View-based and modular eigenspaces for face recognition. In this work we describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated.

Question: How to recognize faces under general viewing orientation?


Search Based Software Engineering. The use of evolutionary algorithms for solving multi-objective optimization problems has become increasingly popular, mainly within the last 15 years. From among the several research trends that have originated in recent years, one of the most promising is the use of hybrid approaches that allow to improve the performance of multi-objective evolutionary algorithms (MOEAs). In this talk, some of the most representative research on the use of hybrid approaches in evolutionary multi-objective optimization will be discussed. The topics discussed will include multi-objective memetic algorithms, hybridization of MOEAs with gradient-based methods and with direct search methods, as well as multi-objective hyperheuristics. Some applications of these approaches as well as some potential paths for future research in this area will also be briefly discussed. Towards Evolutionary Multitasking: A New Paradigm

Question: What is a new paradigm?


Physical Demands of Different Positions in FA Premier League Soccer.. The purpose of this study was to evaluate the physical demands of English Football Association (FA) Premier League soccer of three different positional classifications (defender, midfielder and striker). Computerised time-motion video-analysis using the Bloomfield Movement Classification was undertaken on the purposeful movement (PM) performed by 55 players. Recognition of PM had a good inter-tester reliability strength of agreement (κ= 0.7277). Players spent 40.6 ± 10.0% of the match performing PM. Position had a significant influence on %PM time spent sprinting, running, shuffling, skipping and standing still (p < 0.05). However, position had no significant influence on the %PM time spent performing movement at low, medium, high or very high intensities (p > 0.05). Players spent 48.7 ± 9.2% of PM time moving in a directly forward direction, 20.6 ± 6.8% not moving in any direction and the remainder of PM time moving backward, lateral, diagonal and arced directions. The players performed the equivalent of 726 ± 203 turns during the match; 609 ± 193 of these being of 0° to 90° to the left or right. Players were involved in the equivalent of 111 ± 77 on the ball movement activities per match with no significant differences between the positions for total involvement in on the ball activity (p > 0.05). This study has provided an indication of the different physical demands of different playing positions in FA Premier League match-play through assessment of movements performed by players. Key pointsPlayers spent ~40% of the match performing Pur-poseful Movement (PM).Position had a significant influence on %PM time spent performing each motion class except walking and jogging. Players performed >700 turns in PM, most of these being of 0°-90°.Strikers performed most high to very high intensity activity and most contact situations.Defenders also spent a significantly greater %PM time moving backwards tha

Question: What is the difference between a defender and a midfielder?


Why does the microbiome affect behaviour?. Growing evidence indicates that the mammalian microbiome can affect behaviour, and several symbionts even produce neurotransmitters. One common explanation for these observations is that symbionts have evolved to manipulate host behaviour for their benefit. Here, we evaluate the manipulation hypothesis by applying evolutionary theory to recent work on the gut–brain axis. Although the theory predicts manipulation by symbionts under certain conditions, these appear rarely satisfied by the genetically diverse communities of the mammalian microbiome. Specifically, any symbiont investing its resources to manipulate host behaviour is expected to be outcompeted within the microbiome by strains that do not manipulate and redirect their resources into growth and survival. Moreover, current data provide no clear evidence for manipulation. Instead, we show how behavioural effects can readily arise as a by-product of natural selection on microorganisms to grow within the host and natural selection on hosts to depend upon their symbionts. We argue that understanding why the microbiome influences behaviour requires a focus on microbial ecology and local effects within the host. The microbiota can influence host behaviour through the gut–brain axis. In this Opinion, Johnson and Foster explore the evolution of this relationship and propose that adaptations of competing gut microorganisms may affect behaviour as a by‑product, leading to host dependence.

Question: Why does the microbiome affect behaviour?


Sovrin : digital identities in the blockchain era. In this paper we describe a practical digital identity project of a global scale, which solves a number of privacy and scalability problems using the concepts of anonymous credentials and permissioned blockchains. Even though both ideas have been known and developed for a number of years, our system appears to be the first amalgamation. The prototype has been developed, tested, and published as open source. I. DIGITAL IDENTITY: HISTORY AND CHALLENGES The idea of digital identity can be traced to the beginning of the computer era, when the user profiles were first transferred across networks. In the real world, different authorities issue licenses, passports, identification cards, credit cards, checks, etc., generally called credentials, to ordinary users for their presentation to various service or goods providers. This brings the concept of Issuer, User, and Verifier as subjects of the identity system. It immediately became clear that issuance, storage, and presentation procedures must follow a number of security requirements to be competitive with the traditional paper credentials: • Compatibility. Credentials issued by different Issuers can be combined for a single presentation by User who is required to prove several properties simultaneously: e.g. that he is at least age 18 employed by XYZ Corp. with a driver license and good eyesight (does not require corrective lenses). • Unforgeability. A malicious User is unable to present a credential not issued by a legitimate Issuer. • Scalability. A system must handle hundreds of Issuers and billions of Users, such as an international passport system. It might be required to handle thousands of transactions per second like the Visa system [1]. • Performance/Low latency. Verification of credentials must be almost instant and not require broadband network connectivity per credential, even if it is issued by a previously unknown Issuer. Credentials can be stored and proces

Question: What is the difference between a digital identity and a digital certificate?


Chapter 2 : Game Analytics – The Basics. Take Away Points:  Overview of important key terms in game analytics  Introduction to game telemetry as a source of business intelligence.  In-depth description and discussion of user-derived telemetry and metrics.  Introduction to feature selection in game analytics  Introduction to the knowledge discovery process in game analytics  References to essential further reading. 1 Analytics – a new industry paradigm Developing a profitable game in today's market is a challenging endeavor. Thousands of commercial titles are published yearly, across a number of hardware platforms and distribution channels, all competing for players' time and attention, and the game industry is decidedly competitive. In order to effectively develop games, a variety of tools and techniques from e.g. business practices, project management to user testing have been developed in the game industry, or adopted and adapted from other IT sectors. One of these methods is analytics, which in recent years has decidedly impacted the game industry and game research environment. Analytics is the process of discovering and communicating patterns in data, towards solving problems in business or conversely predictions for supporting enterprise decision management, driving action and/or improving performance. The meth-odological foundations for analytics are statistics, data mining, mathematics, programming and operations research, as well as data visualization in order to communicate insights learned to the relevant stakeholders. Analytics is not just the querying and reporting of BI data, but rests on actual analysis, e.g. statistical analysis, predic-tive modeling, optimization, forecasting, etc. (Davenport and Harris, 2007). Analytics typically relies on computational modeling. There are several branches or domains of analytics, e.g. marketing analytics, risk analytics, web analytics – and game analytics. Importantly, analytics is not the same thing as data

Question: What is analytics?


A Comparative Analysis of Enterprise Architecture Frameworks Based on EA Quality Attributes. Many Enterprise Architecture Frameworks (EAFs) are in use today to guide or serve as a model for the enterprise architecture development. Researchers offer general comparative information about EAF. However, there has been little work on the quality of Enterprise Architecture (EA). This study provides the characteristics of the five EAFs using comparative analysis based on Enterprise Architecture Quality Attributes (EAQAs). The attribute of EA quality was extracted from the EAFs and the definition of EAQA was defined from EA user viewpoint. The criteria for each quality attribute were developed to compare EAFs using four dimensional concepts. This paper compared several frameworks using the criteria to provide guidance in the selection of an EAF that meets the quality requirement of EA

Question: What is the definition of EAQA?


Normalized Cuts and Image Segmentation. w e propose Q novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the amage data, our approach aims a t extracting the global impression of an image. We treat image segmentation QS (I graph partitioning problem and propose Q novel global criterion, the normalized cut, for segmenting the graph. The normalized cut craterion measures both the total dissimilarity between the different groups QS well as the total similarity within the groups. We show that an eficient computational technique based on a generaked eigenvalue problem can be used to optimize this criterion. w e have applied this approach to segmenting static images and found results very enco u raging.

Question: What is the normalized cut?


A Prospective, Open-Label Study of Hyaluronic Acid-Based Filler With Lidocaine (VYC-15L) Treatment for the Correction of Infraorbital Skin Depressions.. BACKGROUND Infraorbital skin depressions are one of the most troublesome facial areas for aesthetically aware patients.   OBJECTIVE Evaluate effectiveness and safety of Juvéderm Volbella with Lidocaine (VYC-15L; Allergan plc, Dublin, Ireland) for correction of bilateral infraorbital depressions.   METHODS In this 12-month, prospective, uncontrolled, open-label study, subjects aged ≥18 years with infraorbital depressions rated ≥1 on the Allergan Infra-oRbital Scale (AIRS) received injections of VYC-15L with optional touch-up treatment on Day 14. The primary efficacy measure was ≥1 AIRS grade improvement from baseline at month 1.   RESULTS Of 80 subjects initially treated with VYC-15L, 75 (94%) completed the study. All injections were intentionally deep, most using multiple microbolus technique. At 1 month, 99.3% of eyes achieved ≥1 AIRS grade improvement. The responder rate (subjects with ≥1 AIRS grade improvement in both eyes) was 99% at month 1, 92% at month 6, and 54% at month 12. Most injection site reactions (e.g., bruising, redness, irregularities/bumps) were mild and resolved by day 14. Late-onset mild to moderate edema was observed in 11% of eyes at month 6% and 4% of eyes at month 12.   CONCLUSION VYC-15L is effective and safe for the treatment of infraorbital depressions, with effectiveness lasting up to 12 months.

Question: What is the effectiveness of Juvéderm Volbella with Lidocaine (VYC-15L) for the correction of infraorbital depressions?


Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer. Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.

Question: What is the largest corpus for formality style transfer?


Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research. With the recent advent of high-throughput technologies for both compound synthesis and biological screening, there is no shortage of publicly or commercially available data sets and databases that can be used for computational drug discovery applications (reviewed recently in Williams et al.). Rapid growth of large, publicly available databases (such as PubChem or ChemSpider containing more than 20 million molecular records each) enabled by experimental projects such as NIH's Molecular Libraries and Imaging Initiative provides new opportunities for the development of cheminformatics methodologies and their application to knowledge discovery in molecular databases. A fundamental assumption of any cheminformatics study is the correctness of the input data generated by experimental scientists and available in various data sets. Nevertheless, a recent study showed that on average there are two errors per each medicinal chemistry publication with an overall error rate for compounds indexed in the WOMBAT database as high as 8%. In another recent study, the authors investigated several public and commercial databases to calculate their error rates: the latter were ranging from 0.1 to 3.4% depending on the database. How significant is the problem of accurate structure representation (given that the error rates in current databases may appear relatively low) since it concerns exploratory cheminformatics and molecular modeling research? Recent investigations by a large group of collaborators from six laboratories have clearly demonstrated that the type of chemical descriptors has much greater influence on the prediction performance of QSAR models than the nature of model optimization techniques. These findings suggest that having erroneous structures represented by erroneous descriptors should have a detrimental effect on model performance. Indeed, a recent semin

Question: What is the difference between a chemical structure and a chemical descriptor?


Finding and tracking road lanes using "line-snakes". This paper presents a method forfinding and tracking road lanes. The method extracts and tracks lane boundaries for vision-guided vehicle navigation by combining the hough transform and the "active line model ( A M ) " . The hough transform can extract vanishing points of the road, which can be used as a good estimation of the vehicle heading. For the curved road, however, the estimation may be too crude to be used for navigation. Therefore, the hough transform is used to obtain an initial position estimation of the lane boundaries on the road. The line snake ALM then improves the initial approximation to an accurate configuration of the lane boundaries. Once the line snake is initialized in the first image, it tracks the road lanes using the external and internal forces computed from images and a proposed boundary refinement technique. Specifically, an image region is divided into a few subregions along the vertical direction. The hough transform is then performed for each sub-region and candidate lines of road lanes in each sub-region are extracted. Among candidate lines, a most prominent line is found by the ALM that minimizes a defined snake energy. The external energy of ALM is a normalized sum of image gradients along the line. The internal deformation energy ensures the continuity of two neighboring lines by using the angledifference between two adjacent lines and the distance between the two lines. A search method based on the dynamic programming reduces the computational cost. The proposed method gives a uniJied framework for detecting, refining and tracking the road lane. Experimental results using images of a real road scene are presented.

Question: How to find road lanes using hough transform?


Learning to reinforcement learn. In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.

Question: What is meta-reinforcement learning?


ACES: Automatic Compartments for Embedded Systems. Securing the rapidly expanding Internet of Things (IoT) is critical. Many of these "things" are vulnerable baremetal embedded systems where the application executes directly on hardware without an operating system. Unfortunately, the integrity of current systems may be compromised by a single vulnerability, as recently shown by Google's P0 team against Broadcom's WiFi SoC. We present ACES (Automatic Compartments for Embedded Systems), an LLVM-based compiler that automatically infers and enforces inter-component isolation on bare-metal systems, thus applying the principle of least privileges. ACES takes a developer-specified compartmentalization policy and then automatically creates an instrumented binary that isolates compartments at runtime, while handling the hardware limitations of baremetal embedded devices. We demonstrate ACES' ability to implement arbitrary compartmentalization policies by implementing three policies and comparing the compartment isolation, runtime overhead, and memory overhead. Our results show that ACES' compartments can have low runtime overheads (13% on our largest test application), while using 59% less Flash, and 84% less RAM than the Mbed μVisor—the current state-of-theart compartmentalization technique for bare-metal systems. ACES ' compartments protect the integrity of privileged data, provide control-flow integrity between compartments, and reduce exposure to ROP attacks by 94.3% compared to μVisor.

Question: What is the current state of the art in compartmentalization for bare-metal systems?


On Human Motion Prediction Using Recurrent Neural Networks. Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state of the art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which results in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.

Question: What is the state of the art in human motion prediction?


Design and Analysis of a Two Stage Operational Amplifier for High Gain and High Bandwidth. In this paper a design and comparison between a fully differential RC Miller compensated CMOS op-amp and conventional op-amp is presented. High gain enables the circuit to operate efficiently in a closed loop feedback system, whereas high bandwidth makes it suitable for high speed applications. A novel RC Miller compensation technique is used to optimize the parameters of gain and bandwidth for high speed applications are illustrated in this research work. The design is also able to address any fluctuation in supply or dc input voltages and stabilizes the operation by nullifying. The design is implemented on TSMC 0.18 m CMOS process at 3.3 V as supply voltage under room temperature 27 C. The simulated result shows that a unity gain bandwidth of 136.8 MHz with a high gain of 92.27 dB is achieved for the proposed op-amp circuit. The total areas of the layouts are 0.000158 mm and 0.000532 mm for conventional and proposed respectively. Key wrods: Two-Stage Op-amp, fully differential, high gain, high bandwidth

Question: What is the difference between a conventional op-amp and a two-stage op-amp?


DEEP LEARNING AND INTELLIGENT AUDIO MIXING. Mixing multitrack audio is a crucial part of music production. With recent advances in machine learning techniques such as deep learning, it is of great importance to conduct research on the applications of these methods in the field of automatic mixing. In this paper, we present a survey of intelligent audio mixing systems and their recent incorporation of deep neural networks. We propose to the community a research trajectory in the field of deep learning applied to intelligent music production systems. We conclude with a proof of concept based on stem audio mixing as a contentbased transformation using a deep autoencoder.

Question: What is the difference between stem audio mixing and intelligent audio mixing?


Bringing PageRank to the citation analysis. The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google's PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications' influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000–2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded. 2007 Elsevier Ltd. All rights reserved.

Question: What is the distribution of the PageRank of scientific papers?


Vertical distance from the crest of bone to the height of the interproximal papilla between adjacent implants.. BACKGROUND As patient demand increases for more natural restorations in the esthetic zone, clinicians must have the highest level of skill and knowledge to maintain or reform the interdental papilla between teeth, between implants and teeth, and between adjacent implants. To date, there are no reports that have measured the distance from the contact point to the bony crest between implants. One reason for this may be the fact that, with two adjacent implants, the contact point of the crown can be established at any distance from the gingival margin according to the restorative dentist's specifications. Therefore, in this study, the height of the soft tissue to the crest of bone was measured between two adjacent implants independent of the location of the contact point. The purpose of this study was to determine the range and average height of tissue between two adjacent implants.   METHODS A total of 136 interimplant papillary heights were examined in 33 patients by eight different examiners in five private dental offices. After administration of appropriate local anesthesia, a standardized periodontal probe was placed vertically from the height of the papilla to the crest of bone. The measurements were rounded off to the nearest millimeter.   RESULTS The mean height of papillary tissue between two adjacent implants was 3.4 mm, with a range of 1 mm to 7 mm.   CONCLUSIONS Clinicians should proceed with great caution when placing two implants adjacent to each other in the esthetic zone. In most cases, only 2, 3, or 4 mm of soft tissue height (average 3.4 mm) can be expected to form over the interimplant crest of bone. These results showed that modification of treatment plans may be necessary when esthetics are critical for success.

Question: How tall is the soft tissue between two adjacent implants?


Screen Printing of Highly Loaded Silver Inks on Plastic Substrates Using Silicon Stencils.. Screen printing is a potential technique for mass-production of printed electronics; however, improvement in printing resolution is needed for high integration and performance. In this study, screen printing of highly loaded silver ink (77 wt %) on polyimide films is studied using fine-scale silicon stencils with openings ranging from 5 to 50 μm wide. This approach enables printing of high-resolution silver lines with widths as small as 22 μm. The printed silver lines on polyimide exhibit good electrical properties with a resistivity of 5.5×10(-6) Ω cm and excellent bending tolerance for bending radii greater than 5 mm (tensile strains less than 0.75%).

Question: What is the resistivity of silver?


Accurate design of corrugated waveguide low-pass filters using exclusively closed-form expressions. A simple, quasi-analytical, and accurate method to design classical corrugated rectangular waveguide low-pass filters is presented in this paper. Unlike in other published techniques, the dimensions of the final filter are directly obtained in a quasi-analytical manner following exclusively closed-form expressions and, in particular, using neither electromagnetic simulation nor optimization. Thus, our method is amenable to be implemented using an off-the-shelf mathematical software tool such as Matlab. The proposed method is based on the synthesis of the heights and lengths of the final structure taking into account the shunt capacitance effect (produced by the excitation of higher-order modes at the height steps) over the magnitude and phase of the local reflection coefficients. The novel method has been validated with the design of a classical Chebyshev corrugated low-pass filter of order 31, which shows a remarkable accordance between the ideal and simulated frequency responses.

Question: How to design a corrugated waveguide low-pass filter?


Clustering Based Approach for Customers ' Classification from Electrical Distribution Systems. The paper presents a clustering based approach for consumers' classification in representative categories characterized by typical load profiles. For determination of the consumption categories, every customer must be characterized by the following primary information: daily (monthly) energy consumption, average load and peak load. The used database contains the daily load curves corresponding to the small customers from a rural distribution system from Romania. The obtained results it demonstrated that the proposed approach can be used with the success in building specific tariff structures of the customers or in the optimal operation and planning of distribution systems.

Question: How to classify customers in representative categories?


Facial performance sensing head-mounted display. There are currently no solutions for enabling direct face-to-face interaction between virtual reality (VR) users wearing head-mounted displays (HMDs). The main challenge is that the headset obstructs a significant portion of a user's face, preventing effective facial capture with traditional techniques. To advance virtual reality as a next-generation communication platform, we develop a novel HMD that enables 3D facial performance-driven animation in real-time. Our wearable system uses ultra-thin flexible electronic materials that are mounted on the foam liner of the headset to measure surface strain signals corresponding to upper face expressions. These strain signals are combined with a head-mounted RGB-D camera to enhance the tracking in the mouth region and to account for inaccurate HMD placement. To map the input signals to a 3D face model, we perform a single-instance offline training session for each person. For reusable and accurate online operation, we propose a short calibration step to readjust the Gaussian mixture distribution of the mapping before each use. The resulting animations are visually on par with cutting-edge depth sensor-driven facial performance capture systems and hence, are suitable for social interactions in virtual worlds.

Question: What is the difference between a head-mounted display and a virtual reality headset?


An Open Smart City IoT Test Bed: Street Light Poles as Smart City Spines: Poster Abstract. Street light poles will be a key enabler for a smart city's hardware infrastructure, thanks to their ubiquity throughout the city as well as access to power. We propose an IoT test bed around light poles for the city, with a modular hardware and software architecture to enable experimentation with various technologies.

Question: What is a smart city?


Beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array. By employing the lens antenna array, beamspace MIMO can utilize beam selection to reduce the number of required RF chains in mmWave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beamspace channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. Specifically, we propose to decompose the total beamspace channel estimation problem into a series of sub-problems, each of which only considers one sparse channel component. For each channel component, we first reliably detect its support by utilizing the structural characteristics of mmWave beamspace channel. Then, the influence of this channel component is removed from the total beamspace channel estimation problem. After the supports of all channel components have been detected, the nonzero elements of the sparse beamspace channel can be estimated with low pilot overhead. Simulation results show that the proposed SD-based channel estimation outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region.

Question: How to detect the support of a sparse beamspace channel?


Understanding and Predicting Graded Search Satisfaction. Understanding and estimating satisfaction with search engines is an important aspect of evaluating retrieval performance. Research to date has modeled and predicted search satisfaction on a binary scale, i.e., the searchers are either satisfied or dissatisfied with their search outcome. However, users' search experience is a complex construct and there are different degrees of satisfaction. As such, binary classification of satisfaction may be limiting. To the best of our knowledge, we are the first to study the problem of understanding and predicting graded (multi-level) search satisfaction. We ex-amine sessions mined from search engine logs, where searcher satisfaction was also assessed on multi-point scale by human annotators. Leveraging these search log data, we observe rich and non-monotonous changes in search behavior in sessions with different degrees of satisfaction. The findings suggest that we should predict finer-grained satisfaction levels. To address this issue, we model search satisfaction using features indicating search outcome, search effort, and changes in both outcome and effort during a session. We show that our approach can predict subtle changes in search satisfaction more accurately than state-of-the-art methods, affording greater insight into search satisfaction. The strong performance of our models has implications for search providers seeking to accu-rately measure satisfaction with their services.

Question: What is the difference between binary and multi-level classification?


Ischiofemoral Impingement Syndrome. Ischiofemoral impingement syndrome is known as one of the causes of hip pain due to impingement of ischium and femur, and usually correlated with trauma or operation. We report a rare case of ischiofemoral impingement syndrome that has no history of trauma or surgery. A 48-year-old female patient was referred for 2 months history of the left hip pain, radiating to lower extremity with a hip snapping sensation. She had no history of trauma or surgery at or around the hip joint and femur. The magnetic resonance imaging (MRI) of the lumbar spine showed no abnormality, except diffuse bulging disc without cord compression at the lumbosacral area. Electrophysiologic study was normal, and there were no neurologic abnormalities compatible with the lumbosacral radiculopathy or spinal stenosis. Hip MRI revealed quadratus femoris muscle edema with concurrent narrowing of the ischiofemoral space. The distance of ischiofemoral space and quadratus femoris space were narrow. It was compatible with ischiofemoral impingement syndrome. After treatment with nonsteroidal anti-inflammatory drugs, physical therapy, and exercise program, the patient's pain was relieved and the snapping was improved. To our knowledge, this is the first reported case of a nontraumatic, noniatrogenic ischiofemoral impingement syndrome, and also the first case to be treated by a nonsurgical method in the Republic of Korea.

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


Online Dictionary Learning on Symmetric Positive Definite Manifolds with Vision Applications. Symmetric Positive Definite (SPD) matrices in the form of region covariances are considered rich descriptors for images and videos. Recent studies suggest that exploiting the Riemannian geometry of the SPD manifolds could lead to improved performances for vision applications. For tasks involving processing large-scale and dynamic data in computer vision, the underlying model is required to progressively and efficiently adapt itself to the new and unseen observations. Motivated by these requirements, this paper studies the problem of online dictionary learning on the SPD manifolds. We make use of the Stein divergence to recast the problem of online dictionary learning on the manifolds to a problem in Reproducing Kernel Hilbert Spaces, for which, we develop efficient algorithms by taking into account the geometric structure of the SPD manifolds. To our best knowledge, our work is the first study that provides a solution for online dictionary learning on the SPD manifolds. Empirical results on both large-scale image classification task and dynamic video processing tasks validate the superior performance of our approach as compared to several state-of-the-art algorithms.

Question: What is the difference between the Riemannian geometry of the SPD manifolds and the Riemannian geometry of the Euclidean space?


Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition methods update a small subset of dual variables by solving a restricted optimization problem. In this paper, we propose an exact and efficient method for solving the restricted problem. In our method, the restricted problem is reduced to the well-known problem of Euclidean projection onto the positive simplex, which we can solve exactly in expected O(k) time, where k is the number of classes. We demonstrate that our method empirically achieves state-of-the-art convergence on several large-scale high-dimensional datasets.

Question: What is the state-of-the-art for training multiclass SVMs?


Capacitive coupling for power and data telemetry to implantable biomedical microsystems. This paper proposes a new method for power telemetry and wireless data transfer to biomedical implants. Confinement of the transferred energy within a predefined area, hence not influencing the neighboring sensitive circuitry on the microsystem, and also allowing for more than one telemetry link on the same platform and even operating at the same carrier frequency are among the advantages of the proposed method over the traditional approaches for power and data telemetry.

Question: How does capacitive coupling work?


Processing capacity defined by relational complexity: implications for comparative, developmental, and cognitive psychology.. Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A binary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by "segmentation" or "conceptual chunking." In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are "collapsed" to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed.

Question: What is the complexity of relations?


A survey of sparse matrix-vector multiplication performance on large matrices. One of the main sources of sparse matrices is the discretization of partial differential equations that govern continuumphysics phenomena such as fluid flow and transport, phase separation, mechanical deformation, electromagnetic wave propagation, and others. Recent advances in high-performance computing area have been enabling researchers to tackle increasingly larger problems leading to sparse linear systems with hundreds of millions to a few tens of billions of unknowns, e.g., [5, 6]. Iterative linear solvers are popular in large-scale computing as they consume less memory than direct solvers. Contrary to direct linear solvers, iterative solvers approach the solution gradually requiring the computation of sparse matrixvector (SpMV) products. The evaluation of SpMV products can emerge as a bottleneck for computational performance within the context of the simulation of large problems. In this work, we focus on a linear system arising from the discretization of the Cahn–Hilliard equation, which is a fourth order nonlinear parabolic partial differential equation that governs the separation of a two-component mixture into phases [3]. The underlying spatial discretization is performed using the discontinuous Galerkin method (e.g. [10]) and Newton's method. A number of parallel algorithms and strategies have been evaluated in this work to accelerate the evaluation of SpMV products.

Question: What is the discretization of the cahn-hilliard equation?


Social Collaborative Viewpoint Regression with Explainable Recommendations. A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction.  In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To this end, we use so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. In addition, such viewpoints can be used as explanations. We apply a Gibbs EM sampler to infer posterior distributions of sCVR. Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations.

Question: What is a latent variable model?


The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other databases. Recent developments of the database include format and content enhancements, cross-references to additional databases, new documentation files and improvements to TrEMBL, a computer-annotated supplement to SWISS-PROT. TrEMBL consists of entries in SWISS-PROT-like format derived from the translation of all coding sequences (CDSs) in the EMBL Nucleotide Sequence Database, except the CDSs already included in SWISS-PROT. We also describe the Human Proteomics Initiative (HPI), a major project to annotate all known human sequences according to the quality standards of SWISS-PROT. SWISS-PROT is available at: http://www.expasy.ch/sprot/ and http://www.ebi.ac.uk/swissprot/

Question: What is the Swiss-Prot protein sequence database?


Using Distributed Scrum for Supporting an Online Community-A Qualitative Descriptive Study of Students ' Perceptions. One purpose of higher education is to prepare students for a modern and ever-changing global society characterized by increasing complexity and collaborative environments. Scrum is an agile, widely used framework for project management dealing with the development of complex products. Scrum projects are conducted in small, empowered teams with intense communication, interaction and collaboration between the team members, facilitated by a servant-leader Scrum master. Scrum has been commonly used in professional software development and is also now being adopted in other areas, including education. There have been few studies of the application of Scrum in higher education and very few of them have studied distributed Scrum in an online context. An online learning community has several positive effects for students such as increased learning, engagement, retention and lower risks for isolation and dropouts. Participating in and contributing to a team is dependent on a sense of community, which can be difficult to build up in a distributed environment where members are geographically dispersed and do not have the possibility to meet and communicate face to face. This study examines to what extent and how distributed Scrum can support building an online learning community, from a student perspective. Twenty students, enrolled in an online course in distributed software development, participated in four Scrum projects as members of distributed Scrum teams, each team consisting of five students. Students' perceptions were investigated by conducting semi-structured interviews. The interview transcripts were analyzed according to Rovai's four dimensions of a classroom community. The results indicate that students were very satisfied with their distributed Scrum projects and that they experienced a high degree of flexibility during the projects. The Scrum pro

Question: How can distributed Scrum support building an online learning community?


Political Facebook groups: Micro-activism and the digital front stage. The growth of social networking sites (SNS), like Facebook, has caused many to rethink how we understand political activism and citizen engagement. In 2010, 43% of Internet users reported using social networking sites " several times a day, " a sizeable increase from 2009 where only 34% reported using social networking at the same rate (Pew 2010). Research on digital activism has emphasized the use of websites, listservs, and other forums by formal organizations to raise awareness for their specific causes (McCaughey and Ayers 2003; Van de Donk 2004). Information communication technologies (ICT's) have been regarded as particularly effective tools in mobilizing disparate, resource-poor counter-spheres in collective off-line activity (Van de Donk 2004). In recent years, scholars have moved away from viewing information communication technologies (ICT's) as simply a tool for formal activist organizations. Breindl (2010) argues that on-line digital activism has largely been examined through a classic " social movement paradigm. " She calls for scholarship that looks at how the meaning and structure of activism has been transformed by ICT's. One key way it has been transformed is by allowing for small scale, many-to-many forms of politically oriented communication, what I conceptualize as a new term: microactivism. Examples include the formation of political Facebook groups, re-tweeting of articles of political interest and sharing politically relevant videos on YouTube. These acts reflect micro-level intentions and are not necessarily geared towards mobilization like more traditional forms of digital activism. This microactivism has helped bring about a radical reformulation of the political arena. One major change that emerges from the ease of many-to-many communication brought about by SMS is the reduced incentive to join social movements (Earl and Schussman 2003). These new applications encourage

Question: What is micro-activism?


A Survey of CPU-GPU Heterogeneous Computing Techniques. As both CPUs and GPUs become employed in a wide range of applications, it has been acknowledged that both of these Processing Units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated a significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this article, we survey Heterogeneous Computing Techniques (HCTs) such as workload partitioning that enable utilizing both CPUs and GPUs to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler, and application levels. Further, we review both discrete and fused CPU-GPU systems and discuss benchmark suites designed for evaluating Heterogeneous Computing Systems (HCSs). We believe that this article will provide insights into the workings and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.

Question: What is the scope of heterogeneous computing?


Multi-Objective Optimization of 50 kW/85 kHz IPT System for Public Transport. Inductive power transfer (IPT) is an attractive solution for the automated battery charging of public transport electric vehicles (EVs) with low maintenance requirements. This paper presents the design of a 50 kW/85 kHz contactless EV charger, with a focus on the IPT transmitter and receiver coils. The multi-objective magnetics optimization reveals the Pareto tradeoffs and performance limitations in terms of high efficiency, high power density, and low stray field for high-power IPT systems without moving mechanical parts. A full-scale hardware prototype is realized and experimentally investigated. The dc-dc conversion efficiency, including all the power electronics stages, is measured as 95.8% at 50 kW power transfer across an air gap of 160 mm (coil dimensions 410 × 760 × 60 mm3). With 150 mm coil misalignment in the worst case direction, the dc-dc efficiency drops to 92%. The measurements of the magnetic stray field show compliance with ICNIRP 2010 at 800 mm distance from the IPT coil center.

Question: How to design a 50 kW/85 kHz IPT system for public transport?


Automatic multilabel classification for Indonesian news articles. Problem transformation and algorithm adaptation are the two main approaches in machine learning to solve multilabel classification problem. The purpose of this paper is to investigate both approaches in multilabel classification for Indonesian news articles. Since this classification deals with a large number of features, we also employ some feature selection methods to reduce feature dimension. There are four factors as the focuses of this paper, i.e., feature weighting method, feature selection method, multilabel classification approach, and single-label classification algorithm. These factors will be combined to determine the best combination. The experiments show that the best performer for multilabel classification of Indonesian news articles is the combination of TF-IDF feature weighting method, Symmetrical Uncertainty feature selection method, Calibrated Label Ranking - which belongs to problem transformation approach -, and SVM algorithm. This best combination achieves F-measure of 85.13% in 10-fold cross-validation, but the F-measure decreases to 76.73% in testing because of OOV.

Question: What is the best combination of feature weighting method, feature selection method, multilabel classification approach, and single-label classification algorithm?


Delineation of line patterns in images using B-COSFIRE filters. Delineation of line patterns in images is a basic step required in various applications such as blood vessel detection in medical images, segmentation of rivers or roads in aerial images, detection of cracks in walls or pavements, etc. In this paper we present trainable B-COSFIRE filters, which are a model of some neurons in area V1 of the primary visual cortex, and apply it to the delineation of line patterns in different kinds of images. B-COSFIRE filters are trainable as their selectivity is determined in an automatic configuration process given a prototype pattern of interest. They are configurable to detect any preferred line structure (e.g. segments, corners, cross-overs, etc.), so usable for automatic data representation learning. We carried out experiments on two data sets, namely a line-network data set from INRIA and a data set of retinal fundus images named IOSTAR. The results that we achieved confirm the robustness of the proposed approach and its effectiveness in the delineation of line structures in different kinds of images

Question: What is the difference between a line-network data set and a data set of retinal fundus images?


Obesity is associated with macrophage accumulation in adipose tissue.. Obesity alters adipose tissue metabolic and endocrine function and leads to an increased release of fatty acids, hormones, and proinflammatory molecules that contribute to obesity associated complications. To further characterize the changes that occur in adipose tissue with increasing adiposity, we profiled transcript expression in perigonadal adipose tissue from groups of mice in which adiposity varied due to sex, diet, and the obesity-related mutations agouti (Ay) and obese (Lepob). We found that the expression of 1,304 transcripts correlated significantly with body mass. Of the 100 most significantly correlated genes, 30% encoded proteins that are characteristic of macrophages and are positively correlated with body mass. Immunohistochemical analysis of perigonadal, perirenal, mesenteric, and subcutaneous adipose tissue revealed that the percentage of cells expressing the macrophage marker F4/80 (F4/80+) was significantly and positively correlated with both adipocyte size and body mass. Similar relationships were found in human subcutaneous adipose tissue stained for the macrophage antigen CD68. Bone marrow transplant studies and quantitation of macrophage number in adipose tissue from macrophage-deficient (Csf1op/op) mice suggest that these F4/80+ cells are CSF-1 dependent, bone marrow-derived adipose tissue macrophages. Expression analysis of macrophage and nonmacrophage cell populations isolated from adipose tissue demonstrates that adipose tissue macrophages are responsible for almost all adipose tissue TNF-alpha expression and significant amounts of iNOS and IL-6 expression. Adipose tissue macrophage numbers increase in obesity and participate in inflammatory pathways that are activated in adipose tissues of obese individuals.

Question: What is the relationship between obesity and macrophages?


The Wnt signaling pathway in development and disease.. Tight control of cell-cell communication is essential for the generation of a normally patterned embryo. A critical mediator of key cell-cell signaling events during embryogenesis is the highly conserved Wnt family of secreted proteins. Recent biochemical and genetic analyses have greatly enriched our understanding of how Wnts signal, and the list of canonical Wnt signaling components has exploded. The data reveal that multiple extracellular, cytoplasmic, and nuclear regulators intricately modulate Wnt signaling levels. In addition, receptor-ligand specificity and feedback loops help to determine Wnt signaling outputs. Wnts are required for adult tissue maintenance, and perturbations in Wnt signaling promote both human degenerative diseases and cancer. The next few years are likely to see novel therapeutic reagents aimed at controlling Wnt signaling in order to alleviate these conditions.

Question: What is the Wnt signaling pathway?


Identifying adverse drug events from patient social media: A case study for diabetes. Patient social media sites have emerged as major platforms for discussion of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient reports from social media continues to be a challenge in health informatics research. In light of the need for more robust extraction methods, the authors developed a novel information extraction framework for identifying adverse drug events from patient social media. They also conducted a case study on a major diabetes patient social media platform to evaluate their framework's performance. Their approach achieves an f-measure of 86 percent in recognizing discussion of medical events and treatments, an f-measure of 69 percent for identifying adverse drug events, and an f-measure of 84 percent in patient report extraction. Their proposed methods significantly outperformed prior work in extracting patient reports of adverse drug events in health social media.

Question: What is the f-measure of identifying adverse drug events from patient social media?


Combining Multimodal Features with Hierarchical Classifier Fusion for Emotion Recognition in the Wild. Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features from video and audio to evaluate their discriminative ability to human emotion analysis. For each clip, we extract SIFT, LBP-TOP, PHOG, LPQ-TOP and audio features. We train different classifiers for every kind of features on the dataset from EmotiW 2014 Challenge, and we propose a novel hierarchical classifier fusion method for all the extracted features. The final achievement we gained on the test set is 47.17% which is much better than the best baseline recognition rate of 33.7%.

Question: What is the best multimodal feature for emotion recognition?


Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks. In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.

Question: How to estimate phoneme class conditional probabilities from raw speech signal using convolutional neural networks?


Data-swapping --a Technique for Disclosure Control. In recent years there has been increasing concern about the confidentiality of computerized databases. This has led to a fast growing interest in the development of techniques for controlling the disclosure of information from such databases about individuals, both natural and legal persons. 3 Much of this interest has been focused on the release of statistical tabulations and microdata files. In this paper, we introduce a new technique, data-swapping, that addresses these interests. This technique can be used to both produce microdata and release statistical tabulations so that confidentiality is not violated. The ideas presented in this report are tentative and have not been tested on real-life data. The illustrations provided in the text are based on small-scale numerical experiments, and serve to illustrate the use of data-swapping for disclosure control. Before the proposed techniques can be used on a large scale, more research will be needed. Directions for this research are pointed out throughout the report. This report is organized in two parts. In Part I, we present the underlying idea of the technique. In Part II, we present some theory and methods.

Question: What is data-swapping?


Development of sEMG-driven assistive devices based on twisted string actuation. A twisted string actuation module with an integrated force sensor based on optoelectronic components is presented in this paper. This solution is especially suited for very compact, light-weight and wearable robotic devices, such as exoskeletons, but is appropriate for various robotic applications. An in-depth presentation of the proposed actuation module, the description and the basic sensor working principle of the integrated force sensor are portrayed and discussed. Extensive experimental measures have been carried out to verify the actuation module compliant frame design and the related finite element analysis results. Therefore, the proposed actuation module has been used for the implementation of a simple assistive application where surface-electromyographic signals are used to detect the user's muscle activity in order to control the support action provided by the actuator, thus reducing his/her effort.

Question: What is a twisted string actuation module?


Lens design as multi-objective optimisation. This paper demonstrates the computational advantages of a multiobjective framework that can overcome the generic and domain-related challenges in optical system design and optimisation. Non-dominated sorting genetic algorithms-II (Deb, 2003) is employed in this study. The optical systems studied in this paper are Cooke triplets, Petzval lens systems and achromatic doublets. We report the results of four studies. In the first study, we optimise the optical systems using computationally efficient image quality objective functions. Our approach uses only two paraxial rays to estimate the objective functions and thus improves the computational efficiency. This timesaving measure can partially compensate for the typically enormous number of fitness function evaluations required in evolutionary algorithms. The reduction in reliability due to the computations from a single ray pair is compensated by the availability of multiple objective functions that help us to navigate to the optima. In the second study, hybridisation of evolutionary and gradient-based approaches and scaling techniques are employed to speed up convergence and enforce the constraints. The third study shows how recent developments in optical system design research can be better integrated in a multi-objective framework. The fourth study optimises an achromatic doublet with suitable constraints applied to the thicknesses and image distance.

Question: What is the computational advantage of a multiobjective framework?


Statistical language learning. The $64,000 question in computational linguistics these days is: "What should I read to learn about statistical natural language processing?" I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textbook, and supplement those texts with an assortment of conference papers and journal articles. Understanding the disappointment this answer provoked, I was delighted to hear that someone had finally written a bookdirectly addressing this topic. However, after reading Eugene Charniak's Statistical Language Learning, I have very mixed feelings about the impact this bookmight have on the ever-growing field of statistical NLP. The book begins with a very brief description of the classic artificial intelligence approach toNLP (chapter 1), including morphology, syntax, semantics, and pragmatics. It presents a few definitions from probability theory and information theory (chapter 2), then proceeds to introduce hidden Markov models (chapters 3–4) and probabilistic context-free grammars (chapters 5–6). The book concludes with a few chapters discussing advanced topics in statistical language learning, such as grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), andword sense disambiguation (chapter 10). To its credit, the book serves as an interesting popular discussion of statistical modeling in NLP. It is well-written and entertaining, and very accessible to the reader with a limited mathematical background. It presents a good selection of statistical NLP topics to introduce the reader to the field. And the descriptions of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are intuitive and easy to follow. However, as a resource for

Question: What is the forward-backward algorithm for hidden Markov models?


Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. Reputation reporting systems have emerged as an important risk management mechanism in online trading communities. However, the predictive value of these systems can be compromised in situations where conspiring buyers intentionally give unfair ratings to sellers or, where sellers discriminate on the quality of service they provide to different buyers. This paper proposes and evaluates a set of mechanisms, which eliminate, or significantly reduce the negative effects of such fraudulent behavior. The proposed mechanisms can be easily integrated into existing online reputation systems in order to safeguard their reliability in the presence of potentially deceitful buyers and sellers.

Question: How can we protect online reputation systems from unfair ratings?


Design of a CMOS bandgap Reference Circuit with a Wide temperature Range, High Precision and Low temperature coefficient. This paper presents an approach to the design of a high-precision CMOS voltage reference. The proposed circuit is designed for TSMC 0.35 m standard CMOS process. We design the ̄rstorder temperature compensation bandgap voltage reference circuit. The proposed post-simulated circuit delivers an output voltage of 0.596V and achieves the reported temperature coe±cient (TC) of 3.96 ppm/ C within the temperature range from 60 C to 130 C when the supply voltage is 1.8V. When simulated in a smaller temperature range from 40 C to 80 C, the circuit achieves the lowest reported TC of 2.09 ppm/ C. The reference current is 16.586 A. This circuit provides good performances in a wide range of temperature with very small TC.

Question: What is the temperature coefficient of a bandgap reference?


A review and analysis of churn prediction methods for customer retention in telecom industries. Customer churn prediction has gathered greater interest in business especially in telecommunications industries. Many authors have presented different versions of the churn prediction models greatly based on the data mining concepts employing the machine learning and meta-heuristic algorithms. This aim of this paper is to study some of the most important churn prediction techniques developed over the recent years. The primary objective is on the churn in telecom industries to accurately estimate the customer survival and customer hazard functions to gain the complete knowledge of churn over the customer tenure. Another objective is the identification of the customers who are at the blink of churn and approximating the time they will churn. This paper focuses on analyzing the churn prediction techniques to identify the churn behavior and validate the reasons for customer churn. This paper summarizes the churn prediction techniques in order to have a deeper understanding of the customer churn and it shows that most accurate churn prediction is given by the hybrid models rather than single algorithms so that telecom industries become aware of the needs of high risk customers and enhance their services to overturn the churn decision.

Question: What churn prediction techniques are used in telecom industries?


Novel Multilevel Inverter Carrier-Based PWM Method. The advent of the transformerless multilevel inverter topology has brought forth various pulsewidth modulation (PWM) schemes as a means to control the switching of the active devices in each of the multiple voltage levels in the inverter. An analysis of how existing multilevel carrier-based PWM affects switch utilization for the different levels of a diode-clamped inverter is conducted. Two novel carrier-based multilevel PWM schemes are presented which help to optimize or balance the switch utilization in multilevel inverters. A 10-kW prototype sixlevel diode-clamped inverter has been built and controlled with the novel PWM strategies proposed in this paper to act as a voltage-source inverter for a motor drive.

Question: How to optimize the switch utilization in a multilevel inverter?


4G- A NEW ERA IN WIRELESS TELECOMMUNICATION. 4G – "connect anytime, anywhere, anyhow" promising ubiquitous network access at high speed to the end users, has been a topic of great interest especially for the wireless telecom industry. 4G seems to be the solution for the growing user requirements of wireless broadband access and the limitations of the existing wireless communication system. The purpose of this paper is to provide an overview of the different aspects of 4G which includes its features, its proposed architecture and key technological enablers. It also elaborates on the roadblocks in its implementations. A special consideration has been given to the security concerns of 4G by discussing a security threat analysis model proposed by International Telecommunication Union (ITU). By applying this model, a detailed analysis of threats to 4G and the corresponding measures to counter them can be performed.

Question: What is the purpose of this paper?


Accurate and robust localization of duplicated region in copy–move image forgery. Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using "dense" MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher

Question: What is the difference between copy–move image forgery and image forgery?


UIMA: an architectural approach to unstructured information processing in the corporate research environment. IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly suggested that improving the organization's ability to quickly discover each other's results and rapidly combine different technologies and approaches would accelerate scientific advance. Furthermore, the ability to reuse and combine results through a common architecture and a robust software framework would accelerate the transfer of research results in NLP into IBM's product platforms. Market analyses indicating a growing need to process unstructured information, specifically multilingual, natural language text, coupled with IBM Research's investment in NLP, led to the development of middleware architecture for processing unstructured information dubbed UIMA. At the heart of UIMA are powerful search capabilities and a data-driven framework for the development, composition and distributed deployment of analysis engines. In this paper we give a general introduction to UIMA focusing on the design points of its analysis engine architecture and we discuss how UIMA is helping to accelerate research and technology transfer. 1 Motivation and Objectives We characterize a UIM application as a software system that analyzes large volumes of unstructured information while consulting structured information sources to discover, organize and present knowledge relevant to the application's end-user. We define structured information as information whose intended meaning is unambiguous and explicitly represented in the structure or format

Question: What is the motivation for developing UIMA?


HUMAN-AUTOMATION INTERACTION : TAXONOMIES AND QUALITATIVE MODELS The Meaning of Human-Automation Interaction. Automation does not mean humans are replaced; quite the opposite. Increasingly, humans are asked to interact with automation in complex and typically large-scale systems, including aircraft and air traffic control, nuclear power, manufacturing plants, military systems, homes, and hospitals. This is not an easy or error-free task for either the system designer or the human operator/automation supervisor, especially as computer technology becomes ever more sophisticated. This review outlines recent research and challenges in the area, including taxonomies and qualitative models of human-automation interaction; descriptions of automation-related accidents and studies of adaptive automation; and social, political, and ethical issues.

Question: What is the meaning of human-automation interaction?


Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders.. Functional near-infrared spectroscopy (fNIRS) is an emerging functional neuroimaging technology offering a relatively non-invasive, safe, portable, and low-cost method of indirect and direct monitoring of brain activity. Most exciting is its potential to allow more ecologically valid investigations that can translate laboratory work into more realistic everyday settings and clinical environments. Our aim is to acquaint clinicians and researchers with the unique and beneficial characteristics of fNIRS by reviewing its relative merits and limitations vis-à-vis other brain-imaging technologies such as functional magnetic resonance imaging (fMRI). We review cross-validation work between fMRI and fNIRS, and discuss possible reservations about its deployment in clinical research and practice. Finally, because there is no comprehensive review of applications of fNIRS to brain disorders, we also review findings from the few studies utilizing fNIRS to investigate neurocognitive processes associated with neurological (Alzheimer's disease, Parkinson's disease, epilepsy, traumatic brain injury) and psychiatric disorders (schizophrenia, mood disorders, anxiety disorders).

Question: What is the difference between fMRI and fNIRS?


MoveIt!: Autonomous Underwater Free-Floating Manipulation. Today, autonomous underwater vehicles (AUVs) are mostly used for survey missions, but many existing applications require manipulation capabilities, such as the maintenance of permanent observatories, submerged oil wells, cabled sensor networks, and pipes; the deployment and recovery of benthic stations; or the search and recovery of black boxes. Currently, these tasks require the use of work-class remotely operated vehicles (ROVs) deployed from vessels equipped with dynamic positioning, leaving such solutions expensive to adopt. To face these challenges during the last 25 years, scientists have researched the idea of increasing the autonomy of underwater intervention systems.

Question: What is the cost of an autonomous underwater vehicle?


Collocated photo sharing, story-telling, and the performance of self. This article reports empirical findings from four inter-related studies, with an emphasis on collocated sharing. Collocated sharing remains important, using both traditional and emerging image-related technologies. Co-present viewing is a dynamic, improvisational construction of a contingent, situated interaction between story-teller and audience. The concept of performance, as articulated differently by Erving Goffman and Judith Butler, is useful understand the enduring importance of co-present sharing of photos and the importance of oral narratives around images in enacting identity and relationships. Finally, we suggest some implications for both HCI research and the design of image-related technologies. & 2009 Elsevier Ltd. All rights reserved.

Question: What is the importance of collocated sharing?


Performing edge detection by difference of Gaussians using q-Gaussian kernels. X iv :1 31 1. 25 61 v2 [ cs .C V ] 1 2 N ov 2 01 3 Performing edge detection by difference of Gaussians using q-Gaussian kernels Lucas Assirati, Núbia R. da Silva, 2 Lilian Berton, Alneu de A. Lopes, and Odemir M. Bruno 2 Scientific Computing Group, São Carlos Institute of Physics, University of São Paulo (USP), cx 369 13560-970 São Carlos, São Paulo, Brazil www.scg.ifsc.usp.br Institute of Mathematics and Computer Science, University of São Paulo (USP), Avenida Trabalhador são-carlense, 40

Question: How to perform edge detection by difference of Gaussians using q-Gaussian kernels?


Variable radius pulley design methodology for pneumatic artificial muscle-based antagonistic actuation systems. There is a growing interest in utilizing pneumatic artificial muscles (PAMs) as actuators for human-friendly robots. However, several performance drawbacks prevent the widespread use of PAMs. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs such as restricted workspace and torque capacity remain to be addressed. This paper analyzes the limitations of conventional circular pulley joints and subsequently proposes a design methodology to synthesize a pair of variable radius pulleys to improve joint torque capacity over a large workspace. Experimental results show that newly synthesized variable radius pulleys significantly improve position tracking performance in the enlarged workspace.

Question: What is the difference between a variable radius pulley and a circular pulley?


An agile process model for product derivation in software product line engineering. Software Product Lines (SPL) and Agile practices have emerged as new paradigms for developing software. Both approaches share common goals; such as improving productivity, reducing time to market, decreasing development costs and increasing customer satisfaction. These common goals provide the motivation for this research. We believe that integrating Agile practices into SPL can bring a balance between agility and formalism. However, there has been little research on such integration. We have been researching the potential of integrating Agile approaches in one of the key SPL process areas, product derivation. In this paper we present an outline of our Agile process model for product derivation that was developed through industry based case study research.

Question: What is the process of product derivation?


Dual Learning based Multi-Objective Pairwise Ranking. There are many different recommendation tasks in our real life. The item ranking task is ranking a set of items based on users' preferences. The user ranking task is referred to as user retrieval, find the potential users and recommend items to them. We find every recommendation task has it's own dual task, e.g., Recommend an item to a user versus the user is supposed to become the item's potential interested user. In this paper, we propose a novel dual learning based ranking framework with the overall aim of learning users' preferences over items and learning items' preferences over users by minimizing a pairwise ranking loss. We generate effective feedback signal from close loop formed by dual task. Through a reinforcement learning process, we can iteratively update these two tasks' models to catch relations between the item recommendation task and the user retrieval task. The experiments show that our method outperforms classical approaches for multi-object recommendation tasks, especially for user retrieval tasks.

Question: What is the difference between user retrieval and item recommendation?


On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey. ÐHandwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the on-line case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered. Index TermsÐHandwriting recognition, on-line, off-line, written language, signature verification, cursive script, handwriting learning tools, writer authentification.

Question: What is the difference between on-line and off-line handwriting recognition?


Evaluating the relationship between white matter integrity, cognition, and varieties of video game learning.. BACKGROUND Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games.   OBJECTIVE To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research.   METHODS Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas.   RESULTS Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age.   CONCLUSION Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related d

Question: What is the difference between action and strategy games?


Vehicle license plate detection and recognition using non-blind image de-blurring algorithm. This paper proposes the method of vehicle license plate recognition, which is essential in the field of intelligent transportation system. The purpose of the study is to present a simple and effective vehicle license plate detection and recognition using non-bling image de-blurring algorithm. The sharpness of the edges in an image is restored by the prior information on images. The blue kernel is free of noise while using the non-blind image de-blurring algorithm. This non-blind image de-blurring (NBID) algorithm is involved in the process of removing the optimization difficulties with respect to unknown image and unknown blur. Estimation is carried out for the length of the motion kernel with Radon transform in Fourier domain. The proposed algorithm was tested on different vehicle images and achieved satisfactory results in license plate detection. The experimental results deal with the efficiency and robustness of the proposed algorithm in both synthesized and real images.

Question: How to detect license plate?


Boosting as a Regularized Path to a Maximum Margin Classifier. In this paper we study boosting methods from a new perspective. We build on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion with an l1 constraint on the coefficient vector. This helps understand the success of boosting with early stopping as regularized fitting of the loss criterion. For the two most commonly used criteria (exponential and binomial log-likelihood), we further show that as the constraint is relaxed—or equivalently as the boosting iterations proceed—the solution converges (in the separable case) to an "l1-optimal" separating hyper-plane. We prove that this l1-optimal separating hyper-plane has the property of maximizing the minimal l1-margin of the training data, as defined in the boosting literature. An interesting fundamental similarity between boosting and kernel support vector machines emerges, as both can be described as methods for regularized optimization in high-dimensional predictor space, using a computational trick to make the calculation practical, and converging to margin-maximizing solutions. While this statement describes SVMs exactly, it applies to boosting only approximately.

Question: What is the difference between boosting and kernel support vector machines?


The Gut Microbiota and Autism Spectrum Disorders. Gastrointestinal (GI) symptoms are a common comorbidity in patients with autism spectrum disorder (ASD), but the underlying mechanisms are unknown. Many studies have shown alterations in the composition of the fecal flora and metabolic products of the gut microbiome in patients with ASD. The gut microbiota influences brain development and behaviors through the neuroendocrine, neuroimmune and autonomic nervous systems. In addition, an abnormal gut microbiota is associated with several diseases, such as inflammatory bowel disease (IBD), ASD and mood disorders. Here, we review the bidirectional interactions between the central nervous system and the gastrointestinal tract (brain-gut axis) and the role of the gut microbiota in the central nervous system (CNS) and ASD. Microbiome-mediated therapies might be a safe and effective treatment for ASD.

Question: What is the gut microbiota?


0.293-mm2 Fast Transient Response Hysteretic Quasi- $V^{2}$  DC–DC Converter With Area-Efficient Time-Domain-Based Controller in 0.35- $\mu$ m CMOS. A time-domain-control-based quasi-<inline-formula> <tex-math notation="LaTeX">$V^{2}$ </tex-math></inline-formula> buck converter with small area, fast transient response, wide load current range, and constant switching frequency is proposed in this paper. The proposed time-domain-based controller achieves compact core area and simplified constant switching frequency control by replacing the conventional voltage-domain comparator with a phase detector having an adaptive detection window. Moreover, a coupling-based inductor current emulator is also proposed to support the discontinuous conduction mode operation in the light load. The proposed buck converter is implemented using 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The core area of the proposed buck converter is only 0.293 mm2. The measured peak efficiency in the continuous conduction mode is 92% and the end-to-end efficiency in the load current range of 25–700 mA is 73%. The undershoot/overshoot and recovery time with the up/down load current steps of 510 mA are 38/20 mV and 2.5/2.6 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, respectively.

Question: What is the area of the proposed buck converter?


Data Mining in Healthcare : Current Applications and Issues By. The successful application of data mining in highly visible fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. This research paper provides a survey of current techniques of KDD, using data mining tools for healthcare and public health. It also discusses critical issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths, and detection of fraudulent insurance claims.

Question: What is the current applications of data mining in healthcare?


Synthesis of a compound T-junction for a two-way splitter with arbitrary power ratio. We have developed a simple and efficient procedure to design H-plane rectangular waveguide T-junctions for equal and unequal two-way power splitters. This synthesis procedure is scalable, renders manufacturable structures, applicable to any arbitrary power split-ratio, and can offer wide band operation. In our implementation, we utilized wedges and inductive windows (being an integral part of the T-junctions), to provide more degrees of freedom, thus, excellent match at the input port, flat power-split ratio over the band with equal phase, where phase balance is essential for various antenna feeds.

Question: What is a T-junction?


Developing an Intelligent Chat-bot Tool to Assist High School Students for Learning General Knowledge Subjects. UPDATED—11 December 2017. Artificial Intelligent Chatbots are used in industries such as Banking systems, Customer services, Education. There are many intelligent tutoring systems currently in practice, but few of them are known to assist high school students for learning their general knowledge subjects. This project paper proposes an artificial intelligent chat-bot tool developed to assist high school students for learning their general knowledge subjects. The proposed intelligent chat-bot tool is a web based intelligent tutoring system which can be accessed by large number of students globally, the users are not required to pay licensing fees. The intelligent chat-bot tool will be available 24 hours a day at schools or public libraries or any other location which has internet access. The intelligent chatbot tool will make use of Natural Language Processing techniques to answer the queries by high school students. The intelligent chat-bot tool will be trained on a knowledge base comprising of general knowledge questions and answers. The intelligent chat-bot tool will have ability to participate in small talks with its learners.

Question: What is an intelligent chat-bot tool?


Compact Circularly-Polarized Patch Antenna Loaded With Metamaterial Structures. A metamaterial-inspired low-profile patch antenna is proposed and studied for circularly-polarized (CP) radiation. The present antenna, which has a single-fed configuration, is loaded with the composite right/left-handed (CRLH) mushroom-like structures and a reactive impedance surface (RIS) for miniaturization purpose. The CP radiation is realized by exciting two orthogonally-polarized modes simultaneously which are located in the left-handed (LH) region. The detailed antenna radiation characteristics are examined and illustrated with both simulated and experimental results. The CP performance can be controlled in several different ways. This antenna exhibits an overall size of 0.177λ0 × 0.181λ0 × 0.025λ0 at 2.58 GHz and a radiation efficiency around 72%. Finally, based on the proposed CP patch antenna, a compact dual-band dual linearly-polarized patch antenna has also been designed and fabricated. Promising experimental results are observed.

Question: What is a compact circularly-polarized patch antenna?


Automatic Topic Labeling in Asynchronous Conversations. Asynchronous conversations are conversations where participants collaborate with each other at different times (e.g., email, blog, forum). The huge amount of textual data generated everyday in these conversations calls for automated methods of conversational text analysis. Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many NLP applications including summarization, information extraction and conversation visualization. Topic segmentation in asynchronous conversation refers to the task of clustering the sentences into a set of coherent topical clusters. In [6, 7], we presented unsupervised and supervised topic segmentation models for asynchronous conversations, which to our knowledge are the state-of-the-art. This study concerns topic labeling, that is, given a set of topical clusters in a conversation, the task is to assign appropriate topic labels to the clusters. For example, five topic labels in a Slashdot [2] blog conversation about releasing a new game called Daggerfall are game contents and size, game design, bugs/faults, other gaming options and performance/speed issues. Such topic labels can serve as a concise summary of the conversation and can also be used for indexing. Ideally, topic labels should be meaningful, semantically similar to the underlying topic, general (i.e., broad coverage of the topic) and discriminative (or exclusive) when there are multiple topics [10]. Traditionally, the top K terms in a multinomial topic model (e.g., LDA [3]) are used to represent a topic. However, as pointed out by [10], at the word-level, topic labels may become too general that it can impose cognitive difficulties on a user to interpret the meaning of the topic by associating the words together. On the other hand, if the labels are expressed at the sentence-level, they may become too specific to cover the whole theme of

Question: What is the state of the art in topic labeling in asynchronous conversations?


AFRL-RQ-WP-TR-2014-0212 A BROADBAND HIGH-GAIN BILAYER LOG-PERIODIC DIPOLE ARRAY ( LPDA ) FOR ULTRA HIGH FREQUENCY ( UHF ) CONFORMAL LOAD BEARING ANTENNA STRUCTURES ( CLAS ) APPLICATIONS. A broadband high-gain bi-layer Log-PeriodicDipole-Array (LPDA) is introduced for Conformal Load Bearing Antenna Structures (CLAS) applications. Under the proposed scheme the two layers of the LPDA are printed on two separate thin dielectric substrates which are substantially separated from each other. A meander line geometry is adapted to achieve size reduction for the array. The fabricated and tested array easily exceeds more than an octave of gain, pattern and VSWR bandwidth. Index Terms — Broadband antenna, array, log-periodic, LPDA, endfire, ultrawideband, UWB, CLAS.

Question: What is the size of the array?


Addressing Security and Privacy Risks in Mobile Applications. Applications for mobile platforms are being developed at a tremendous rate, but often without proper security implementation. Insecure mobile applications can cause serious information security and data privacy issues and can have severe repercussions on users and organizations alike.

Question: What are the security and privacy risks of mobile applications?