Qualitative Research & Evaluation Methods: Integrating Theory and Practice. In what case do you like reading so much? What about the type of the qualitative research evaluation methods integrating theory and practice book? The needs to read? Well, everybody has their own reason why should read some books. Mostly, it will relate to their necessity to get knowledge from the book and want to read just to get entertainment. Novels, story book, and other entertaining books become so popular this day. Besides, the scientific books will also be the best reason to choose, especially for the students, teachers, doctors, businessman, and other professions who are fond of reading.

Question: What is the best book to read?


Coupling Matrix Synthesis for a New Class of Microwave Filter Configuration. In this paper a new approach to the synthesis of coupling matrices for microwave filters is presente d. The new approach represents an advance on existing direct a nd optimization methods for coupling matrix synthesis in that it will exhaustively discover all possible coupling matrix solutions for a network if more than one exists. This enables a se lection to be made of the set of coupling values, resonator frequ ency offsets, parasitic coupling tolerance etc that will be best suited to the technology it is intended to realize the microwave filter with. To demonstrate the use of the method, the case of the r cently – introduced 'extended box' (EB) coupling matrix configuration is taken. The EB represents a new class of filter con figuration featuring a number of important advantages, one of which is the existence of multiple coupling matrix solutions for each prototype filtering function, eg 16 for 8 degree cases. This case is taken as an example to demonstrate the use of the synthesis method – yielding one solution suitable for dual-mode realiz ation and one where some couplings are small enough to neglect. Index Terms — Coupling matrix, filter synthesis, Groebner basis, inverted characteristic, multiple solutions.

Question: What is the difference between the direct and optimization methods for coupling matrix synthesis?


Stabilizing Dynamic Controllers for Hybrid Systems: A Hybrid Control Lyapunov Function Approach. This paper proposes a dynamic controller structure and a systematic design procedure for stabilizing discrete-time hybrid systems. The proposed approach is based on the concept of control Lyapunov functions (CLFs), which, when available, can be used to design a stabilizing state-feedback control law. In general, the construction of a CLF for hybrid dynamical systems involving both continuous and discrete states is extremely complicated, especially in the presence of non-trivial discrete dynamics. Therefore, we introduce the novel concept of a hybrid control Lyapunov function, which allows the compositional design of a discrete and a continuous part of the CLF, and we formally prove that the existence of a hybrid CLF guarantees the existence of a classical CLF. A constructive procedure is provided to synthesize a hybrid CLF, by expanding the dynamics of the hybrid system with a specific controller dynamics. We show that this synthesis procedure leads to a dynamic controller that can be implemented by a receding horizon control strategy, and that the associated optimization problem is numerically tractable for a fairly general class of hybrid systems, useful in real world applications. Compared to classical hybrid receding horizon control algorithms, the proposed approach typically requires a shorter prediction horizon to guarantee asymptotic stability of the closed-loop system, which yields a reduction of the computational burden, as illustrated through two examples.

Question: How to design a stabilizing controller for a hybrid system?


OntoNotes: The 90% Solution. OntoNotes is a five year multi-site collaboration between BBN Technologies, Information Sciences Institute of University of Southern California, University of Colorado, University of Pennsylvania and Brandeis University. The goal of the OntoNotes project is to provide linguistic data annotated with a skeletal representation of the literal meaning of sentences including syntactic parse, predicate-argument structure, coreference, and word senses linked to an ontology, allowing a new generation of language understanding technologies to be developed with new functional capabilities. In its third year of existence, the OntoNotes project has generated a large amount of high quality data covering various layers of linguistic annotation. This is probably the first time that data of such quality has been available in large quantities covering multiple genres (newswire, broadcast news, broadcast conversation and weblogs) and languages (English, Chinese and Arabic). The guiding principle has been to find a "sweet spot" in the space of inter-tagger agreement, productivity, and depth of representation. The most effective use of this resource for research requires simultaneous access to multiple layers of annotation. This has been made possible by representing the corpus with a relational database to accommodate the dense connectedness of the data and ensure consistency across layers. In order to facilitate ease of understanding and manipulability, the database has also been supplemented with a object-oriented Python API. The tutorial consists of two parts. In the first part we will familiarize the user with this new resource, describe the various layers of annotations in some detail and discuss the linguistic principles and sometimes practical considerations behind the important design decisions that shapes the corpus. We will also describe the salient differences between the three languages at each layer of annotation and how linguistic peculiaritie

Question: What is the ontology used in ontonotes?


Design and prototyping of 3-phase BLDC motor. The development of electric vehicle is now growing rapidly. Demands to deliver a reliable and easy to drive in motor control causes Brushless Direct Current (BLDC) motor becomes a potential candidate. A BLDC motor drive is a potential option for an electric vehicle since it has a high reliability, simple design, and ability to work at high rotation per minute (RPM). This paper discussed the Permanent Magnet BLDC Motor design method. The structure of an interior rotor permanent magnet type is selected to be used in the design of Permanent Magnet BLDC motor so that it can be applied in a drive that requires a large torque and capable of acceleration and deceleration with good response. Selection of 12 slots and 8 poles configuration aims for improving the motor performance. The motor is designed and simulated using a software-based Motor Solve FEA (Finite Element Analysis). Based on this design and simulation results, a prototype of BLDC motor is built. Parameters testing as stator resistance, inductances (the d-axis and q-axis inductance), and the back emf constant (Ke) were used to evaluate the result of the design and prototype motor. Measuring the prototype motor's parameters was carried out by several different methods depending on the parameters tested. Stator resistance testing is performed with the measurement of current in the coil which is then obtained by calculating the magnitude of stator resistance as 0.14710296 Ω. Measurements of d-axis stator inductance, q-axis stator inductance, and back emf constant of prototype permanent magnet BLDC motor is obtained as results of 0.35304710 mH, 0.38246769 mH and 0.09690626 Vs/rad respectively. The test results between design and prototype testing were quite good. The difference between the test results and the design of the prototype test results was caused by incompatibility of material composition although using the same type of material. The evaluation shows the elec

Question: What is the difference between a permanent magnet and a electromagnet?


DC Bus Voltage Control for a Distributed Power System. This paper addresses voltage control of distributed dc power systems. DC power systems have been discussed as a result of the introduction of renewable, small-scale power generation units. Also, telecommunication power systems featuring UPS properties might benefit from a broader introduction of dc power systems. Droop control is utilized to distribute the load between the source converters. In order to make the loading of the source converters equal, in per unit, the voltage control algorithm for each converter has to be designed to act similar. The dc side capacitor of each converter, needed for filtering, is also determined as a consequence. The root locus is investigated for varying dc bus impedance. It is found that the risk of entering converter over-modulation is a stronger limitation than stability, at least for reasonable dc bus cable parameters. The stationary and dynamic properties during load variations are also investigated.

Question: What is the difference between a dc bus and a dc cable?


Subjective Well ‐ Being and Income : Is There Any Evidence of Satiation ? *. Subjective Well‐Being and Income: Is There Any Evidence of Satiation? Many scholars have argued that once "basic needs" have been met, higher income is no longer associated with higher in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of "basic needs" and multiple questions about well-being, we find no support for this claim. The relationship between wellbeing and income is roughly linear-log and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it. JEL Classification: D6, I3, N3, O1, O4

Question: Is there any evidence of satiation?


Applying the Clique Percolation Method to analyzing cross-market branch banking network structure: the case of Illinois. This study applies the Clique Percolation Method (CPM) to an investigation of the changing spatial organization of the Illinois cross-market branch banking network. Nonoverlapping community detection algorithms assign nodes into exclusive communities and, when results are mapped, these techniques may generate spatially disjointed geographical regions, an undesirable characteristic for geographical study. Alternative overlapping community detection algorithms allow overlapping membership where a node can be a member of different communities. Such a structure simultaneously accommodates spatial proximity and spatial separation which occur with respect to a node in relation to other nodes in the system. Applying such a structure in geographical analysis helps preserve well-established principles regarding spatial relationships within the geography discipline. The result can also be mapped for display and correct interpretation. The CPM is chosen in this study due to the complete connection within cliques which simulates the practice by banking institutions of forming highly connected networks through multi-location operations in order to diversify their business and hedge against risks. Applying the CPM helps reveal the spatial pattern of branch banking connections which would otherwise be difficult to see. However, the CPM has been shown to not be among the best performing overlapping community detection algorithms. Future research should explore other possible algorithms for detecting overlapping communities. Detecting communities in a network only reveals certain characteristics of the spatial organization of the network, rather than providing explanation of the spatial-network patterns revealed. Full interpretation of the pattern must rely on the attribute data and additional information. This may illustrate the value of an integrated approach in

Question: What is the clique percolation method?


Automatic annotation of human actions in video. This paper addresses the problem of automatic temporal annotation of realistic human actions in video using minimal manual supervision. To this end we consider two associated problems: (a) weakly-supervised learning of action models from readily available annotations, and (b) temporal localization of human actions in test videos. To avoid the prohibitive cost of manual annotation for training, we use movie scripts as a means of weak supervision. Scripts, however, provide only implicit, noisy, and imprecise information about the type and location of actions in video. We address this problem with a kernel-based discriminative clustering algorithm that locates actions in the weakly-labeled training data. Using the obtained action samples, we train temporal action detectors and apply them to locate actions in the raw video data. Our experiments demonstrate that the proposed method for weakly-supervised learning of action models leads to significant improvement in action detection. We present detection results for three action classes in four feature length movies with challenging and realistic video data.

Question: How to train a temporal action detector?


StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation. Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presumed that humans should take the role to lead the conversation and introduce new content when a stalemate occurs, and that the computer only needs to "respond." In this paper, we propose STALEMATEBREAKER, a conversation system that can proactively introduce new content when appropriate. We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. We further propose a novel reranking algorithm BiPageRank-HITS to enable rich interaction between conversation context and candidate replies. Experiments show that both the content-introducing approach and the reranking algorithm are effective. Our full STALEMATEBREAKER model outperforms a state-of-the-practice conversation system by +14.4% p@1 when a stalemate occurs.

Question: How does a conversation system work?


What storytelling can do for information visualization. A well-told story conveys great quantities of information in relatively few words in a format that is easily assimilated by the listener or viewer. People usually find it easier to understand information integrated into stories than information spelled out in serial lists (such as bulleted items in an overhead slide). Stories are also just more compelling. For example, despite its sketchiness, the story fragment in Figure 1 is loaded with information, following an analysis similar to that of John Thomas of IBM Research [5]. We find that Jim uses technology (a pager and the Internet) and is dedicated to his job. Many other pieces of information can be deduced about Jim and his work, as well as about his relationships with his coworkers, as noted in the right side of the figure. The story does not express all this information explicitly; some is only implied; for example, we can surmise that Jim is probably not at the gym and his attendance at the meeting is important to his boss and coworkers, as well as to his company's business performance. As in most stories, this one involves uncerFor as long as people have been around, they have used stories to convey information, cultural values, and experiences. Since the invention of writing and the printing press until today, technology and culture have constantly provided new and increasingly sophisticated means to tell stories. More recently, technology, entertainment, and art have converged in the computer. The ancient art of storytelling and its adaptation in film and video can now be used to efficiently convey information in our increasingly computerized world. What Storytelling Can Do for Information Visualization

Question: What is the difference between a story and a story fragment?


Q-Learning-Based Power Control for LTE Enterprise Femtocell Networks. Small cells are beneficial for both users and operators to improve communication quality. The femtocell is one type of small cell used for indoor coverage. Enterprise femtocell installation inevitably produces intralayer and interlayer interference management issues. Enterprise femtocell optimization and indoor propagation modeling are built in this work based on the communications environment characteristics. Q-learning-based distributed and hybrid power control strategies are then proposed. The throughput, energy efficiency, and user experience satisfaction with conventional scheduling methods are compared. The simulation results show that distributed Q-learning performs better local optimization while hybrid Q-learning enhances global performance.

Question: What is the difference between distributed and hybrid Q-learning?


Exploratory Analysis of the Factors Affecting Consumer Choice in E-Commerce: Conjoint Analysis. According to previous studies of online consumer behaviour, three factors are the most influential on purchasing behavior brand, colour and position of the product on the screen. However, a simultaneous influence of these three factors on the consumer decision making process has not been investigated previously. In this particular work we aim to execute a comprehensive study of the influence of these three factors. In order to answer our main research questions, we conducted an experiment with 96 different combinations of the three attributes, and using statistical analysis, such as conjoint analysis, t-test analysis and Kendall analysis we identified that the most influential factor to the online consumer decision making process is brand, the second most important attribute is the colour, which was estimated half as important as brand, and the least important attribute is the position on the screen. Additionally, we identified the main differences regarding consumers stated and revealed preferences regarding these three attributes.

Question: What is the most important factor in online consumer decision making?


Resting state networks and consciousness Alterations of multiple resting state network connectivity in physiological , pharmacological , and pathological consciousness states. *Correspondence: Athena Demertzi , Coma Science Group, Cyclotron Research Center, Allée du 6 août no 8, Sart Tilman B30, University of Liège, 4000 Liège, Belgium. e-mail: a.demertzi@ulg.ac.be In order to better understand the functional contribution of resting state activity to conscious cognition, we aimed to review increases and decreases in functional magnetic resonance imaging (fMRI) functional connectivity under physiological (sleep), pharmacological (anesthesia), and pathological altered states of consciousness, such as brain death, coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.The reviewed resting state networks were the DMN, left and right executive control, salience, sensorimotor, auditory, and visual networks. We highlight some methodological issues concerning resting state analyses in severely injured brains mainly in terms of hypothesis-driven seed-based correlation analysis and data-driven independent components analysis approaches. Finally, we attempt to contextualize our discussion within theoretical frameworks of conscious processes.We think that this "lesion" approach allows us to better determine the necessary conditions under which normal conscious cognition takes place. At the clinical level, we acknowledge the technical merits of the resting state paradigm. Indeed, fast and easy acquisitions are preferable to activation paradigms in clinical populations. Finally, we emphasize the need to validate the diagnostic and prognostic value of fMRI resting state measurements in non-communicating brain damaged patients.

Question: What is the difference between a lesion and a lesion?


Accurate Identification of Ontology Alignments at Different Granularity Levels. As more and more ontologies are defined with different terms, ontology matching plays a crucial role in addressing the semantic heterogeneity problem in many different disciplines. Many efforts have been made to discover correspondences among terms in different ontologies. Most studies directly match two ontologies by utilizing terminological and structural methods that rely on ontologies themselves only. However, the decentralized characteristic of ontologies raises the uncertainty in ontology matching. To address this problem, we propose a four-stage ontology matching framework (FOMF) to enhance ontology matching performance. It is built upon the commonly accepted claim that an external comprehensive knowledge base can be used as a semantic bridge between domain ontologies for ontology matching. First, FOMF semantically maps domain ontologies to a knowledge base and then produces different types of alignments, including equivalence, subclass, sameas, and instance alignments. Similarities between two domain ontologies are next employed to enhance the equivalence and sameas alignments discovery. Finally, based on acquired alignments, inferred alignments are deduced to guarantee the completeness of matching results. Our experimental results show the superiority of the proposed method over the existing ones.

Question: How to identify ontology alignments at different granularity levels?


An automotive radar network based on 77 GHz FMCW sensors. Automotive radar sensors will be used in many future applications to increase comfort and safety. Adaptive cruise control (ACC), parking aid, collision warning or avoidance and pre crash are some automotive applications, which are based on high performance radar sensors. This paper describes results gained by an European Commission funded research project and a new radar network based on four individual monostatic 77 GHz FMCW radar sensors. Each sensor measures target range and velocity simultaneously and extremely accurate even in multiple target situations with a maximum range of 40 m. The azimuth angle is estimated by multilateration techniques in a radar network architecture. The system design and the signal processing of this network are described in detail in this paper. Due to the high range resolution performance of each radar sensor ordinary targets cannot be considered any longer as point but as extended targets. System capability, performance and reliability can be improved essentially if the single sensor FMCW signal processing, the target position processing in the radar network and the tracking scheme are jointly optimized.

Question: What is a monostatic radar sensor?


The Basal Ganglia and Chunking of Action Repertoires. The basal ganglia have been shown to contribute to habit and stimulus-response (S-R) learning. These forms of learning have the property of slow acquisition and, in humans, can occur without conscious awareness. This paper proposes that one aspect of basal ganglia-based learning is the recoding of cortically derived information within the striatum. Modular corticostriatal projection patterns, demonstrated experimentally, are viewed as producing recoded templates suitable for the gradual selection of new input-output relations in cortico-basal ganglia loops. Recordings from striatal projection neurons and interneurons show that activity patterns in the striatum are modified gradually during the course of S-R learning. It is proposed that this recoding within the striatum can chunk the representations of motor and cognitive action sequences so that they can be implemented as performance units. This scheme generalizes Miller's notion of information chunking to action control. The formation and the efficient implementation of action chunks are viewed as being based on predictive signals. It is suggested that information chunking provides a mechanism for the acquisition and the expression of action repertoires that, without such information compression would be biologically unwieldy or difficult to implement. The learning and memory functions of the basal ganglia are thus seen as core features of the basal ganglia's influence on motor and cognitive pattern generators.

Question: How does chunking work?


Guidelines for usability testing with children. A Although user-centered design is a well-supported concept in the literature on adult computer products, not until recently have publications begun to appear addressing the need to include the user in the design process of children's computer products. Good examples are a recent panel discussion in interactions on the importance of understanding the perspectives and needs of children, and the energizing work of Allison Druin and Cynthia Solomon [1, 2]. Growth has also occurred in evaluation research in both the industrial and academic communities, assessing the effectiveness or appeal of various types of (6 to 10 years), and middle-school-aged children (11 to 14 years). These age divisions are arbitrary , and many behaviors will overlap. In our experience, most children younger than 2 1/2 years of age are not proficient enough with standard input devices (e.g., mouse, trackball, or keyboard) to interact with the technology and provide useful data. Children older than 14 years of age will likely behave as adults in a testing situation and should be treated accordingly. Preschool (ages 2 to 5 years) Preschoolers require the most extensive adaptations of usability testing because their attention span, their motivation to please adults, and their ability to adjust to strange surroundings and new people may change from one moment to the next. In general, children in this age range should be allowed to explore the computer according to their own interests and pacing instead of performing a series of directed tasks. They will often be happy to show you what they know, and what they can do on the computer independently. When assessing appeal or engagement, testers will need to closely observe children's behavior such as sighing, smiling, or sliding under the table. Children this age often have difficulty expressing their likes and dislikes in words. Elementary School (ages 6 to 10 years) Children in this age range are relativ

Question: What is the difference between a child and a preschooler?


High quality information provisioning and data pricing. This paper presents ideas on how to advance the research on high quality information provisioning and information pricing. To this end, the current state of the art in combining data curation and information provisioning as well as in regard to data pricing is reviewed. Based on that, open issues, such as tailoring data to a user's need and determining a market value of data, are identified. As preliminary solutions, it is proposed to investigate the identified problems in an integrated manner.

Question: What is the current state of the art in combining data curation and information provisioning?


Fast Computation of the Difference of Low-Pass Transform. This paper defines the difference of low-pass (DOLP) transform and describes a fast algorithm for its computation. The DOLP is a reversible transform which converts an image into a set of bandpass images. A DOLP transform is shown to require O(N2) multiplies and produce O(N log(N)) samples from an N sample image. When Gaussian low-pass filters are used, the result is a set of images which have been convolved with difference of Gaussian (DOG) filters from an exponential set of sizes. A fast computation technique based on ``resampling'' is described and shown to reduce the DOLP transform complexity to O(N log(N)) multiplies and O(N) storage locations. A second technique, ``cascaded convolution with expansion,'' is then defined and also shown to reduce the computational cost to O(N log(N)) multiplies. Combining these two techniques yields an algorithm for a DOLP transform that requires O(N) storage cells and requires O(N) multiplies.

Question: How to compute the difference of low-pass transform?


The new blocs on the block: using community forums to foster new neighbourhoods. Research has consistently shown that online tools increase social capital. In the context of neighbourhoods Hampton and Wellman have shown that in newly developed areas residents effectively used mailing lists to connect with each other, circulate information, and ask for help. The research question of whether similar findings would hold in the larger context of a city for a long period of time is still open. To tackle this research question, we have gathered the complete dataset of the most popular neighbourhood online forum in Dublin. In this dataset, we have people sharing a common purpose (blocs) who live in the same neighbourhood and interact online to ask for help, engage in local activities, and, more generally, have a better understanding of their physical community. Our analysis highlights the particularly concentrated usage in newly established developments where a pre-existing community may be absent. Additionally, these communications provide a valuable resource to understand local issues relevant to the community.

Question: How do people use online forums to foster new neighbourhoods?


The CN2 Induction Algorithm. Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the CN2, ID3, and AQ algorithms are compared on three medical classification tasks.

Question: What is a concept description?


Innovations in the Field of Child Abuse and Neglect Prevention : A Review of the Literature. ii Acknowledgments I would like to thank all of the experts I interviewed during the process of writing this paper. Their deep understanding of the history and visions for the future of child maltreatment prevention provided a strong foundation for this work and guided the direction and structure of the project overall. I am grateful for how generous they were with their time and wisdom. The names and affiliations of all the professionals that participated in an interview are listed in Appendix A. Additionally, I would like to thank my mentor, Dr. Deborah Daro. She is a creative and sophisticated thinker, an eloquent and articulate writer, and a passionate and inspiring leader moving child abuse and neglect prevention efforts forward. Lastly, I would like to thank Anne Clary and Matthew Brenner for lending their editing expertise, and the greater Chapin Hall community for their support. Trend #2: Social context and culture can protect the developing child and strengthen parental capacity in important ways that can buffer against individual and contextual risk factors.. Trend #6: Implementation science offers program managers effective research frameworks to monitor and strengthen the service delivery process and to improve the odds of replicating model programs with fidelity and quality.. Trend #7: Maximizing population-level change requires new understanding of how to construct and sustain effective state systems, local community collaboration, and robust community-based Trend #8: New technologies offer important, cost-effective opportunities for advancing our reach into new populations and supporting direct service providers.. 1 Introduction Child maltreatment prevention policy and practice have been driven by a continuous stream of innovation generated by researchers, advocates, and practitioners working across disciplines to improve child safety and well-being. Settlemen

Question: What is the difference between a policy and a practice?


Family presence during resuscitation and/or invasive procedures in the Emergency Department: one size does not fit all..

Question: What is the best way to teach family presence during resuscitation and/or invasive procedures in the Emergency Department?


A Hybrid Approach to Detect and Localize Texts in Natural Scene Images. Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results.

Question: How to detect and localize texts in natural scene images?


Fast Keypoint Recognition in Ten Lines of Code. While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.

Question: How to recognize keypoints in images?


Young adults' experiences of seeking online information about diabetes and mental health in the age of social media. BACKGROUND The Internet is a primary source of health information for many. Since the widespread adoption of social media, user-generated health-related content has proliferated, particularly around long-term health issues such as diabetes and common mental health disorders (CMHDs).   OBJECTIVE To explore perceptions and experiences of engaging with health information online in a sample of young adults familiar with social media environments and variously engaged in consuming user-generated content.   METHODS Forty semi-structured interviews were conducted with young adults, aged 18-30, with experience of diabetes or CMHDs. Data were analysed following a thematic networks approach to explore key themes around online information-seeking and content consumption practices.   RESULTS Although participants primarily discussed well-rehearsed approaches to health information-seeking online, particularly reliance on search engines, their accounts also reflected active engagement with health-related content on social media sites. Navigating between professionally produced websites and user-generated content, many of the young adults seemed to appreciate different forms of health knowledge emanating from varied sources. Participants described negotiating health content based on social media practices and features and assessing content heuristically. Some also discussed habitual consumption of content related to their condition as integrated into their everyday social media use.   CONCLUSION Technologies such as Facebook, Twitter and YouTube offer opportunities to consume and assess content which users deem relevant and useful. As users and organizations continue to colonize social media platforms, opportunities are increasing for health communication and intervention. However, how such innovations are adopted is dependent on their alignment with users' expectat

Question: How do young adults use social media to seek health information?


Active Learning Algorithms for Multi-label Data. The iterative supervised learning setting, in which learning algorithms can actively query an oracle for labels, e.g. a human annotator that understands the nature of the problem, is called active learning. As the learner is allowed to interactively choose the data from which it learns, it is expected that the learner would perform better with less training. The active learning approach is appropriate to machine learning applications where training labels are costly to obtain but unlabeled data is abundant. Although active learning has been widely considered for single-label learning, this is not the case for multi-label learning, in which objects can have more than one class label and a multi-label learner is trained to assign multiple labels simultaneously to an object. There are different scenarios to query the annotator. This work focuses on the scenario in which the evaluation of unlabeled data is taken into account to select the object to be labeled. In this scenario, several multilabel active learning algorithms were identified in the literature. These algorithms were implemented in a common framework and experimentally evaluated in two multi-label datasets which have different properties. The influence of the properties of the datasets in the results obtained by the multi-label active learning algoritm is highlighted.

Question: What is the difference between single and multi-label learning?


Design and implementation of server cluster dynamic load balancing based on OpenFlow. Nowadays, the Internet is flooded with huge traffic, many applications have millions users, a single server is difficult to bear a large number of clients' access, so many application providers will put several servers as a computing unit to provide support for a specific application, usually people will use distributed computing, load balancing technology to complete the work. A typical load balancing technique is to use a dedicated load balancer to forward the client requests to different servers, this technique requires dedicated hardware support, the hardware is expensive, lacks of flexibility and is easy to become a single point failure. There will be a new solution for load balancing with OpenFlow proposed., this paper mainly studies dynamic load balancing technology in the OpenFlow environment, the Controller collected the server running status through the SNMP protocol, and calculated the aggregated load of the severs according to dynamic load balancing scheduling algorithm, the OpenFlow switch will forward the client's request to the server whose aggregated load is smallest, thus minimize the response time of the web server. In the OpenFlow network environment, using this method can brings high flexibility without additional equipment.

Question: How to design and implement a server cluster dynamic load balancing based on OpenFlow?


Bitcoin Network Measurements for Simulation Validation and Parameterization. Bitcoin is gaining increasing popularity nowadays, even though the crypto-currencies field has plenty of digital currencies that have emerged before the adoption of Bitcoin idea. Bitcoin is a decentralized digital currency which relies on set of miners to maintain a distributed public ledger and peer-to-peer network to broadcast transactions. In this paper, we analyse how transaction validation is achieved by the transaction propagation round trip and how transaction dissemination throughout the network can lead to inconsistencies in the view of the current transactions ledger by different nodes. We then measure the transaction propagation delay in the real Bitcoin network and how it is affected by the number of nodes and network topology. This measurement enables a precise validation of any simulation model of the Bitcoin network. Large-scale measurements of the real Bitcoin network are performed in this paper. This will provide an opportunity to parameterise any model of the Bitcoin

Question: How many nodes are there in the bitcoin network?


EARLY DEVELOPMENTS OF A PARALLELLY ACTUATED HUMANOID , SAFFIR. This paper presents the design of our new 33 degree of freedom full size humanoid robot, SAFFiR (Shipboard Autonomous Fire Fighting Robot). The goal of this research project is to realize a high performance mixed force and position controlled robot with parallel actuation. The robot has two 6 DOF legs and arms, a waist, neck, and 3 DOF hands/fingers. The design is characterized by a central lightweight skeleton actuated with modular ballscrew driven force controllable linear actuators arranged in a parallel fashion around the joints. Sensory feedback on board the robot includes an inertial measurement unit, force and position output of each actuator, as well as 6 axis force/torque measurements from the feet. The lower body of the robot has been fabricated and a rudimentary walking algorithm implemented while the upper body fabrication is completed. Preliminary walking experiments show that parallel actuation successfully minimizes the loads through individual actuators.

Question: How does a robot walk?


TRESOR Runs Encryption Securely Outside RAM. Current disk encryption techniques store necessary keys in RAM and are therefore susceptible to attacks that target volatile memory, such as Firewire and cold boot attacks. We present TRESOR, a Linux kernel patch that implements the AES encryption algorithm and its key management solely on the microprocessor. Instead of using RAM, TRESOR ensures that all encryption states as well as the secret key and any part of it are only stored in processor registers throughout the operational time of the system, thereby substantially increasing its security. Our solution takes advantage of Intel's new AES-NI instruction set and exploits the x86 debug registers in a non-standard way, namely as cryptographic key storage. TRESOR is compatible with all modern Linux distributions, and its performance is on a par with that of standard AES implementations.

Question: How does TRESOR work?


Project Management Process Maturity " PM ... 2 Model. This paper presents the project management process maturity (PM) 2 model that determines and positions an organizatio relative project management level with other organizations. The comprehensive model follows a systematic approach to es organization's current project management level. Each maturity level consists of major project management characteristics, fa processes. The model evolves from functionally driven organizational practices to project driven organization that incorporates co project learning. The (PM) 2 model provides an orderly, disciplined process to achieve higher levels of project management mat DOI: 10.1061/ ~ASCE!0742-597X~2002!18:3~150! CE Database keywords: Project management; Models; Organizations. ve lan ani uiteron's r th at ctic

Question: What is the project management maturity model?


Detecting adversarial advertisements in the wild. In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful advertisements. In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because both false positives and false negatives have high cost, our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing the effectiveness of our system.

Question: How do you detect adversarial advertisements in the wild?


Named Entity Recognition using Support Vector Machine: A Language Independent Approach. Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes , defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) . In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context

Question: What is the size of the canadian military?


Direct Desktop Printed-Circuits-on-Paper Flexible Electronics. There currently lacks of a way to directly write out electronics, just like printing pictures on paper by an office printer. Here we show a desktop printing of flexible circuits on paper via developing liquid metal ink and related working mechanisms. Through modifying adhesion of the ink, overcoming its high surface tension by dispensing machine and designing a brush like porous pinhead for printing alloy and identifying matched substrate materials among different papers, the slightly oxidized alloy ink was demonstrated to be flexibly printed on coated paper, which could compose various functional electronics and the concept of Printed-Circuits-on-Paper was thus presented. Further, RTV silicone rubber was adopted as isolating inks and packaging material to guarantee the functional stability of the circuit, which suggests an approach for printing 3D hybrid electro-mechanical device. The present work paved the way for a low cost and easygoing method in directly printing paper electronics.

Question: What is the difference between a desktop printer and a printer?


Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning. In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brain's neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA) spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting Independent Components (ICs) of the epoched feature datasets. The datasets were inputted into two machinelearning algorithms: Neural Networks (NNs) and Support Vector Machines (SVMs). Intensive experiments were carried out and optimum classification performances of 89.8 and 97.1 were obta

Question: What is the difference between a neural network and a support vector machine?


Explaining nonlinear classification decisions with deep Taylor decomposition. Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processing or human action recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method is based on deep Taylor decomposition and efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.

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


ParaBox: Exploiting Parallelism for Virtual Network Functions in Service Chaining. Service Function Chains (SFCs) comprise a sequence of Network Functions (NFs) that are typically traversed in-order by data flows. Consequently, SFC delay grows linearly with the length of the SFC. Yet, for highly latency sensitive applications, this delay may be unacceptable---particularly when the constituent NFs are virtualized, running on commodity servers. In this paper, we investigate how SFC latency may be reduced by exploiting opportunities for parallel packet processing across NFs. We propose ParaBox, a novel hybrid packet processing architecture that, when possible, dynamically distributes packets to VNFs in parallel and merges their outputs intelligently to ensure the preservation of correct sequential processing semantics. To demonstrate the feasibility of our approach, we implement a ParaBox prototype on top of the DPDK-enabled Berkeley Extensible Software Switch. Our preliminary experiment results show that ParaBox can not only significantly reduce the service chaining latency, but also improve throughput.

Question: How does service chaining work?


Security and Privacy Challenges in the Smart Grid. Global electrical grids are verging on the largest technological transformation since the introduction of electricity into the home. The antiquated infrastructure that delivers power to our homes and businesses is being replaced with a collection of digital systems called the smart grid. This grid is the modernization of the existing electrical system that enhances customers' and utilities' ability to monitor, control, and predict energy use.

Question: What is the largest technological transformation since the introduction of electricity into the home?


A comparative analysis of biclustering algorithms for gene expression data. The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.

Question: What is the best biclustering algorithm for gene expression data?


FPGA based Time-to-Digital Converter. High-resolution Time-to-Digital converter (TDC) is one of the crucial blocks in Highenergy nuclear physics time of flight experiments. The contemporary time interval measurement techniques rely on employing methods like Time-to-Amplitude Conversion, Vernier method, Delay Locked Loops (DLL), Tapped Delay Lines (TDL), Differential Delay Lines (DDL) etc. Recently, FPGA based TDC designs with merits of low cost, fast development cycle and re-programmability are reported [1]. The TDC implementation in FPGA have design challenges and issues like, lack of direct control over the propagation delays, unpredictable Place & Route (P&R) delays and delay variations due to Process, Voltage & Temperature (PVT) variations. In the TDC design presented here, the resolution below the minimum gate delay is achieved by employing differential (Vernier oscillator) technique. The predictable P&R is achieved by manual P&R of the critical elements and avoiding the use of digital synthesis tools. Further, in order to compensate for the delay variations due to PVT, a calibration methodology is developed and implemented. By implementing the design in a Flash-based FPGA, the low power design objective is achieved.

Question: What is a time-to-digital converter?


Twitter vigilance: A multi-user platform for cross-domain Twitter data analytics, NLP and sentiment analysis. The growth and diffusion of online social media have been enormously increased in recent years, as well as the research and commercial interests toward these rising sources of information as a direct public expression of the communities. Moreover, the depth and the quality of data that can be harvested by monitoring and analysis tools have evolved significantly. In particular, Twitter has revealed to be one of the most widespread microblogging services for instantly publishing and sharing opinions, feedbacks, ratings etc., contributing in the development of the emerging role of users as sensors. However, due to the huge amount of data to be collected and analyzed and limitations on data access imposed by Twitter public APIs, more efficient requirements are needed for analytics tools, both in terms of data ingestion and processing, as well as for the computation of analysis metrics, to be provided for deeper statistic insights and further investigations. In this paper, the Twitter Vigilance platform is presented, realized by the DISIT Lab at University of Florence. Twitter Vigilance has been designed as a cross-domain, multi-user tool for collecting and analyzing Twitter data, providing aggregated metrics based on the volume of tweets and retweets, users' influence network, Natural Language Processing and Sentiment Analysis of textual content. The proposed architecture has been validated against a dataset of about 270 million tweets showing a high efficiency in recovering Twitter data. For this reason it has been adopted by a number of researchers as a study platform for social media analysis, early warning, etc.

Question: What is the twitter vigilance platform?


Estimating the relationship between isoseismal area and earthquake magnitude by a hybrid fuzzy-neural-network method. Utilizing information diffusion method and artificial neural networks, we propose in this paper a hybrid fuzzy neural network to estimate the relationship between isoseismal area and earthquake magnitude. We focus on the study of incompleteness and contradictory nature of patterns in scanty historical earthquake records. Information diffusion method is employed to construct fuzzy relationships which are equal to the number of observations. Integration of the relationships can change the contradictory patterns into more compatible ones which, in turn, can smoothly and quickly train the feedforward neural network with backpropagation algorithm (BP) to obtain the final relationship. A practical application is employed to show the superiority of the model. @ 1999 Elsevier Science B.V. All rights reserved.

Question: How to estimate the relationship between isoseismal area and earthquake magnitude?


Multi-style paper pop-up designs from 3D models. Paper pop-ups are interesting three-dimensional books that fascinate people of all ages. The design and construction of these pop-up books however are done manually and require a lot of time and effort. This has led to computer-assisted or automated tools for designing paper pop-ups. This paper proposes an approach for automatically converting a 3D model into a multi-style paper pop-up. Previous automated approaches have only focused on single-style pop-ups, where each is made of a single type of pop-up mechanisms. In our work, we combine multiple styles in a pop-up, which is more representative of actual artist's creations. Our method abstracts a 3D model using suitable primitive shapes that both facilitate the formation of the considered pop-up mechanisms and closely approximate the input model. Each shape is then abstracted using a set of 2D patches that combine to form a valid pop-up. We define geometric conditions that ensure the validity of the combined pop-up structures. In addition, our method also employs an image-based approach for producing the patches to preserve the textures, finer details and important contours of the input model. Finally, our system produces a printable design layout and decides an assembly order for the construction instructions. The feasibility of our results is verified by constructing the actual paper pop-ups from the designs generated by our system.

Question: How to design a paper pop-up?


Bidirectional Expansion For Keyword Search on Graph Databases. Relational, XML and HTML data can be represented as graphs with entities as nodes and relationships as edges. Text is associated with nodes and possibly edges. Keyword search on such graphs has received much attention lately. A central problem in this scenario is to efficiently extract from the data graph a small number of the "best" answer trees. A Backward Expanding search, starting at nodes matching keywords and working up toward confluent roots, is commonly used for predominantly text-driven queries. But it can perform poorly if some keywords match many nodes, or some node has very large degree. In this paper we propose a new search algorithm, Bidirectional Search, which improves on Backward Expanding search by allowing forward search from potential roots towards leaves. To exploit this flexibility, we devise a novel search frontier prioritization technique based on spreading activation. We present a performance study on real data, establishing that Bidirectional Search significantly outperforms Backward Expanding search.

Question: What is the difference between a graph database and a relational database?


Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. SUMMARY Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools.   AVAILABILITY Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.

Question: What is the name of the software?


Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network. Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word representations (word2vec). Next, we make use of the distributed word representations learned to train a deep feed forward neural network. We test our deep neural network by recommending hashtags for tweets with user-assigned hashtags, using Mean Squared Error (MSE) as the objective function. We also test our deep neural network by recommending hashtags for tweets without user-assigned hashtags. Our experimental results show that the proposed approach recommends hashtags that are specific to the semantics of the tweets and that preserve the linguistic regularity of the tweets. In addition, our experimental results show that the proposed approach is capable of generating hashtags that have not been seen before.

Question: What is a skip-gram model?


Survey on Hadoop and Introduction to YARN Amogh. Big Data, the analysis of large quantities of data to gain new insight has become a ubiquitous phrase in recent years. Day by day the data is growing at a staggering rate. One of the efficient technologies that deal with the Big Data is Hadoop, which will be discussed in this paper. Hadoop, for processing large data volume jobs uses MapReduce programming model. Hadoop makes use of different schedulers for executing the jobs in parallel. The default scheduler is FIFO (First In First Out) Scheduler. Other schedulers with priority, pre-emption and non-pre-emption options have also been developed. As the time has passed the MapReduce has reached few of its limitations. So in order to overcome the limitations of MapReduce, the next generation of MapReduce has been developed called as YARN (Yet Another Resource Negotiator). So, this paper provides a survey on Hadoop, few scheduling methods it uses and a brief introduction to YARN. Keywords—Hadoop, HDFS, MapReduce, Schedulers, YARN.

Question: What is the difference between Hadoop and YARN?


Neural Embeddings of Graphs in Hyperbolic Space. ABSTRACT Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted signi cant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in highdimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the at Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry signi cantly improves performance on downstream tasks for several real-world public datasets.

Question: What is the appropriate isometric space for embedding complex networks?


Human detection in surveillance videos and its applications - a review. Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.

Question: What is the future of human detection in surveillance videos?


FRACTIONAL DIFFERENTIAL EQUATIONS IN BANACH SPACES. This paper deals with some impulsive fractional differential equations in Banach spaces. Utilizing the Leray-Schauder fixed point theorem and the impulsive nonlinear singular version of the Gronwall inequality, the existence of PC-mild solutions for some fractional differential equations with impulses are obtained under some easily checked conditions. At last, an example is given for demonstration.

Question: What is the fractional differential equation?


Multiple memory systems as substrates for multiple decision systems. It has recently become widely appreciated that value-based decision making is supported by multiple computational strategies. In particular, animal and human behavior in learning tasks appears to include habitual responses described by prominent model-free reinforcement learning (RL) theories, but also more deliberative or goal-directed actions that can be characterized by a different class of theories, model-based RL. The latter theories evaluate actions by using a representation of the contingencies of the task (as with a learned map of a spatial maze), called an "internal model." Given the evidence of behavioral and neural dissociations between these approaches, they are often characterized as dissociable learning systems, though they likely interact and share common mechanisms. In many respects, this division parallels a longstanding dissociation in cognitive neuroscience between multiple memory systems, describing, at the broadest level, separate systems for declarative and procedural learning. Procedural learning has notable parallels with model-free RL: both involve learning of habits and both are known to depend on parts of the striatum. Declarative memory, by contrast, supports memory for single events or episodes and depends on the hippocampus. The hippocampus is thought to support declarative memory by encoding temporal and spatial relations among stimuli and thus is often referred to as a relational memory system. Such relational encoding is likely to play an important role in learning an internal model, the representation that is central to model-based RL. Thus, insofar as the memory systems represent more general-purpose cognitive mechanisms that might subserve performance on many sorts of tasks including decision making, these parallels raise the question whether the multiple decision systems are served by multiple memory systems, such that one dissociation is grounded in the other.

Question: What is the difference between declarative and procedural memory?


Comparing Two IRT Models for Conjunctive Skills. A step in ITS often involve multiple skills. Thus a step requiring a conjunction of skills is harder than steps that require requiring each individual skill only. We developed two Item-Response Models – Additive Factor Model (AFM) and Conjunctive Factor Model (CFM) – to model the conjunctive skills in the student data sets. Both models are compared on simulated data sets and a real assessment data set. We showed that CFM was as good as or better than AFM in the mean cross validation errors on the simulated data. In the real data set CFM is not clearly better. However, AFM is essentially performing as a conjunctive model.

Question: How to compare two IRT models?


Visualizing Semantic Table Annotations with TableMiner+. This paper describes an extension of the TableMiner system, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an effective and efficient approach. It adds a graphical user interface to TableMiner, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web.

Question: What is a Semantic Table Interpretation system?


Pulse-Modulated Intermittent Control in Consensus of Multiagent Systems. This paper proposes a control framework, called pulse-modulated intermittent control, which unifies impulsive control and sampled control. Specifically, the concept of pulse function is introduced to characterize the control/rest intervals and the amplitude of the control. By choosing some specified functions as the pulse function, the proposed control scheme can be reduced to sampled control or impulsive control. The proposed control framework is applied to consensus problems of multiagent systems. Using discretization approaches and stability theory, several necessary and sufficient conditions are established to ensure the consensus of the controlled system. The results show that consensus depends not only on the network topology, the sampling period and the control gains, but also the pulse function. Moreover, a lower bound of the asymptotic convergence factor is derived as well. For a given pulse function and an undirected graph, an optimal control gain is designed to achieve the fastest convergence. In addition, impulsive control and sampled control are revisited in the proposed control framework. Finally, some numerical examples are given to verify the effectiveness of theoretical results.

Question: What is the difference between impulsive control and sampled control?


Automatic creation of WordNets from parallel corpora. In this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We have used three very large parallel corpora for our experiments: DGT-TM, EMEA and ECB. The English part of each corpus is semantically tagged using Freeling and UKB. After this step, the process of WordNet creation is converted into a word alignment problem, where we want to align WordNet synsets in the English part of the corpus with lemmata on the target language part of the corpus. The word alignment algorithm used in these experiments is a simple most frequent translation algorithm implemented into the WN-Toolkit. The obtained precision values are quite satisfactory, but the overall number of extracted synset-variant pairs is too low, leading into very poor recall values. In the conclusions, the use of more advanced word alignment algorithms, such as Giza++, Fast Align or Berkeley aligner is suggested.

Question: How to create wordnets from parallel corpora?


The magical number seven plus or minus two: some limits on our capacity for processing information.. My problem is that I have been persecuted by an integer. For seven years this number has followed me around, has intruded in my most private data, and has assaulted me from the pages of our most public journals. This number assumes a variety of disguises, being sometimes a little larger and sometimes a little smaller than usual, but never changing so much as to be unrecognizable. The persistence with which this number plagues me is far more than a random accident. There is, to quote a famous senator, a design behind it, some pattern governing its appearances. Either there really is something unusual about the number or else I am suffering from delusions of persecution. I shall begin my case history by telling you about some experiments that tested how accurately people can assign numbers to the magnitudes of various aspects of a stimulus. In the traditional language of psychology these would be called experiments in absolute

Question: What is the magical number seven plus or minus two?


Spacer-First Damascene-Gate FinFET Architecture Featuring Stringer-Free Integration. This letter presents a new Damascene-gate FinFET process that inherently suppresses stringers, resulting from gate and spacers patterning. The so-called spacer-first integration scheme relies on the engineering of a hydrogen silsesquioxane layer by electron beam lithography followed by two selective compartmentalized development steps to successively release the Damascene-gate cavity and the source/drain (S/D) contact regions. In contrast to the existing gate-first and gate-last integration approaches, the resulting FinFET process does not impose any restriction or interdependency on the sizing of the fins, gate, spacers, and S/D regions. A complete morphological and electrical validation is proposed in the particular case of wrap-around self-aligned metallic Schottky S/D contacts.

Question: What is the difference between the spacer-first and gate-first integration schemes?


ORCHID : Thai Part-Of-Speech Tagged Corpus. This paper presents a procedure in building a Thai part-of-speech (POS) tagged corpus named ORCHID [1]. It is a collaboration project between Communications Research Laboratory (CRL) of Japan and National Electronics and Computer Technology Center (NECTEC) of Thailand. We proposed a new tagset based on the previous research on Thai parts-of-speech for using in a multi-lingual machine translation project. We marked the corpus in three levels:paragraph, sentence and word. The corpus keeps text information in text information line and numbering line which are necessary in retrieving process. Since there are no explicit word/sentence boundary, punctuation and inflection in Thai text, we have to separate a paragraph into sentences before tagging the POS. We applied a probabili stic trigram model for simultaneously word segmenting and POS tagging. Rule for syllable construction is additionally used to reduce the number of candidates for computing the probabili ty. The problems in POS assignment are formalized to reduce the ambiguity occurring in case of the similar POSs.

Question: How to build a Thai part-of-speech tagged corpus?


Real-time Simulation of Large Bodies of Water with Small Scale Details. We present a hybrid water simulation method that combines grid based and particles based approaches. Our specialized shallow water solver can handle arbitrary underlying terrain slopes, arbitrary water depth and supports wet-dry regions tracking. To treat open water scenes we introduce a method for handling non-reflecting boundary conditions. Regions of liquid that cannot be represented by the height field including breaking waves, water falls and splashing due to rigid and soft bodies interaction are automatically turned into spray, splash and foam particles. The particles are treated as simple non-interacting point masses and they exchange mass and momentum with the height field fluid. We also present a method for procedurally adding small scale waves that are advected with the water flow. We demonstrate the effectiveness of our method in various test scene including a large flowing river along a valley with beaches, big rocks, steep cliffs and waterfalls. Both the grid and the particles simulations are implemented in CUDA. We achieve real-time performance on modern GPUs in all the examples.

Question: How large is the canadian military?


Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges. Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

Question: What is the difference between the user-item matrix and the user-user matrix?


Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications. The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.

Question: What is the Internet of Things?


Information Fusion in the Immune System. Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.

Question: What is the aim of this paper?


A RSSI-based and calibrated centralized localization technique for wireless sensor networks. This paper presents a multi-hop localization technique for WSNs exploiting acquired received signal strength indications. The proposed system aims at providing an effective solution for the self localization of nodes in static/semi-static wireless sensor networks without requiring previous deployment information

Question: What is RSSI-based and calibrated centralized localization technique for wireless sensor networks?


Acceptance of homegrown enterprise resource planning (ERP) systems in Ethiopia. In the current competitive global market, organizations are implementing information and communication technology (ICT) that could add value to their products, processes, and satisfaction of their users. The adoption, implementation and use of homegrown enterprise resource planning (ERP) systems is one of these mechanisms being globally used for recording, processing, storing, and exchanging organizational information anytime anywhere. Although organizations have been utilizing ERP systems, the acceptance of homegrown ERP systems is given less attention as compared to commercial off-the shelf (COTS) software. Hence, this research studied factors that determine acceptance of homegrown ERP through the extension of unified theory of acceptance and use of technology (UTAUT) model. The finding revealed that performance expectancy, effort expectancy, social influence, competitive advantage, cost effectiveness, and facilitations functions are determinants of homegrown ERP system acceptance in Ethiopia. Moreover, experience and voluntariness are found to be significant moderators of the study.

Question: What is the difference between homegrown and commercial ERP systems?


A privacy-compliant fingerprint recognition system based on homomorphic encryption and Fingercode templates. The privacy protection of the biometric data is an important research topic, especially in the case of distributed biometric systems. In this scenario, it is very important to guarantee that biometric data cannot be steeled by anyone, and that the biometric clients are unable to gather any information different from the single user verification/identification. In a biométrie system with high level of privacy compliance, also the server that processes the biométrie matching should not learn anything on the database and it should be impossible for the server to exploit the resulting matching values in order to extract any knowledge about the user presence or behavior. Within this conceptual framework, in this paper we propose a novel complete demonstrator based on a distributed biométrie system that is capable to protect the privacy of the individuals by exploiting cryptosystems. The implemented system computes the matching task in the encrypted domain by exploiting homomorphic encryption and using Fingercode templates. The paper describes the design methodology of the demonstrator and the obtained results. The demonstrator has been fully implemented and tested in real applicative conditions. Experimental results show that this method is feasible in the cases where the privacy of the data is more important than the accuracy of the system and the obtained computational time is satisfactory.

Question: How to protect the privacy of the data?


Broadband compact microstrip patch antenna design loaded by multiple split ring resonator superstrate and substrate. We present microstrip patch antenna loaded with multiple split ring resonator substrate and superstrate. We analyze how the loading of split ring resonator superstrate and substrate can improve the bandwidth compared to the simple microstrip patch antenna and microstrip patch antenna loaded with split ring resonator superstrate. Another important observation is made for multiple split ring resonator loading in superstrate and substrate of microstrip patch antenna. The design is compared for two, three, and four-ring split ring resonator loading. The designs are also compared for different gap spacing between the rings. All three designs are compared for small gap and large gap between the rings. The design results in the form of reflection coefficient and bandwidth is presented in this manuscript. The design results are also compared with previously published designs.

Question: What is the bandwidth of the microstrip patch antenna?


Comprehensive Dataset of Broadcast Soccer Videos. In the absence of a rich dataset, sports video analysis remains a challenging task. Shot analysis, event detection, and player tracking are important aspects of soccer game research, however currently available datasets focus mainly on single-type annotations, and have several limitations when applied to full video analysis; these include a lack of training samples, temporal annotations, and rich labels of different types. In this paper, we focus on broadcast soccer videos and present a comprehensive dataset for analysis. In its current version, the dataset contains 222 broadcast soccer videos, totaling 170 video hours. The dataset covers three annotation types: (1) A shot boundary with two shot transition types, and shot type annotations with five shot types; (2) Event annotations with 11 event labels, and a story annotation with 15 story labels at coarser granularity; and (3) bounding boxes of the players under analysis in a subset of 19908 video frames. We hope that the introduction of this dataset will enable the research community to develop soccer video analysis techniques.

Question: What is a comprehensive dataset of broadcast soccer videos?


Electrodermal responses: what happens in the brain.. Electrodermal activity (EDA) is now the preferred term for changes in electrical conductance of the skin, including phasic changes that have been referred to as galvanic skin responses (GSR), that result from sympathetic neuronal activity. EDA is a sensitive psychophysiological index of changes in autonomic sympathetic arousal that are integrated with emotional and cognitive states. Until recently there was little direct knowledge of brain mechanisms governing generation and control of EDA in humans. However, studies of patients with discrete brain lesions and, more recently, functional imaging techniques have clarified the contribution of brain regions implicated in emotion, attention, and cognition to peripheral EDA responses. Moreover, such studies enable an understanding of mechanisms by which states of bodily arousal, indexed by EDA, influence cognition and bias motivational behavior.

Question: What is the difference between electrodermal activity and galvanic skin response?


THE ROLE OF DIGITAL AND SOCIAL MEDIA MARKETING IN CONSUMER BEHAVIOR. This article reviews recently published research about consumers in digital and social media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile environments, and (v) online word of mouth (WOM). Collectively these articles shed light from many different angles on how consumers experience, influence, and are influenced by the digital environments in which they are situated as part of their daily lives. Much is still to be understood, and existing knowledge tends to be disproportionately focused on WOM, which is only part of the digital consumer experience. Several directions for future research are advanced to encourage researchers to consider a broader range of phenomena.

Question: What is the role of digital and social media marketing in consumer behavior?


Representation of Events in Nerve Nets and Finite Automata.

Question: Representation of Events in Nerve Nets and Finite Automata


Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems. Virtualization facilitates the provision of flexible resources and improves energy efficiency through the consolidation of virtualized servers into a smaller number of physical servers. As an increasingly essential component of the emerging cloud computing model, virtualized environments bill their users based on processor time or the number of virtual machine instances. However, accounting based only on the depreciation of server hardware is not sufficient because the cooling and energy costs for data centers will exceed the purchase costs for hardware. This paper suggests a model for estimating the energy consumption of each virtual machine without dedicated measurement hardware. Our model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual machine. Based on this estimation model, we also propose a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of each virtual machine. The suggested schemes are implemented in the Xen virtualization system, and an evaluation shows that the suggested schemes estimate and provide energy consumption with errors of less than 5% of the total energy

Question: What is the energy consumption of a virtual machine?


brat: a Web-based Tool for NLP-Assisted Text Annotation. We introduce the brat rapid annotation tool (BRAT), an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annotation for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. We discuss several case studies of real-world annotation projects using pre-release versions of BRAT and present an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15% decrease in total annotation time. BRAT is available under an opensource license from: http://brat.nlplab.org

Question: What is a web-based tool for text annotation?


HotFlip: White-Box Adversarial Examples for NLP. We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the onehot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.

Question: How to generate white-box adversarial examples for NLP?


Study on Bluetooth technology and its access to BACnet. Bluetooth technology is now widely used in wireless application systems; however, wireless access service is not involved in BACnet. This paper proposes a design of Bluetooth/BACnet gateway after analyzing the architecture of Bluetooth and BACnet, and gives the implementation of the gateway based on MCU LPC2210(ARM7).

Question: What is the difference between Bluetooth and BACnet?


Lifetime Portfolio Selection By Dynamic Stochastic Programming. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How to use JSTOR?


Torus data center network with smart flow control enabled by hybrid optoelectronic routers [Invited]. A torus-topology photonic data center network with smart flow control is presented, where optical packet switching (OPS), optical circuit switching (OCS), and virtual OCS (VOCS) schemes are all enabled on a unified hardware platform via the combination of hybrid optoelectronic routers (HOPR) and a centralized network controller. An upgraded HOPR is being developed with the goal of handling 100 Gbps optical packets with a high energy efficiency of 90 mW/Gbps and low latency of less than 100 ns. The architecture of HOPR and its enabling technologies are reviewed, including a label processor, optical switch, and optoelectronic shared buffer. We also explain the concept and operation mechanisms of both the data and control planes in the hybrid OPS/OCS/VOCS torus network. The performance of these three transmission modes is evaluated by numerical simulations for a network composed of 4096 HOPRs.

Question: What is the torus data center network?


Big Data analytics. Three use cases with R, Python and Spark. Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained by the direct use of three reference environments : R, Python Scikit-learn, ∗Université de Toulouse – INSA, Institut de Mathématiques, UMR CNRS 5219 †Institut de Mathématiques, UMR CNRS 5219 ‡Université de Toulouse – UT3, Institut de Mathématiques, UMR CNRS 5219 1 ar X iv :1 60 9. 09 61 9v 1 [ st at .A P] 3 0 Se p 20 16 Spark MLlib on three public use cases : character recognition, recommending films, categorizing products. As main result, it appears that, if Spark is very efficient for data munging and recommendation by collaborative filtering (non-negative factorization), current implementations of conventional learning methods (logistic regression, random forests) in MLlib or SparkML do not ou poorly compete habitual use of these methods (R, Python Scikit-learn) in an integrated or undistributed architecture.

Question: What is the difference between Spark and Hadoop?


A Pneumatic-Driven Haptic Glove with Force and Tactile Feedback. The advent of Oculus Rift indicates the start of a booming era of virtual reality. In order to increase the immersive feeling of interaction with the virtual world, haptic devices allow us to touch and manipulate virtual objects in an intuitive way. In this paper, we introduce a portable and low-cost haptic glove that provides both force and tactile feedback using a direct-control pneumatic concept. To produce force feedback, two inlet ports of a double acting pneumatic cylinder are opened and closed via solenoid DC valves through Pulse-width modulation (PWM) technique. For tactile feedback, an air bladder is actuated using a diaphragm pump via PWM operated solenoid valve. Experiments on a single finger prototype validated that the glove can provide force and tactile feedback with sufficient moving range of the finger joints. The maximum continuous force is 9 Newton and the response time is less than 400ms. The glove is light weighted and easy to be mounted on the index finger. The proposed glove could be potentially used for virtual reality grasping scenarios and for teleoperation of a robotic hand for handling hazardous objects.

Question: What is a haptic glove?


One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations. Discourse relations bind smaller linguistic units into coherent texts. Automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lowerlevel components, such as entity mentions. Our solution computes distributed meaning representations for each discourse argument by composition up the syntactic parse tree. We also perform a downward compositional pass to capture the meaning of coreferent entity mentions. Implicit discourse relations are then predicted from these two representations, obtaining substantial improvements on the Penn Discourse Treebank.

Question: What is the meaning of the sentence?


Comparative study of automatic speech recognition techniques. Over the past decades, extensive research has been carried out on various possible implementations of automatic speech recognition (ASR) systems. The most renowned algorithms in the field of ASR are the mel-frequency cepstral coefficients and the hidden Markov models. However, there are also other methods, such as wavelet-based transforms, artificial neural networks and support vector machines, which are becoming more popular. This review article presents a comparative study on different approaches that were proposed for the task of ASR, and which are widely used nowadays.

Question: What is the most popular algorithm for automatic speech recognition?