on the standard PASCAL VOC detection dataset, we perform a large-scale study on the Image Net Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quantitatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent test bed for understanding the performance of detectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of image-level and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors.

Question: What is the current state of the art in object detection?


Locality-sensitive hashing scheme based on dynamic collision counting. Locality-Sensitive Hashing (LSH) and its variants are well-known methods for solving the c-approximate NN Search problem in high-dimensional space. Traditionally, several LSH functions are concatenated to form a "static" compound hash function for building a hash table. In this paper, we propose to use a base of m single LSH functions to construct "dynamic" compound hash functions, and define a new LSH scheme called Collision Counting LSH (C2LSH). If the number of LSH functions under which a data object o collides with a query object q is greater than a pre-specified collision threhold l, then o can be regarded as a good candidate of c-approximate NN of q. This is the basic idea of C2LSH.  Our theoretical studies show that, by appropriately choosing the size of LSH function base m and the collision threshold l, C2LSH can have a guarantee on query quality. Notably, the parameter m is not affected by dimensionality of data objects, which makes C2LSH especially good for high dimensional NN search. The experimental studies based on synthetic datasets and four real datasets have shown that C2LSH outperforms the state of the art method LSB-forest in high dimensional space.

Question: What is the c-approximate nearest neighbor search?


USARSim: a robot simulator for research and education. This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.

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


A Business Intelligence System. ระบบ Business Intelligence เป็นซอฟแวร์ที่เข้าไปช่วยอ านวยความสะดวกในด้านการวเิคราะห์ข้อมลู และการตัดสินใจใน ข้อมูลทางธุรกิจทั้งหมดของในหนว่ยงาน ซึ่งช่วยให้ผู้ใช้งานระบบงานในองค์กรท าการตัดสินใจทางธุรกิจที่ดียิ่งข้ึน มีฟังก์ช่ันการใช้ งานต่างๆครบถ้วนไม่ว่าจะเป็น Data Integration, Reporting, ad-hoc Query, Analysis Dashboard, Data Mining ทั้งนี้เพื่อลดการท างานท่ี ซ้ าซ้อน ลดความผิดพลาดและความขัดแย้งท่ีอาจจะเกิดขึ้นเนื่องจากการสื่อสารระหว่างหน่วยงานภายในกันเอง หรือกับหน่วยงาน ภายนอก

Question: What is the difference between Business Intelligence and Business Intelligence System?


SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data. We present SWELLSHARK, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SWELLSHARK achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SWELLSHARK achieved within 5.1% of a crowdsourced annotation approach – which originally utilized 20 teams over the course of several weeks – in 24 hours.

Question: What is a generative model for biomedical named entity recognition?


Distributed large-scale natural graph factorization. Natural graphs, such as social networks, email graphs, or instant messaging patterns, have become pervasive through the internet. These graphs are massive, often containing hundreds of millions of nodes and billions of edges. While some theoretical models have been proposed to study such graphs, their analysis is still difficult due to the scale and nature of the data.  We propose a framework for large-scale graph decomposition and inference. To resolve the scale, our framework is distributed so that the data are partitioned over a shared-nothing set of machines. We propose a novel factorization technique that relies on partitioning a graph so as to minimize the number of neighboring vertices rather than edges across partitions. Our decomposition is based on a streaming algorithm. It is network-aware as it adapts to the network topology of the underlying computational hardware. We use local copies of the variables and an efficient asynchronous communication protocol to synchronize the replicated values in order to perform most of the computation without having to incur the cost of network communication. On a graph of 200 million vertices and 10 billion edges, derived from an email communication network, our algorithm retains convergence properties while allowing for almost linear scalability in the number of computers.

Question: What is the largest graph that can be decomposed?


Improving Performances of MSER Features in Matching and Retrieval Tasks. MSER features are redefined to improve their performances in matching and retrieval tasks. The proposed SIMSER features (i.e. scale-insensitive MSERs) are the extremal regions which are maximally stable not only under the threshold changes (like MSERs) but, additionally, under image rescaling (smoothing). Theoretical advantages of such a modification are discussed. It is also preliminarily verified experimentally that such a modification preserves the fundamental properties of MSERs, i.e. the average numbers of features, repeatability, and computational complexity (which is only multiplicatively increased by the number of scales used), while performances (measured by typical CBVIR metrics) can be significantly improved. In particular, results on benchmark datasets indicate significant increments in recall values, both for descriptor-based matching and word-based matching. In general, SIMSERs seem particularly suitable for a usage with large visual vocabularies, e.g. they can be prospectively applied to improve quality of BoW pre-retrieval operations in large-scale databases.

Question: What is the average number of features?


Compressive Sensing Image Sensors-Hardware Implementation. The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.

Question: What is the difference between a compressive sensing camera and a conventional camera?


Child murder by parents: a psychiatric review of filicide..

Question: What is filicide?


Rotation Invariant Multi-View Color Face Detection Based on Skin Color and Adaboost Algorithm. As the training of Adaboost is complicated or does not work well in the multi-view face detection with large plane-rotated angle, this paper proposes a rotation invariant multi-view color face detection method combining skin color segmentation and multi-view Adaboost algorithm. First the possible face region is fast detected by skin color table, and the skin-color background adhesion of face is separated by color clustering. After region merging, the candidate face region is received. Then the face direction in plane is calculated by K-L transform, and finally the candidate face region corrected by rotating is scanned by multi-view Adaboost classifier to locate the face accurately. The experiments show that the method can effectively detect the plane large-angle multi-view face image which the conventional Adaboost can not do. It can be effectively applied to the cases of multi-view and multi-face image with complex background.

Question: How to detect the plane large-angle multi-view face image which the conventional Adaboost can not do?


New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots. Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.

Question: What is the cost of a fleet of robots?


Personalization and Customization Technologies. The article presents a survey of personalization and customization technologies. It starts with an analysis of user profiling and explains how user profiles can be used for generating personalized products and services. The article explains some standard personalization techniques such as rule-based filtering, collaborative filtering, and content-based filtering. Web usage mining is also explained and it is shown how data mining can be used in learning customer behavior and for providing personalized content. Several applications are discussed such as adaptive websites, recommender systems, adaptive web stores, and intelligent agents. The article concludes by summarizing current trends.

Question: What is a recommender system?


Predicting the Price of Used Cars using Machine Learning Techniques. In this paper, we investigate the application of supervised machine learning techniques to predict the price of used cars in Mauritius. The predictions are based on historical data collected from daily newspapers. Different techniques like multiple linear regression analysis, k-nearest neighbours, naïve bayes and decision trees have been used to make the predictions. The predictions are then evaluated and compared in order to find those which provide the best performances. A seemingly easy problem turned out to be indeed very difficult to resolve with high accuracy. All the four methods provided comparable performance. In the future, we intend to use more sophisticated algorithms to make the predictions. Keywords-component; formatting; style; styling; insert (key words)

Question: What is the price of used cars in Mauritius?


Proofs of Space-Time and Rational Proofs of Storage. We introduce a new cryptographic primitive: Proofs of SpaceTime (PoSTs) and construct a practical protocol for implementing these proofs. A PoST allows a prover to convince a verifier that she spent a "spacetime" resource (storing data—space—over a period of time). Formally, we define the PoST resource as a linear tradeoff between CPU work and space-time (under reasonable cost assumptions, a rational user will prefer to use the lower-cost space-time resource over CPU work). Compared to a proof-of-work, a PoST requires less energy use, as the "difficulty" can be increased by extending the time period over which data is stored without increasing computation costs. Our definition is very similar to "Proofs of Space" [ePrint 2013/796, 2013/805] but, unlike the previous definitions, takes into account amortization attacks and storage duration. Moreover, our protocol uses a very different (and simpler) technique, making use of the fact that we explicitly allow a space-time tradeoff.

Question: What is the difference between a proof of space and a proof of space-time?


Infants' Metaphysics: The Case of Numerical Identity. Adults conceptualize the world in terms of enduring physical objects. Sortal concepts provide conditions of individuation (establishing the boundaries of objects) and numerical identity (establishing whether an object is the same one as one encountered at some other time). In the adult conceptual system, there are two roughly hierarchical levels of object sortals. Most general is the sortal bounded physical object itself, for which spatiotemporal properties provide the criteria for individuation and identity. More specific sortals, such as dog or car, rely on additional types of properties to provide criteria for individuation and identity. We conjecture that young infants might represent only the general sortal, object, and construct more specific sortals later (the Object-first Hypothesis). This is closely related to Bower's (1974) conjecture that infants use spatiotemporal information to trace identity before they use property information. Five studies using the visual habituation paradigm were conducted to address the Object-first Hypothesis. In these studies, 10-month-old infants were able to use spatiotemporal information but failed to use property/kind information to set up representations of numerically distinct individuals, thus providing empirical evidence for the Object-first Hypothesis. Finally, infants succeed at object individuation in terms of more specific sortals by 12 months. The relation between success at our task and early noun comprehension is discussed.

Question: What is the relation between early noun comprehension and success at our task?


Towards network-wide QoE fairness using openflow-assisted adaptive video streaming. Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.

Question: How to fairly maximise the QoE of multiple competing clients in a shared network environment?


Predicting Rare Events In Temporal Domains. Temporal data mining aims at finding patterns in historical data. Our work proposes an approach to extract temporal patterns from data to predict the occurrence of target events, such as computer attacks on host networks, or fraudulent transactions in financial institutions. Our problem formulation exhibits two major challenges: 1) we assume events being characterized by categorical features and displaying uneven inter-arrival times; such an assumption falls outside the scope of classical time-series analysis, 2) we assume target events are highly infrequent; predictive techniques must deal with the class-imbalance problem. We propose an efficient algorithm that tackles the challenges above by transforming the event prediction problem into a search for all frequent eventsets preceding target events. The class imbalance problem is overcome by a search for patterns on the minority class exclusively; the discrimination power of patterns is then validated against other classes. Patterns are then combined into a rule-based model for prediction. Our experimental analysis indicates the types of event sequences where target events can be accurately predicted.

Question: How to predict rare events in temporal domains?


Requirements of Quality of Service in Wireless Sensor Network. Sensor networks are distributed networks made up of small sensing devices equipped with processors, memory, and short-range wireless communication. They differ from traditional computer networks in that they have resource constraints, unbalanced mixture traffic, data redundancy, network dynamics, and energy balance. Work within wireless sensor networks (WSNs) Quality of service (QoS) has been isolated and specific either on certain functional layers or application scenarios. However the area of sensor network quality of service (QoS) remains largely open. In this paper we define WSNs QoS requirements within a WSNs application, and then analyzing Issues for QoS Monitoring.

Question: What is the difference between QoS and QoE?


Optimized Edge Detection Algorithm for Face Recognition. Face recognition is one of the most challenging tasks in the field of image processing. This paper presents an optimized edge detection algorithm for the task of face recognition. In this method a gradient based filter using a wide convolution kernel is applied on the image to extract the edges. Later a thinning algorithm optimized for the wide convolution kernel is applied on the extracted edges. The advantages of this method over other gradient based methods is its ability to find the missing edges more successfully and boost the significant edges of the overall facial contour.

Question: What is the difference between a wide convolution kernel and a thinning algorithm?


3-Axis Magnetic Sensor Array System for Tracking Magnet's Position and Orientation. In medical diagnoses and treatments, e.g., the endoscopy, the dosage transition monitoring, it is often desirable to wirelessly track an object that moves through the human GI tract. In this paper, we present a magnetic localization and orientation system for such applications. This system uses a small magnet enclosed in the object to serve as excitation source. It does not require the connection wire and power supply for excitation signal. When the magnet moves, it establishes a static magnetic field around, whose intensity is related to the magnet's position and orientation. With the magnetic sensors, the magnetic intensities in some pre-determined spatial points can be detected, and the magnet's position and orientation parameters can be calculated based on an appropriate algorithm. Here, we propose a real-time tracking system built by Honeywell 3-axis magnetic sensors, HMC1053, as well as the computer sampling circuit. The results show that satisfactory tracking accuracy (average localization error is 3.3 mm) can be achieved using a sensor array with enough number of the 3-axis magnetic sensors

Question: How to track the magnet's position and orientation?


Tapping the power of text mining. Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.

Question: What is text mining?