Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model. As a result of the recent trend towards digitization -- which increasingly affects evidence-based medicine, accountable care, personalized medicine, and medical "Big Data" analysis -- growing amounts of clinical data are becoming available for analysis. In this paper, we follow the idea that one can model clinical processes based on clinical data, which can then be the basis for many useful applications. We model the whole clinical evolution of each individual patient, which is composed of thousands of events such as ordered tests, lab results and diagnoses. Specifically, we base our work on a dataset provided by the Charité University Hospital of Berlin which is composed of patients that suffered from kidney failure and either obtained an organ transplant or are still waiting for one. These patients face a lifelong treatment and periodic visits to the clinic. Our goal is to develop a system to predict the sequence of events recorded in the electronic medical record of each patient, and thus to develop the basis for a future clinical decision support system. For modelling, we use machine learning approaches which are based on a combination of the embedding of entities and events in a multidimensional latent space, in combination with Neural Network predictive models. Similar approaches have been highly successful in statistical models for recommendation systems, language models, and knowledge graphs. We extend existing embedding models to the clinical domain, in particular with respect to temporal sequences, long-term memories and personalization. We compare the performance of our proposed models with standard approaches such as K-nearest neighbors method, Naïve Bayes classifier and Logistic Regression, and obtained favorable results with our proposed model.

Question: What is the difference between a knowledge graph and a temporal latent embedding model?


Review of Machine Learning Algorithms in Differential Expression Analysis. In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNASeq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments.

Question: What is the difference between a machine learning algorithm and a statistical algorithm?


Diversifying Web Service Recommendation Results via Exploring Service Usage History. The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users' potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.

Question: How to diversify web service recommendation results?


Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. Transportation is a necessary infrastructure for our modern society. The performance of transportation systems is of crucial importance for individual mobility, commerce, and for the economic growth of all nations. In recent years modern society has been facing more traffic jams, higher fuel prices, and an increase in CO2 emissions. It is imperative to improve the safety and efficiency of transportation. Developing a sustainable intelligent transportation system requires the seamless integration and interoperability with emerging technologies such as connected vehicles, cloud computing, and the Internet of Things. In this article we present and discuss some of the integration challenges that must be addressed to enable an intelligent transportation system to address issues facing the transportation sector such as high fuel prices, high levels of CO2 emissions, increasing traffic congestion, and improved road safety.

Question: What are the integration challenges of intelligent transportation systems?


Implementation of RFID on Computer Based Test (RF-CBT) system. The paper presents an improvement of assessment model using RFID (Radio Frequency Identification) technology which implemented on Computer Based Test (CBT). The RFID worked at frequency 13.56 MHz. The RF-CBT system is constructed with User-PC and Server-PC. A RFID reader module (Mifare RC522, Microcontroller ATMEGA328) and LabVIEW program are installed on User-PC. An RFID tag information from User-PC was sent into Server-PC which is installed with MySQL database and PHP. The registered RFID tag on Server-PC could log in the CBT automatically. Our system is proposed not only for auto login to CBT but also for developing a random question and scoring test result on CBT. The system could reduce the opportunity to cheat in test as anyone cannot login to the test except have the authentic RFID tag. This system is feasible to measure students' cognitive abilities at once because it uses multiple choices test type.

Question: How does the RFID reader module work?


eSports: collaborative and synchronous video annotation system in grid computing environment. We designed eSports - a collaborative and synchronous video annotation platform, which is to be used in Internet scale cross-platform grid computing environment to facilitate computer supported cooperative work (CSCW) in education settings such as distance sport coaching, distance classroom etc. Different from traditional multimedia annotation systems, eSports provides the capabilities to collaboratively and synchronously play and archive real time live video, to take snapshots, to annotate video snapshots using whiteboard and to play back the video annotations synchronized with original video streams. eSports is designed based on the grid based collaboration paradigm $the shared event model using NaradaBrokering, which is a publish/subscribe based distributed message passing and event notification system. In addition to elaborate the design and implementation of eSports, we analyze the potential use cases of eSports under different education settings. We believed that eSports is very useful to improve the online collaborative coaching and education.

Question: What is the potential use case of eSports?


VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals. Enabling touch gesture sensing on all surfaces of the mobile device, not limited to the touchscreen area, leads to new user interaction experiences. In this paper, we propose VSkin, a system that supports fine-grained gesture-sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense finger tapping and movements. By measuring both the amplitude and the phase of each path of sound signals, VSkin detects tapping events with an accuracy of 99.65% and captures finger movements with an accuracy of 3.59mm.

Question: How does VSkin work?


Modelling Railway Interlocking Tables Using Coloured Petri Nets. Interlocking tables are the functional specification defining the routes, on which the passage of the train is allowed. Associated with the route, the states and actions of all related signalling equipment are also specified. This paper formally models the interlocking tables using Coloured Petri Nets (CPN). The CPN model comprises two parts: Signaling Layout and Interlocking Control. The Signaling Layout part is used to simulate the passage of the train. It stores geographic information of the signalling layout in tokens. The Interlocking Control part models actions of the controller according to the functions specified in the interlocking tables. The arc inscriptions in the model represent the content of the interlocking tables. Following our modelling approach we can reuse the same CPN net structure to model any new or modified interlocking system regardless of its size. Experimental results are presented to provide increased confidence in the model correctness.

Question: What is a Coloured Petri Net?


Generalized Sampling and Infinite-Dimensional Compressed Sensing. We introduce and analyze a framework and corresponding method for compressed sensing in infinite dimensions. This extends the existing theory from finite-dimensional vector spaces to the case of separable Hilbert spaces. We explain why such a new theory is necessary by demonstrating that existing finitedimensional techniques are ill-suited for solving a number of key problems. This work stems from recent developments in generalized sampling theorems for classical (Nyquist rate) sampling that allows for reconstructions in arbitrary bases. A conclusion of this paper is that one can extend these ideas to allow for significant subsampling of sparse or compressible signals. Central to this work is the introduction of two novel concepts in sampling theory, the stable sampling rate and the balancing property, which specify how to appropriately discretize an infinite-dimensional problem.

Question: What is generalized sampling?


Deconstructing Dynamic Symbolic Execution. Dynamic symbolic execution (DSE) is a well-known technique for automatically generating tests to achieve higher levels of coverage in a program. Two keys ideas of DSE are to: (1) seed symbolic execution by executing a program on an initial input; (2) using concrete values from the program execution in place of symbolic expressions whenever symbolic reasoning is hard or not desired. We describe DSE for a simple core language and then present a minimalist implementation of DSE for Python (in Python) that follows this basic recipe. The code is available at https://www.github.com/thomasjball/PyExZ3/ (tagged "v1.0") and has been designed to make it easy to experiment with and extend.

Question: What is dynamic symbolic execution?


Individuals' Stress Assessment Using Human-Smartphone Interaction Analysis. The increasing presence of stress in people' lives has motivated much research efforts focusing on continuous stress assessment methods of individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., they use invasive external devices, thus increasing entry costs and reducing user acceptance, or they use some of privacy-related information. This paper presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. Two different approaches are presented. One, based on smartphone gestures analysis, e.g., 'tap', 'scroll', 'swipe' and 'text writing', and evaluated in laboratory settings with 13 participants (F-measure 79-85 percent within-subject model, 70-80 percent global model); the second one based on smartphone usage analysis and tested in-the-wild with 25 participants (F-measure 77-88 percent within-subject model, 63-83 percent global model). Results show how these two methods enable an accurate stress assessment without being too intrusive, thus increasing ecological validity of the data and user acceptance.

Question: How to assess stress using smartphone sensors?


Model selection in ecology and evolution.. Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and mark-recapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.

Question: What is model selection?


THE DEMOCRATIZATION OF PERSONAL CONSUMER LOANS? DETERMINANTS OF SUCCESS IN ONLINE PEER-TO-PEER LENDING COMMUNITIES. Online peer-to-peer (P2P) lending communities enable individual consumers to borrow from, and lend money to, one another directly. We study the borrowerand loan listing-related determinants of funding success in an online P2P lending community by conceptualizing loan decision variables (loan amount, interest rate offered, duration of loan listing) as mediators between borrower attributes such as demographic characteristics, financial strength, and effort prior to making the request, and the likelihood of funding success. Borrower attributes are also treated as moderators of the effects of loan decision variables on funding success. The results of our empirical study, conducted using a database of 5,370 completed P2P loan transactions, provide support for the proposed conceptual framework. Although demographic attributes such as race and gender do affect likelihood of funding success, their effects are very small in comparison to effects of borrowers' financial strength and their effort when listing and publicizing the loan. These results are substantially different from the documented discriminatory practices of US financial institutions, suggesting that individual lenders lend more fairly when their own investment money is at stake in P2P loans. The paper concludes with specific suggestions to borrowers to increase their chances of receiving funding in P2P lending communities, and a discussion of future research opportunities.

Question: What are the determinants of success in online peer-to-peer lending?


Speaker Identification using Mel Frequency Cepstral Coefficient and BPNN. Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/developed by the researchers for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. BPNN is used for identification of speaker after training the feature set from MFCC. Some modifications to the existing technique of MFCC for feature extraction are also suggested to improve the speaker recognition efficiency. Information from speech recognition can be used in various ways in state-of-the-art speaker recognition systems. This includes the obvious use of recognized words to enable the use of text-dependent speaker modeling techniques when the words spoken are not given. Furthermore, it has been shown that the choice of words and phones itself can be a useful indicator of speaker identity. Also, recognizer output enables higher-level features, in particular those related to prosodic properties of speech. Keywords— Speaker identification, BPNN, MFCC, speech processing, feature extraction, speech signal

Question: What is speaker identification?


A stochastic control strategy for hybrid electric vehicles. The supervisory control strategy of a hybrid vehicle coordinates the operation of vehicle sub-systems to achieve performance targets such as maximizing fuel economy and reducing exhaust emissions. This high-level control problem is commonly referred as the power management problem. In the past, many supervisory control strategies were developed on the basis of a few pre-defined driving cycles, using intuition and heuristics. The resulting control strategy was often inherently cycle-beating and lacked a guaranteed level of optimality. In this study, the power management problem is tackled from a stochastic viewpoint. An infinite-horizon stochastic dynamic optimization problem is formulated. The power demand from the driver is modeled as a random Markov process. The optimal control strategy is then obtained by using stochastic dynamic programming (SDP). The obtained control law is in the form of a stationary full-state feedback and can be directly implemented. Simulation results over standard driving cycles and random driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach. It was found that the obtained SDP control algorithm outperforms a sub-optimal rule-based control strategy trained from deterministic DP results.

Question: What is a stochastic dynamic optimization problem?


Large margin hierarchical classification. We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set. Our approach combines ideas from large margin kernel methods and Bayesian analysis. Following the large margin principle, we associate a prototype with each label in the tree and formulate the learning task as an optimization problem with varying margin constraints. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent labels in the hierarchy. We describe new online and batch algorithms for solving the constrained optimization problem. We derive a worst case loss-bound for the online algorithm and provide generalization analysis for its batch counterpart. We demonstrate the merits of our approach with a series of experiments on synthetic, text and speech data.

Question: What is a large margin hierarchical classification?


A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization. The Teaching-Learning-Based Optimization (TLBO) algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO) algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.

Question: What is the difference between the basic TLBO and the NIWTLBO algorithm?


Autonomous Takeoff and Flight of a Tethered Aircraft for Airborne Wind Energy. A control design approach to achieve fully autonomous takeoff and flight maneuvers with a tethered aircraft is presented and demonstrated in real-world flight tests with a small-scale prototype. A ground station equipped with a controlled winch and a linear motion system accelerates the aircraft to takeoff speed and controls the tether reeling in order to limit the pulling force. This setup corresponds to airborne wind energy (AWE) systems with ground-based energy generation and rigid aircrafts. A simple model of the aircraft's dynamics is introduced and its parameters are identified from experimental data. A model-based, hierarchical feedback controller is then designed, whose aim is to manipulate the elevator, aileron, and propeller inputs in order to stabilize the aircraft during the takeoff and to achieve figure-of-eight flight patterns parallel to the ground. The controller operates in a fully decoupled mode with respect to the ground station. Parameter tuning and stability/robustness aspect are discussed, too. The experimental results indicate that the controller is able to achieve satisfactory performance and robustness, notwithstanding its simplicity, and confirm that the considered takeoff approach is technically viable and solves the issue of launching this kind of AWE systems in a compact space and at low additional cost.

Question: How to design a controller for a tethered aircraft?


Temporal Data Mining: An Overview. — To classify data mining problems and algorithms we used two dimensions: data type and type of mining operations. One of the main issue that arise during the data mining process is treating data that contains temporal information. The area of temporal data mining has very much attention in the last decade because from the time related feature of the data, one can extract much significant information which can not be extracted by the general methods of data mining. Many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as databases, statistics and machine learning the literature is scattered among many different sources. In this paper, we present a survey on techniques of temporal data mining.

Question: What is temporal data mining?


Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis. Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We describe in detail the interplay between objective and constraints in a number of single-period variants, including semivariance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multiperiod models based on scenario trees. A key property is the possibility of removing surplus money in future decisions, yielding approximate downside risk minimization.

Question: What is the difference between mean-variance and mean-semivariance?


Level Lines based Disocclusion. Object recognition, robotic vision, occluding noise removal or photograph design require the ability to perform disocclusion. We call disocclusion the recovery of hidden parts of objects in a digital image by interpolation from the vicinity of the occluded area. It is shown in this paper how disocclusion can be performed by means of level lines structure, which offers a reliable, complete and contrast-invariant representation of image, in contrast to edges. Level lines based disocclusion yields a solution that may have strong discontinuities, which is not possible with PDE-based interpolation. Moreover, the proposed method is fully compatible with Kanizsa's theory of "amodal completion".

Question: What is the level lines based disocclusion?


Comparative Study on Load Balancing Techniques in Cloud Computing. The present era has witnessed tremendous growth of the internet and various applications that are running over it. Cloud computing is the internet based technology, emphasizing its utility and follows pay-as-you-go model, hence became so popular with high demanding features. Load balancing is one of the interesting and prominent research topics in cloud computing, which has gained a large attention recently. Users are demanding more services with better results. Many algorithms and approaches are proposed by various researchers throughout the world, with the aim of balancing the overall workload among given nodes, while attaining the maximum throughput and minimum time. In this paper, various proposed algorithms addressing the issue of load balancing in Cloud Computing are analyzed and compared to provide a gist of the latest approaches in this research area.

Question: What is the difference between cloud computing and load balancing?


Virtualized and flexible ECC for main memory. We present a general scheme for virtualizing main memory error-correction mechanisms, which map redundant information needed to correct errors into the memory namespace itself. We rely on this basic idea, which increases flexibility to increase error protection capabilities, improve power efficiency, and reduce system cost; with only small performance overheads. We augment the virtual memory system architecture to detach the physical mapping of data from the physical mapping of its associated ECC information. We then use this mechanism to develop two-tiered error protection techniques that separate the process of detecting errors from the rare need to also correct errors, and thus save energy. We describe how to provide strong chipkill and double-chip kill protection using existing DRAM and packaging technology. We show how to maintain access granularity and redundancy overheads, even when using ×8 DRAM chips. We also evaluate error correction for systems that do not use ECC DIMMs. Overall, analysis of demanding SPEC CPU 2006 and PARSEC benchmarks indicates that performance overhead is only 1% with ECC DIMMs and less than 10% using standard Non-ECC DIMM configurations, that DRAM power savings can be as high as 27%, and that the system energy-delay product is improved by 12% on average.

Question: What is the performance overhead of using ECC DIMMs?


Decentralized frequency control of a DDG-PV Microgrid in islanded mode. This paper presents an innovative approach to control the frequency in Diesel-Driven Generator (DDG)-Photovoltaic (PV) Microgrid (MG) in islanded mode. The common approach is based on hierarchical control: primary control and secondary control. The conventional primary controller is a P-controller. Compared to this, we propose a novel primary controller for PV to mimic the dynamic behavior of the conventional synchronous generator (SG) so that more inertia in the MG can be obtained. The proposed secondary control is accomplished decentralized, too, so that no communication links for the real-time control are required and the single point of failure can be avoided. Although in this approach every controller works locally, the controllers and their parameters are designed centrally. This means, the controllers can be appropriately adjusted, when the parameters or the topology of the system change. The parameters of the controllers are optimized by minimizing the H-inf-norm using a known non-smooth method.

Question: How to control the frequency in a microgrid?


Delta-Sigma FDC Based Fractional-N PLLs. Fractional-N phase-locked loop frequency synthesizers based on time-to-digital converters (TDC-PLLs) have been proposed to reduce the area and linearity requirements of conventional PLLs based on delta-sigma modulation and charge pumps (ΔΣ-PLLs). Although TDC-PLLs with good performance have been demonstrated, TDC quantization noise has so far kept their phase noise and spurious tone performance below that of the best comparable ΔΣ-PLLs. An alternative approach is to use a delta-sigma frequency-to-digital converter (ΔΣ FDC) in place of a TDC to retain the benefits of TDC-PLLs and ΔΣ-PLLs. This paper proposes a practical ΔΣ FDC based PLL in which the quantization noise is equivalent to that of a ΔΣ-PLL. It presents a linearized model of the PLL, design criteria to avoid spurious tones in the ΔΣFDC quantization noise, and a design methodology for choosing the loop parameters in terms of standard PLL target specifications.

Question: What is a fractional-N phase-locked loop?


An algorithm to determine peer-reviewers. The peer-review process is the most widely accepted certification mechanism for officially accepting the written results of researchers within the scientific community. An essential component of peer-review is the identification of competent referees to review a submitted manuscript. This article presents an algorithm to automatically determine the most appropriate reviewers for a manuscript by way of a co-authorship network data structure and a relative-rank particle-swarm algorithm. This approach is novel in that it is not limited to a pre-selected set of referees, is computationally efficient, requires no human-intervention, and, in some instances, can automatically identify conflict of interest situations. A useful application of this algorithm would be to open commentary peer-review systems because it provides a weighting for each referee with respects to their expertise in the domain of a manuscript. The algorithm is validated using referee bid data from the 2005 Joint Conference on Digital Libraries.

Question: How to determine peer-reviewers?


Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation. Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.

Question: How to forecast the load?


Improv Chat: Second Response Generation for Chatbot. Existing research on response generation for chatbot focuses on First Response Generation which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user's query). In this paper, we introduce a new task Second Response Generation, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users' query and the chatbot's first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based models for response matching and ranking. We include the preliminary experiments and results in this paper. This work could be further advanced with better deep matching models for retrieval base systems or generative models for generation based systems as well as extensive evaluations in real-life applications.

Question: What is the second response generation for chatbot?


High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data. The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id. It plays a critical role in the construction of a high quality information network, and can be further leveraged for a variety of information retrieval and NLP tasks such as text categorization and document tagging. EDL is a complex and challenging problem due to ambiguity of the mentions and real world text being multi-lingual. Moreover, EDL systems need to have high throughput and should be lightweight in order to scale to large datasets and run on off-the-shelf machines. More importantly, these systems need to be able to extract and disambiguate dense annotations from the data in order to enable an Information Retrieval or Extraction task running on the data to be more efficient and accurate. In order to address all these challenges, we present the Lithium EDL system and algorithm a high-throughput, lightweight, language-agnostic EDL system that extracts and correctly disambiguates 75% more entities than state-of-the-art EDL systems and is significantly faster than them.

Question: What is the name of the algorithm?


Reconstructing Indian Population History. India has been underrepresented in genome-wide surveys of human variation. We analyse 25 diverse groups in India to provide strong evidence for two ancient populations, genetically divergent, that are ancestral to most Indians today. One, the 'Ancestral North Indians' (ANI), is genetically close to Middle Easterners, Central Asians, and Europeans, whereas the other, the 'Ancestral South Indians' (ASI), is as distinct from ANI and East Asians as they are from each other. By introducing methods that can estimate ancestry without accurate ancestral populations, we show that ANI ancestry ranges from 39–71% in most Indian groups, and is higher in traditionally upper caste and Indo-European speakers. Groups with only ASI ancestry may no longer exist in mainland India. However, the indigenous Andaman Islanders are unique in being ASI-related groups without ANI ancestry. Allele frequency differences between groups in India are larger than in Europe, reflecting strong founder effects whose signatures have been maintained for thousands of years owing to endogamy. We therefore predict that there will be an excess of recessive diseases in India, which should be possible to screen and map genetically.

Question: What is the difference between the ancestral north and the ancestral south indians?


Empirical formula for propagation loss in land mobile radio services. An empirical formula for propagation loss is derived from Okumura's report in order to put his propagation prediction method to computational use. The propagation loss in an urban area is presented in a simple form: A + B log10R, where A and B are frequency and antenna height functions and R is the distance. The introduced formula is applicable to system designs for UHF and VHF land mobile radio services, with a small formulation error, under the following conditions: frequency range 100-1500 MHz, distance 1-20 km, base station antenna height 30-200 m, and vehicular antenna height 1-10 m.

Question: What is the formula for propagation loss?


Blind image quality assessment by relative gradient statistics and adaboosting neural network. The image gradient is a commonly computed image feature and a potentially predictive factor for image quality assessment (IQA). Indeed, it has been successfully used for both fulland noreference image quality prediction. However, the gradient orientation has not been deeply explored as a predictive source of information for image quality assessment. Here we seek to amend this by studying the quality relevance of the relative gradient orientation, viz., the gradient orientation relative to the surround. We also deploy a relative gradient magnitude feature which accounts for perceptual masking and utilize an AdaBoosting back-propagation (BP) neural network to map the image features to image quality. The generalization of the AdaBoosting BP neural network results in an effective and robust quality prediction model. The new model, called Oriented Gradients Image Quality Assessment (OG-IQA), is shown to deliver highly competitive image quality prediction performance as compared with the most popular IQA approaches. Furthermore, we show that OG-IQA has good database independence properties and a low complexity. & 2015 Elsevier B.V. All rights reserved.

Question: What is the difference between the gradient orientation and the gradient magnitude?


Text2Scene: Generating Abstract Scenes from Textual Descriptions. In this paper, we propose an end-to-end model that learns to interpret natural language describing a scene to generate an abstract pictorial representation. The pictorial representations generated by our model comprise the spatial distribution and attributes of the objects in the described scene. Our model uses a sequence-to-sequence network with a double attentive mechanism and introduces a regularization strategy. These scene representations can be sampled from our model similarly as in languagegeneration models. We show that the proposed model, initially designed to handle the generation of cartoon-like pictorial representations in the Abstract Scenes Dataset, can also handle, under minimal modifications, the generation of semantic layouts corresponding to real images in the COCO dataset. Human evaluations using a visual entailment task show that pictorial representations generated with our full model can entail at least one out of three input visual descriptions 94% of the times, and at least two out of three 62% of the times for each image.

Question: How does the model work?


E-Learning's Impact on the Academic Performance of Student-Teachers: A Curriculum Lens. This study was planned to explore the impact of eLearning on the academic performance of student-teachers. The researchers' concern with e-Learning was sanctioned by the need for a teaching and learning strategy that can help institutions of learning address their problems and improve on their outcome. In this respect, the researchers conducted an experiment to find out whether student-teachers taught using the method of e-Learning (blended learning) perform better than studentteachers taught using the traditional method of teaching and learning. Findings offers new evidence that e-Learning has a significant influence on the performance of students as student-teachers taught using eLearning consistently performed better than student-teachers taught using the traditional method. Based on this result, recommendations are made to training institutions to embrace ICTs and become more flexible by adopting learning approaches that are dynamic and multidimensional as problems in education are becoming more complex.

Question: What is the impact of e-learning on the academic performance of student-teachers?


Dynamic Online Pricing with Incomplete Information. Consider the pricing decision for a manager at large online retailer, such as Amazon.com, that sells millions of products. The pricing manager must decide on real-time prices for each of these product. Due to the large number of products, the manager must set retail prices without complete demand information. A manager can run price experiments to learn about demand and maximize long run profits. There are two aspects that make the online retail pricing different from traditional brick and mortar settings. First, due to the number of products the manager must be able to automate pricing. Second, an online retailer can make frequent price changes. Pricing differs from other areas of online marketing where experimentation is common, such as online advertising or website design, as firms do not randomize prices to different customers at the same time. In this paper we propose a dynamic price experimentation policy where the firm has incomplete demand information. For this general setting, we derive a pricing algorithm that balances earning profit immediately and learning for future profits. The proposed approach marries statistical machine learning and economic theory. In particular we combine multi-armed bandit (MAB) algorithms with partial identification of consumer demand into a unique pricing policy. Our automated policy solves this problem using a scalable distribution-free algorithm. We show that our method converges to the optimal price faster than standard machine learning MAB solutions to the problem. In a series of Monte Carlo simulations, we show that the proposed approach perform favorably compared to methods in computer science and revenue management. ∗Ross School of Business, University of Michigan. Contact: ericmsch@umich.edu †Rady School of Management, University of California, San Diego. Contact: kamisra@ucsd.edu ‡Department of Electrical Engineering and Computer Science, University of Michigan. Conta

Question: How to price products with incomplete information?


Applied machine vision of plants: a review with implications for field deployment in automated farming operations. Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.

Question: What is the difference between a monocular and a stereo vision system?


Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems. Numerical solution of ill-posed problems is often accomplished by discretization (projection onto a finite dimensional subspace) followed by regularization. If the discrete problem has high dimension, though, typically we compute an approximate solution by projecting the discrete problem onto an even smaller dimensional space, via iterative methods based on Krylov subspaces. In this work we present a common framework for efficient algorithms that regularize after this second projection rather than before it. We show that determining regularization parameters based on the final projected problem rather than on the original discretization has firmer justification and often involves less computational expense. We prove some results on the approximate equivalence of this approach to other forms of regularization, and we present numerical examples.

Question: How to choose regularization parameters in iterative methods for ill-posed problems?


Abnormal brain chemistry in chronic back pain: an in vivo proton magnetic resonance spectroscopy study. The neurobiology of chronic pain, including chronic back pain, is unknown. Structural imaging studies of the spine cannot explain all cases of chronic back pain. Functional brain imaging studies indicate that the brain activation patterns are different between chronic pain patients and normal subjects, and the thalamus, and prefrontal and cingulate cortices are involved in some types of chronic pain. Animal models of chronic pain suggest abnormal spinal cord chemistry. Does chronic pain cause brain chemistry changes? We examined brain chemistry changes in patients with chronic back pain using in vivo single- voxel proton magnetic resonance spectroscopy ((1)H-MRS). In vivo (1)H-MRS was used to measure relative concentrations of N-acetyl aspartate, creatine, choline, glutamate, glutamine, gamma-aminobutyric acid, inositol, glucose and lactate in relation to the concentration of creatine. These measurements were performed in six brain regions of nine chronic low back pain patients and 11 normal volunteers. All chronic back pain subjects underwent clinical evaluation and perceptual measures of pain and anxiety. We show that chronic back pain alters the human brain chemistry. Reductions of N-acetyl aspartate and glucose were demonstrated in the dorsolateral prefrontal cortex. Cingulate, sensorimotor, and other brain regions showed no chemical concentration differences. In chronic back pain, the interrelationship between chemicals within and across brain regions was abnormal, and there was a specific relationship between regional chemicals and perceptual measures of pain and anxiety. These findings provide direct evidence of abnormal brain chemistry in chronic back pain, which may be useful in diagnosis and future development of more effective pharmacological treatments.

Question: What is the relationship between brain chemistry and chronic back pain?


Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces. Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.

Question: What is the maximum ITR of a noncontact EEG?


A Li-Ion Battery Charger With Smooth Control Circuit and Built-In Resistance Compensator for Achieving Stable and Fast Charging. A built-in resistance compensator (BRC) technique is presented to speed up the charging time of a lithium-ion battery. A smooth control circuit (SCC) is proposed to ensure the stable transition from the constant-current (CC) to the constant-voltage (CV) stage. Due to the external parasitic resistance of the Li-ion battery-pack system, the charger circuit switches from the CC to the CV stage without fully charging the cell. The BRC technique dynamically estimates the external resistance to extend the CC stage. The experimental results show that the period of the CC stage can be extended to 40% of that of the original design. The charging time is effectively reduced.

Question: How to speed up the charging time of a lithium-ion battery?


154-2008: Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry. The explosion of customer data in the last twenty years has increased the need for data mining aimed at customer relationship management (CRM) and understanding the customer. It is well known that the telecom sector consists of customers with a wide array of customer behaviors. These customers pose different risks, making it imperative to implement different treatment strategies to maximize shareholder profit and improve revenue generation. Segmentation is the process of developing meaningful customer groups that are similar based on individual account characteristics and behaviors. The goal of segmentation is to know your customer better and to apply that knowledge to increase profitability, reduce operational cost, and enhance customer service. Segmentation can provide a multidimensional view of the customer for better treatment targeting. An improved understanding of customer risk and behaviors enables more effective portfolio management and the proactive application of targeted treatments to lift account profitability. In this paper we outline several segmentation techniques using SAS Enterprise MinerTM. INTRODUCTION Rapid advances in computer technology and an explosion of data collection systems over the last thirty years make it more critical for business to understand their customers. Companies employing data driven analytical strategies often enjoy a competitive advantage. Many organizations across several industries widely employ analytical models to gain a better understanding of their customers. They use these models to predict a wide array of events such as behavioral risk, fraud, or the likelihood of response. Regardless of the predictive variable, a single model may not perform optimally across the target population because there may be distinct segments with different characteristics inherent in the population. Segm

Question: What is segmentation?


0-Day Vulnerabilities and Cybercrime. This study analyzes 0-day vulnerabilities in the broader context of cybercrime and economic markets. The work is based on the interviews of several leading experts and on a field research of the authors. In particular, cybercrime is considered when involving traditional criminal activities or when military operations are involved. A description of different 0-day vulnerability markets - White, Black and Government markets - is provided, as well as the characteristics of malware factories and their major customers are discussed.

Question: What is cybercrime?


Distortion correction formulas for pre-objective dual galvanometer laser scanning.. For many applications a laser beam needs to be scanned across a plane. Several methods exist to accomplish this.One method, dual galvanometer pre-objective scanning, is particularly well suited to precise laser marking applications. The laser beam impinges on a first mirror mounted on a galvanometer motor, then on a second mirror with its rota tion axis parallel to the laser axis, and passes then through the flat-field objective lens, which focuses the beam onto the image plane. Figure 1 shows a photograph of the SILAMATIK laser marker. This letter investigates what amount of distortion is intro duced and how it can be corrected electronically. We as sume that the lens is axial symmetric. The geometry is that of Fig. 2. Say the first mirror is rotated a mechanical angle a/2 from its rest position and the second mirror an angle 6/2. The direction vector lR of the beam that falls on the lens is

Question: What is the distortion introduced by a dual galvanometer pre-objective scanning system?


Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong. Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently proposed defenses that intentionally combine components designed to work well together. A third defense combines three independent defenses. For all the components of these defenses and the combined defenses themselves, we show that an adaptive adversary can create adversarial examples successfully with low distortion. Thus, our work implies that ensemble of weak defenses is not sufficient to provide strong defense against adversarial examples.

Question: How can we create a strong defense against adversarial examples?


Yacc is dead. We present two novel approaches to parsing context-free languages. The first approach is based on an extension of Brzozowski's derivative from regular expressions to context-free grammars. The second approach is based on a generalization of the derivative to parser combinators. The payoff of these techniques is a small (less than 250 lines of code), easy-to-implement parsing library capable of parsing arbitrary context-free grammars into lazy parse forests. Implementations for both Scala and Haskell are provided. Preliminary experiments with S-Expressions parsed millions of tokens per second, which suggests this technique is efficient enough for use in practice. 1 Top-down motivation: End cargo cult parsing "Cargo cult parsing" is a plague upon computing. Cargo cult parsing refers to the use of "magic" regular expressions—often cut and pasted directly from Google search results—to parse languages which ought to be parsed with contextfree grammars. Such parsing has two outcomes. In the first case, the programmer produces a parser that works "most of the time" because the underlying language is fundamentally irregular. In the second case, some domain-specific language ends up with a mulish syntax, because the programmer has squeezed the language through the regular bottleneck. There are two reasons why regular expressions are so abused while context-free languages remain foresaken: (1) regular expression libraries are available in almost every language, while parsing libraries and toolkits are not, and (2) regular expressions are "WYSIWYG"—the language described is the language that gets matched—whereas parser-generators are WYSIWYGIYULR(k)—"what you see is what you get if you understand LR(k)." To end cargo-cult parsing, we need a new approach to parsing that: 1. handles arbitrary context-free grammars; 2. parses efficiently on average; and 3. can be implemented as a library with little effort. The end goals of the three conditions are simplicity, feasi

Question: What is the difference between a parser and a parser generator?


Low cost power failure protection for MLC NAND flash storage systems with PRAM/DRAM hybrid buffer. In the latest PRAM/DRAM hybrid MLC NAND flash storage systems (NFSS), DRAM is used to temporarily store file system data for system response time reduction. To ensure data integrity, super-capacitors are deployed to supply the backup power for moving the data from DRAM to NAND flash during power failures. However, the capacitance degradation of super-capacitor severely impairs system robustness. In this work, we proposed a low cost power failure protection scheme to reduce the energy consumption of power failure protection and increase the robustness of the NFSS with PRAM/DRAM hybrid buffer. Our scheme enables the adoption of the more reliable regular capacitor to replace the super capacitor as the backup power. The experimental result shows that our scheme can substantially reduce the capacitance budget of power failure protection circuitry by 75.1% with very marginal performance and energy overheads.

Question: How to protect the data integrity of the system during power failure?


Mining significant graph patterns by leap search. With ever-increasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with user-specified objective functions. Most objective functions are not antimonotonic, which could fail all of frequency-centric graph mining algorithms. In this paper, we give the first comprehensive study on general mining method aiming to find most significant patterns directly. Our new mining framework, called LEAP (Descending Leap Mine), is developed to exploit the correlation between structural similarity and significance similarity in a way that the most significant pattern could be identified quickly by searching dissimilar graph patterns. Two novel concepts, structural leap search and frequency descending mining, are proposed to support leap search in graph pattern space. Our new mining method revealed that the widely adopted branch-and-bound search in data mining literature is indeed not the best, thus sketching a new picture on scalable graph pattern discovery. Empirical results show that LEAP achieves orders of magnitude speedup in comparison with the state-of-the-art method. Furthermore, graph classifiers built on mined patterns outperform the up-to-date graph kernel method in terms of efficiency and accuracy, demonstrating the high promise of such patterns.

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


Evaluating Window Joins over Unbounded Streams. We investigate algorithms for evaluating sliding window joins over pairs of unbounded streams. We introduce a unittime-basis cost model to analyze the expected performance of these algorithms. Using this cost model, we propose strategies for maximizing the efficiency of processing joins in three scenarios. First, we consider the case where one stream is much faster than the other. We show that asymmetric combinations of join algorithms, (e.g., hash join on one input, nested-loops join on the other) can outperform symmetric join algorithm implementations. Second, we investigate the case where system resources are insufficient to keep up with the input streams. We show that we can maximize the number of join result tuples produced in this case by properly allocating computing resources across the two input streams. Finally, we investigate strategies for maximizing the number of result tuples produced when memory is limited, and show that proper memory allocation across the two input streams can result in significantly lower resource usage and/or more result tuples produced.

Question: How to evaluate window joins over unbounded streams?


Applications of Random Sampling in Computational Geometry, II. Random sampling is used for several new geometric algorithms. The algorithms are "Las Vegas," and their expected bounds are with respect to the random behavior of the algorithms. One algorithm reports all the intersecting pairs of a set of line segments in the plane, and requires <italic>&Ogr;</italic>(<italic>A</italic> + <italic>n</italic> log <italic>n</italic>) expected time, where <italic>A</italic> is the size of the answer, the number of intersecting pairs reported. The algorithm requires <italic>&Ogr;</italic>(<italic>n</italic>) space in the worst case. Another algorithm computes the convex hull of a point set in <italic>E</italic><supscrpt>3</supscrpt> in <italic>&Ogr;</italic>(<italic>n</italic> log <italic>A</italic>) expected time, where <italic>n</italic> is the number of points and <italic>A</italic> is the number of points on the surface of the hull. A simple Las Vegas algorithm triangulates simple polygons in <italic>&Ogr;</italic>(<italic>n</italic> log log <italic>n</italic>) expected time. Algorithms for half-space range reporting are also given. In addition, this paper gives asymptotically tight bounds for a combinatorial quantity of interest in discrete and computational geometry, related to halfspace partitions of point sets.

Question: What is the expected time complexity of a Las Vegas algorithm?


Machine Translation Approaches: Issues and Challenges. In the modern world, there is an increased need for language translations owing to the fact that language is an effective medium of communication. The demand for translation has become more in recent years due to increase in the exchange of information between various regions using different regional languages. Accessibility to web document in other languages, for instance, has been a concern for information Professionals. Machine translation (MT), a subfield under Artificial Intelligence, is the application of computers to the task of translating texts from one natural (human) language to another. Many approaches have been used in the recent times to develop an MT system. Each of these approaches has its own advantages and challenges. This paper takes a look at these approaches with the few of identifying their individual features, challenges and the best domain they are best suited to.

Question: What is the best domain for machine translation?


Pushover Analysis of 4 Storey ' s Reinforced Concrete Building. The earthquakes in the Indian subcontinent have led to an increase in the seismic zoning factor over many parts of the country. Also, ductility has become an issue for all building that was designed and detailed using earlier versions of the codes. Under such circumstances, seismic qualification of building has become extremely important. The structural engineering profession has been using the nonlinear static procedure (NSP) or pushover analysis. Modeling for such analysis requires the determination of the nonlinear properties of each component in the structure, quantified by strength and deformation capacities, which depend on the modeling assumptions. Pushover analysis is carried out for either user-defined nonlinear hinge properties or default-hinge properties, available in some programs based on the FEMA-356 and ATC-40 guidelines. This paper aims to evaluate the zone –IV selected reinforced concrete building to conduct the non-linear static analysis (Pushover Analysis). The pushover analysis shows the pushover curves, capacity spectrum, plastic hinges and performance level of the building. The non-linear static analysis gives better understanding and more accurate seismic performance of buildings of the damage or failure element.

Question: How to conduct nonlinear static analysis?


Appraisal of homogeneous techniques in Distributed Data Mining: classifier approach. In recent years, Distributed Data Mining (DDM) has evolved in large space aiming at minimizing computation cost and memory overhead in processing huge geographically distributed data. There are two approaches of DDM -- homogeneous and heterogeneous classifier approach. This paper presents implementation of four homogeneous classifier techniques for DDM with different Electronic Health Records (EHRs) homogeneous datasets namely diabetes, hepatitis, hypothyroid and further analyzing results based on metric evaluation.

Question: What is the difference between homogeneous and heterogeneous classifier approach?


N-Fold Inspection: A Requirements Analysis Technique. N-fold inspection uses traditional inspections of the user requirements document (URD) but replicates the inspection activities using N independent teams. A pilot study was conducted to explore the usefulness of N-fold inspection during requirements analysis. A comparison of N-fold inspection with other development techniques reveals that N-fold inspection is a cost-effective method for finding faults in the URD and may be a valid technique in the development of mission-critical software systems.

Question: What is N-fold inspection?


AdapNet: Adaptive semantic segmentation in adverse environmental conditions. Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world. In this paper, we propose a novel semantic segmentation architecture and the convoluted mixture of deep experts (CMoDE) fusion technique that enables a multi-stream deep neural network to learn features from complementary modalities and spectra, each of which are specialized in a subset of the input space. Our model adaptively weighs class-specific features of expert networks based on the scene condition and further learns fused representations to yield robust segmentation. We present results from experimentation on three publicly available datasets that contain diverse conditions including rain, summer, winter, dusk, fall, night and sunset, and show that our approach exceeds the state-of-the-art. In addition, we evaluate the performance of autonomously traversing several kilometres of a forested environment using only the segmentation for perception.

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


Face poser: Interactive modeling of 3D facial expressions using facial priors. This article presents an intuitive and easy-to-use system for interactively posing 3D facial expressions. The user can model and edit facial expressions by drawing freeform strokes, by specifying distances between facial points, by incrementally editing curves on the face, or by directly dragging facial points in 2D screen space. Designing such an interface for 3D facial modeling and editing is challenging because many unnatural facial expressions might be consistent with the user's input. We formulate the problem in a maximum a posteriori framework by combining the user's input with priors embedded in a large set of facial expression data. Maximizing the posteriori allows us to generate an optimal and natural facial expression that achieves the goal specified by the user. We evaluate the performance of our system by conducting a thorough comparison of our method with alternative facial modeling techniques. To demonstrate the usability of our system, we also perform a user study of our system and compare with state-of-the-art facial expression modeling software (Poser 7).

Question: How to model facial expressions?


Next-Term Student Performance Prediction: A Recommender Systems Approach. An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success, interventions that support course selections on a semester-to-semester basis have yet to be deeply understood. To further this goal, we develop a system to predict students' grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with additional information about students, courses and the instructors teaching them. We explore a variety of classic and state-of-the-art techniques which have proven effective for recommendation tasks in the e-commerce domain. In our experiments, Factorization Machines (FM), Random Forests (RF), and the Personalized Linear Multiple Regression model achieve the lowest prediction error. Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. By comparing feature importance across populations and across models, we uncover strong connections between instructor characteristics and student performance. We also discover key differences between transfer and non-transfer students. Ultimately we find that a hybrid FM-RF method can be used to accurately predict grades for both new and returning students taking both new and existing courses. Application of these techniques holds promise for student degree planning, instructor interventions, and personalized advising, all of which could improve retention and academic performance.

Question: What is the difference between transfer and non-transfer students?


Which Hotel attributes Matter ? A review of previous and a framework for future research. A lot of effort has been made in the last decades to reveal, which hotel attributes guest care about. Due to the high costs that are typically involved with investments in the hotel industry, it makes a lot of sense to study, which product components the travellers appreciate. This study reveals that hotel attribute research turns out to be a wide and extremely heterogeneous field of research. The authors review empirical studies investigating the importance of hotel attributes, provide attribute rankings and suggest a framework for past and future research projects in the field, based on the dimensions "focus of research", "risk versus utility" and "trade-off versus no trade-off questioning situation".

Question: Which hotel attributes matter?


Lyapunov-function-based flux and speed observer for AC induction motor sensorless control and parameters estimation. AC induction motors have become very popular for motion-control applications due to their simple and reliable construction. Control of drives based on ac induction motors is a quite complex task. Provided the vector-control algorithm is used, not only the rotor speed but also the position of the magnetic flux inside the motor during the control process should be known. In most applications, the flux sensors are omitted and the magnetic-flux phasor position has to be calculated. However, there are also applications in which even speed sensors should be omitted. In such a situation, the task of state reconstruction can be solved only from voltage and current measurements. In the current paper, a method based on deterministic evaluation of measurement using the state observer based on the Lyapunov function is presented. The method has been proven in testing on a real ac induction machine.

Question: How to calculate the magnetic flux phasor position?


Power Amplifiers and Transmitters for RF and Microwave. The generation of RF/microwave power is required not only in wireless communications, but also in applications such as jamming, imaging, RF heating, and miniature dc/dc converters. Each application has its own unique requirements for frequency, bandwidth, load, power, efficiency, linearity, and cost. RF power is generated by a wide variety of techniques, implementations, and active devices. Power amplifiers are incorporated into transmitters in a similarly wide variety of architectures, including linear, Kahn, envelope tracking, outphasing, and Doherty. Linearity can be improved through techniques such as feedback, feedforward, and predistortion.

Question: What is the difference between a power amplifier and a transmitter?


Bayesian Compressive Sensing Using Laplace Priors. In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the sparsity of the unknown signal. We describe the relationship among a number of sparsity priors proposed in the literature, and show the advantages of the proposed model including its high degree of sparsity. Moreover, we show that some of the existing models are special cases of the proposed model. Using our model, we develop a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings. Unlike most existing CS reconstruction methods, the proposed algorithm is fully automated, i.e., the unknown signal coefficients and all necessary parameters are estimated solely from the observation, and, therefore, no user-intervention is needed. Additionally, the proposed algorithm provides estimates of the uncertainty of the reconstructions. We provide experimental results with synthetic 1-D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.

Question: What is the difference between the Bayesian Compressive Sensing and the Bayesian Compressive Sensing Using Laplace Priors?


Micro bubble fluidics by EWOD and ultrasonic excitation for micro bubble tweezers. Recently, we envisioned so called micro bubble tweezers where EWOD (electrowetting-on-dielectric) actuated bubbles can manipulate micro objects such as biological cells by pushing or pulling them. Besides, oscillating (shrinking and expanding) bubbles in the presence of ultrasonic wave act as a to deliver drugs and molecules into the cells. In this paper, as a great stride in our quest for micro bubble tweezers, we present (1) full realization of two critical bubble operations (generating of bubbles in an on-chip and on-demand manner and splitting of single bubbles) and (2) two possible applications of mobile bubbles oscillating under acoustic excitation (a mobile vortex generator and micro particle carrier).

Question: What is the difference between micro bubble tweezers and micro bubble tweezers?


An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems. In the last two decades, we have witnessed that Interval Type-2 Fuzzy Logic Systems (IT2-FLSs) have been successfully implemented in various engineering areas. In this paper, we will introduce a free open source Mat lab/Simulink toolbox for the development of IT2-FLSs for a wider accessibility to users beyond the type-2 fuzzy logic community. The presented IT2-FLS toolbox allows intuitive implementation of IT2-FLSs where it is capable to cover all the phases of its design. In order to allow users to easily construct IT2-FISs, a GUI is developed which is similar to that of Matlab® Fuzzy Logic Toolbox. We have embedded various Type Reduction (TR) methods in the toolbox since the TR method is the most important operation that must be taken into consideration. This gives the opportunity to the user to examine the performance of his/her IT2-FLS design with respect to the TR methods. We have also developed an IT2-FLS Simulink library so that the designer can perform various simulation analyses and can also investigate the effect of TR method on the performance of the IT2-FLS. Moreover, the developed IT2-FLS Mat lab/Simulink toolbox contains an automatic connection between Matlab and Simulink environments. Once the user has finished the design of the IT2-FLS via the GUI, it is possible to export his/her design directly to Simulink via an automatic Simulink file generation. We believe that the availability of the developed free and open-source IT2-FLS Mat lab/Simulink toolbox will be an important step for a wider deployment and development of IT2-FLSs.

Question: What is an open source Matlab/Simulink toolbox?


An Evaluation Framework for Plagiarism Detection. We present an evaluation framework for plagiarism detection.1 The framework provides performance measures that address the specifics of plagiarism detection, and the PAN-PC-10 corpus, which contains 64 558 artificial and 4 000 simulated plagiarism cases, the latter generated via Amazon's Mechanical Turk. We discuss the construction principles behind the measures and the corpus, and we compare the quality of our corpus to existing corpora. Our analysis gives empirical evidence that the construction of tailored training corpora for plagiarism detection can be automated, and hence be done on a large scale.

Question: What is the size of the corpus?


User Traffic Profile for Traffic Reduction and Effective Bot C&C Detection. Bots are malicious software components used for generating spams, launching denial of service attacks, phishing, identity theft and information exfiltration and such other illegal activities. Bot detection is an area of active research in recent times. Here we propose a bot detection mechanism for a single host. A user traffic profile is used to filter out normal traffic generated by the host. The remaining suspicious traffic is subject to detailed analysis. The nature of detailed analysis is derived from a characterization of bot traffic. The detection system is tested using two real world bots. Performance evaluation results show that the proposed system achieves a high detection rate (100%) and a low false positive rate (0–8%). The traffic filtering yielded an average reduction in traffic by 70%.

Question: What is bot detection?


A New Nested Neutral Point-Clamped (NNPC) Converter for Medium-Voltage (MV) Power Conversion. In this paper, a new voltage source converter for medium voltage applications is presented which can operate over a wide range of voltages (2.4-7.2 kV) without the need for connecting the power semiconductor in series. The operation of the proposed converter is studied and analyzed. In order to control the proposed converter, a space-vector modulation (SVM) strategy with redundant switching states has been proposed. SVM usually has redundant switching state anyways. What is the main point we are trying to get to? These redundant switching states help to control the output voltage and balance voltages of the flying capacitors in the proposed converter. The performance of the converter under different operating conditions is investigated in MATLAB/Simulink environment. The feasibility of the proposed converter is evaluated experimentally on a 5-kVA prototype.

Question: What is the main point we are trying to get to?


Osmotic Heat Engine Using Thermally Responsive Ionic Liquids.. The osmotic heat engine (OHE) is a promising technology for converting low grade heat to electricity. Most of the existing studies have focused on thermolytic salt systems. Herein, for the first time, we proposed to use thermally responsive ionic liquids (TRIL) that have either an upper critical solution temperature (UCST) or lower critical solution temperature (LCST) type of phase behavior as novel thermolytic osmotic agents. Closed-loop TRIL-OHEs were designed based on these unique phase behaviors to convert low grade heat to work or electricity. Experimental studies using two UCST-type TRILs, protonated betaine bis(trifluoromethyl sulfonyl)imide ([Hbet][Tf2N]) and choline bis(trifluoromethylsulfonyl)imide ([choline][Tf2N]) showed that (1) the specific energy of the TRIL-OHE system could reach as high as 4.0 times that of the seawater and river water system, (2) the power density measured from a commercial FO membrane reached up to 2.3 W/m2, and (3) the overall energy efficiency reached up to 2.6% or 18% of the Carnot efficiency at no heat recovery and up to 10.5% or 71% of the Carnet efficiency at 70% heat recovery. All of these results clearly demonstrated the great potential of using TRILs as novel osmotic agents to design high efficient OHEs for recovery of low grade thermal energy to work or electricity.

Question: What is the osmotic heat engine?


An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. Detecting database records that are approximate duplicates, but not exact duplicates, is an important task. Databases may contain duplicate records concerning the same real-world entity because of data entry errors, because of un-standardized abbreviations, or because of diierences in the detailed schemas of records from multiple databases, among other reasons. In this paper, we present an eecient algorithm for recognizing clusters of approximately duplicate records. Three key ideas distinguish the algorithm presented. First, a version of the Smith-Waterman algorithm for computing minimum edit-distance is used as a domain-independent method to recognize pairs of approximately duplicate records. Second, the union//nd algorithm is used to keep track of clusters of duplicate records incrementally, as pairwise duplicate relationships are discovered. Third, the algorithm uses a priority queue of cluster subsets to respond adaptively to the size and homogeneity of the clusters discovered as the database is scanned. This typically reduces by over 75% the number of times that the expensive pair-wise record matching (Smith-Waterman or other) is applied, without impairing accuracy. Comprehensive experiments on synthetic databases and on a real database of bibliographic records connrm the eeectiveness of the new algorithm.

Question: What is the name of the algorithm?


Performance Optimization of a High Current Dual Active Bridge with a Wide Operating Voltage Range. The main aim of this paper is to improve the performance of high current dual active bridge converters when operated over a wide voltage range. A typical application is for fuel cell vehicles where a bi-directional interface between a 12V battery and a high voltage DC bus is required. The battery side voltage ranges from 11V to 16V while the fuel cell is operated between 220V and 447V and the required power is typically 1kW. Careful analysis shows that the high currents on the battery side cause significant design issues in order to obtain a high efficiency. The standard phase shift modulation method can result in high conduction and switching losses. This paper proposes a combined triangular and trapezoidal modulation method to reduce losses over the wide operating range. Approximately, a 2% improvement in efficiency can be expected. An experimental system is used to verify the improved performance of the dual active bridge using the proposed advanced modulation method.

Question: How to improve the performance of a high current dual active bridge converter?


Ontology-based decision making on uncontrolled intersections and narrow roads. Many Advanced Driver Assistance Systems (ADAS) have been developed to improve car safety. However, it is still a challenging problem to make autonomous vehicles to drive safely on urban streets such as uncontrolled intersections (without traffic lights) and narrow roads. In this paper, we introduce a decision making system that can assist autonomous vehicles at uncontrolled intersections and narrow roads. We constructed a machine understandable ontology-based Knowledge Base, which contains maps and traffic regulations. The system makes decisions in comply with traffic regulations such as Right-Of-Way rules when it receives a collision warning signal. The decisions are sent to a path planning system to change the route or stop to avoid collisions.

Question: How to make autonomous vehicles drive safely on urban streets?


Gender Recognition Based on Sift Features. This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consuming pre-processing step in order to alignment in which face images are aligned so that facial landmarks like eyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based on mathematical analysis is represented in three stages that eliminates alignment step. First, a new color based face detection method is represented with a better result and more robustness in complex backgrounds. Next, the features which are invariant to affine transformations are extracted from each face using scale invariant feature transform (SIFT) method. To evaluate the performance of the proposed algorithm, experiments have been conducted by employing a SVM classifier on a database of face images which contains 500 images from distinct people with equal ratio of male and female.

Question: How to detect gender in a face?


Alterations in brain and immune function produced by mindfulness meditation.. OBJECTIVE The underlying changes in biological processes that are associated with reported changes in mental and physical health in response to meditation have not been systematically explored. We performed a randomized, controlled study on the effects on brain and immune function of a well-known and widely used 8-week clinical training program in mindfulness meditation applied in a work environment with healthy employees.   METHODS We measured brain electrical activity before and immediately after, and then 4 months after an 8-week training program in mindfulness meditation. Twenty-five subjects were tested in the meditation group. A wait-list control group (N = 16) was tested at the same points in time as the meditators. At the end of the 8-week period, subjects in both groups were vaccinated with influenza vaccine.   RESULTS We report for the first time significant increases in left-sided anterior activation, a pattern previously associated with positive affect, in the meditators compared with the nonmeditators. We also found significant increases in antibody titers to influenza vaccine among subjects in the meditation compared with those in the wait-list control group. Finally, the magnitude of increase in left-sided activation predicted the magnitude of antibody titer rise to the vaccine.   CONCLUSIONS These findings demonstrate that a short program in mindfulness meditation produces demonstrable effects on brain and immune function. These findings suggest that meditation may change brain and immune function in positive ways and underscore the need for additional research.

Question: What is the difference between a meditation and a mindfulness meditation?


Conceptual modeling for ETL processes. Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the problem of the definition of ETL activities and provide formal foundations for their conceptual representation. The proposed conceptual model is (a) customized for the tracing of inter-attribute relationships and the respective ETL activities in the early stages of a data warehouse project; (b) enriched with a 'palette' of a set of frequently used ETL activities, like the assignment of surrogate keys, the check for null values, etc; and (c) constructed in a customizable and extensible manner, so that the designer can enrich it with his own re-occurring patterns for ETL activities.

Question: What is a conceptual model for ETL processes?


Script independent scene text segmentation using fast stroke width transform and GrabCut. In this paper, a novel script independent scene text segmentation method is presented. The proposed method introduces an approach for character segmentation which combines the advantages of connected component analysis, stroke width analysis and graph cuts. The method was evaluated on a standard dataset, and also on a custom dataset of scene images of text from various Indian scripts. Additionally, a fast implementation of Stroke Width Transform (SWT) is presented here, which improves speed over other implementations by 79%. From our experiments, we have achieved encouraging results on the usefulness of the combination of text/non-text classification with GrabCut. The technique is robust to the language/script of text.

Question: How to segment text in images?


Lévy flights and random searches. In this work we discuss some recent contributions to the random search problem. Our analysis includes superdiffusive Lévy processes and correlated random walks in several regimes of target site density, mobility and revisitability. We present results in the context of mean-field-like and closed-form average calculations, as well as numerical simulations. We then consider random searches performed in regular lattices and lattices with defects, and we discuss a necessary criterion for distinguishing true superdiffusion from correlated random walk processes. We invoke energy considerations in relation to critical survival states on the edge of extinction, and we analyze the emergence of Lévy behavior in deterministic search walks. Finally, we comment on the random search problem in the context of biological foraging. PACS numbers: 05.50.Fb, 05.40.−a

Question: What is the average time to find a target?


An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.. This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.

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


Fast Screening Algorithm for Rotation and Scale Invariant Template Matching. This paper presents a generic pre-processor for expediting conventional template matching techniques. Instead of locating the best matched patch in the reference image to a query template via exhaustive search, the proposed algorithm rules out regions with no possible matches with minimum computational efforts. While working on simple patch features, such as mean, variance and gradient, the fast prescreening is highly discriminative. Its computational efficiency is gained by using a novel octagonal-star-shaped template and the inclusion-exclusion principle to extract and compare patch features. Moreover, it can handle arbitrary rotation and scaling of reference images effectively. Extensive experiments demonstrate that the proposed algorithm greatly reduces the search space while never missing the best match.

Question: What is a template matching algorithm?


Preputial flaps to correct buried penis. The authors developed a preputial skin flap technique to correct the buried penis which was simple and practical. This simple procedure can be applied to most boys with buried penis. In the last 3 years, we have seen 12 boys with buried penis and have been treated by using preputial flaps. The mean age is about 5.1 (from 3 to 12). By making a longitudinal incision on the ventral side of penis, the tightness of the foreskin is released and leave a diamond-shaped skin defect. It allows the penile shaft to extend out. A circumferential incision is made about 5 mm proximal to the coronal sulcus. Pedicled preputial flaps are obtained leaving optimal penile skin on the dorsal side. The preputial skin flaps are rotated onto the ventral side and tailored to cover the defect. All patients are followed for at least 3 months. Edema and swelling on the flaps are common, but improves with time. None of our patients need a second operation. The preputial flaps technique is a simple technique which allows surgeons to deal with most cases of buried penis by tailoring the flaps providing good cosmetic and functional results.

Question: How to correct buried penis?


WebWatcher : A Tour Guide for the World Wide Web. 1 We explore the notion of a tour guide software agent for assisting users browsing the World Wide Web. A Web tour guide agent provides assistance similar to that provided by a human tour guide in a museum { it guides the user along an appropriate path through the collection, based on its knowledge of the user's interests, of the location and relevance of various items in the collection, and of the way in which others have interacted with the collection in the past. This paper describes a simple but operational tour guide, called WebWatcher, which has given over 5000 tours to people browsing CMU's School of Computer Science Web pages. WebWatcher accompanies users from page to page, suggests appropriate hyperlinks, and learns from experience to improve its advice-giving skills. We describe the learning algorithms used by WebWatcher, experimental results showing their e ectiveness, and lessons learned from this case study in Web tour guide agents.

Question: What is a tour guide software agent?


Regional differences in synaptogenesis in human cerebral cortex.. The formation of synaptic contacts in human cerebral cortex was compared in two cortical regions: auditory cortex (Heschl's gyrus) and prefrontal cortex (middle frontal gyrus). Synapse formation in both cortical regions begins in the fetus, before conceptual age 27 weeks. Synaptic density increases more rapidly in auditory cortex, where the maximum is reached near postnatal age 3 months. Maximum synaptic density in middle frontal gyrus is not reached until after age 15 months. Synaptogenesis occurs concurrently with dendritic and axonal growth and with myelination of the subcortical white matter. A phase of net synapse elimination occurs late in childhood, earlier in auditory cortex, where it has ended by age 12 years, than in prefrontal cortex, where it extends to midadolescence. Synaptogenesis and synapse elimination in humans appear to be heterochronous in different cortical regions and, in that respect, appears to differ from the rhesus monkey, where they are concurrent. In other respects, including overproduction of synaptic contacts in infancy, persistence of high levels of synaptic density to late childhood or adolescence, the absolute values of maximum and adult synaptic density, and layer specific differences, findings in the human resemble those in rhesus monkeys.

Question: What is the difference between the human and the rhesus monkey?


Human activity analysis: A review. Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach.  Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas.

Question: What is the difference between a simple action and a high-level activity?