Impact of User Pairing on 5G Non-Orthogonal Multiple Access. Non-orthogonal multiple access (NOMA) represents a paradigm shift from conventional orthogonal multiple access (MA) concepts, and has been recognized as one of the key enabling technologies for 5G systems. In this paper, the imp act of user pairing on the performance of two NOMA systems, NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), is characterized. For FNOMA, both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F-NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive. For CR-NOMA, the quality of service (QoS) for users with the poorer channe l condition can be guaranteed since the transmit power alloca ted to other users is constrained following the concept of cogniti ve radio networks. Because of this constraint, CR-NOMA has different behavior compared to F-NOMA. For example, for the user with the best channel condition, CR-NOMA prefers to pair it with the user with the second best channel condition, whereas the use r with the worst channel condition is preferred by F-NOMA. I. I NTRODUCTION Multiple access in 5G mobile networks is an emerging research topic, since it is key for the next generation netwo rk to keep pace with the exponential growth of mobile data and multimedia traffic [1] and [2]. Non-orthogonal multiple acc ess (NOMA) has recently received considerable attention as a promising candidate for 5G multiple access [3]–[6]. Partic ularly, NOMA uses the power domain for multiple access, where different users are served at different power levels. The users with better channel conditions employ successive int erference cancellation (SIC) to remove the messages intended for other users before decoding their own [7]. The benefit of usin g NOMA can be illustrated by the following example. Consider th

Question: What is the difference between NOMA and orthogonal multiple access?


Convolutional recurrent neural networks: Learning spatial dependencies for image representation. In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.

Question: What is the difference between convolutional neural networks and recurrent neural networks?


A FRAMEWORK FOR DESIGNING MOBILE QURANIC MEMORIZATION TOOL USING MULTIMEDIA INTERACTIVE LEARNING METHOD FOR CHILDREN. Quran is the fundamental holy book of Islam. Among the most important concerns is to learn it by heart. However, current method in Quranic schools is becoming less effective towards young generation. Thus, there is a need to find alternative solutions to memorize Quran for young Muslims. Mobile learning is an alternative to conventional learning that will support the existing method. Mobile devices have made an immediate impact on teaching and learning practices. However, for mobile learning to be effective for children in memorizing Quran, it is necessary to find specific design guidelines and pedagogy to this learning method. This paper aims at providing a unifying framework for developing Quran memorizer application using multimedia interactive method and learning theories for mobile learning.

Question: What is the unifying framework for designing mobile Quranic memorization tool?


An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning. In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people's daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users' privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL), to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

Question: What is the difference between the adaptive privacy protection method and the existing methods?


Inter-AS traffic engineering with SDN. Egress selection in an Internet Service Provider (ISP) is the process of selecting an egress router to route interdomain traffic across the ISP such that a traffic engineering objective is achieved. In traditional ISP networks, traffic through the ISP is carried through the network and exits via an egress that is closest to the source in an attempt to minimize network resources for transit traffic. This exit strategy is known as Hot Potato Routing (HPR). The emerging field of Software-Defined Networking (SDN) has opened up many possibilities and promised to bring new flexibility to the rigid traditional paradigm of networking. In an ISP network, however, completely replacing legacy network devices with SDN nodes is neither simple nor straightforward. This has led to the idea of incremental and selective deployment of SDN nodes in an ISP network. Such a hybrid network gives us control over traffic flows that pass through the SDN nodes without requiring extensive changes to an existing ISP network. In this paper, we look at the problem of choosing an optimal set of egress routers to route inter-domain transit traffic in a hybrid SDN network such that the maximum link utilization of the egress links is minimized. We formulate the optimization problem, show that it is related to the makespan scheduling problem of unrelated parallel machines, and propose heuristics to solve it. We perform simulations to evaluate our heuristic on a real ISP topology and show that even with a small number of SDN nodes in the network, the maximum link utilization on the egress links can be lower than that of traditional HPR.

Question: What is the difference between inter-AS and inter-domain traffic engineering?


Age differences in the neural systems supporting human allocentric spatial navigation. Age-related declines in spatial navigation are well-known in human and non-human species. Studies in non-human species suggest that alteration in hippocampal and other neural circuitry may underlie behavioral deficits associated with aging but little is known about the neural mechanisms of human age-related decline in spatial navigation. The purpose of the present study was to examine age differences in functional brain activation during virtual environment navigation. Voxel-based analysis of activation patterns in young subjects identified activation in the hippocampus and parahippocampal gyrus, retrosplenial cortex, right and left lateral parietal cortex, medial parietal lobe and cerebellum. In comparison to younger subjects, elderly participants showed reduced activation in the hippocampus and parahippocampal gyrus, medial parietal lobe and retrosplenial cortex. Relative to younger participants elderly subjects showed increased activation in anterior cingulate gyrus and medial frontal lobe. These results provide evidence of age specific neural networks supporting spatial navigation and identify a putative neural substrate for age-related differences in spatial memory and navigational skill.

Question: What is the purpose of the study?


Question: What is the difference between the architecture tradeoff analysis method and the cost benefit analysis method?


Twin Clouds: An Architecture for Secure Cloud Computing (Extended Abstract). Cloud computing promises a more cost effective enabling technology to outsource storage and computations. Existing approaches for secure outsourcing of data and arbitrary computations are either based on a single tamper-proof hardware, or based on recently proposed fully homomorphic encryption. The hardware based solutions are not scaleable, and fully homomorphic encryption is currently only of theoretical interest and very inefficient. In this paper we propose an architecture for secure outsourcing of data and arbitrary computations to an untrusted commodity cloud. In our approach, the user communicates with a trusted cloud (either a private cloud or built from multiple secure hardware modules) which encrypts and verifies the data stored and operations performed in the untrusted commodity cloud. We split the computations such that the trusted cloud is mostly used for security-critical operations in the less time-critical setup phase, whereas queries to the outsourced data are processed in parallel by the fast commodity cloud on encrypted data.

Question: What is the architecture for secure outsourcing of data and arbitrary computations?


Ranking Scientific Articles in a Dynamically Evolving Citation Network. Scientific ranking has long been a hot and important topic in both computer science and scientometrics. A lot of statistics-based and graph-based methods have been proposed for calculating a prestige value as the assessment of each paper's scientific influence. However, being ignorant of the dynamic nature of scientific publication and science evolution, all these methods present a biased point of view of scientific influence. Besides, the ranking results of these methods are not accessible to users because of lack of an explainable model. As an alternative to the state-of-the-art, this paper proposes a cognitively explainable model by integrating three factors in scientific development including knowledge accumulation by individual papers, knowledge diffusion through citation behaviour and knowledge decay with time elapse. Evaluated on ACL Anthology Network using the reference lists of four textbooks or handbooks as the gold standard, the proposed model is proved to be effective in scientific ranking and potential for new insights into the definition and measurement of scientific influence.

Question: How to rank scientific articles?


A Novel, Planar, and Compact Crossover Design for Dual-Band Applications. This paper presents, for the first time, the novel design of a dual-band crossover. The proposed circuit features planar implementation with moderate line impedance, compact layout, and widely separated frequency bands. Moreover, simple and explicit closed-form expressions are available for the exact evaluation of the line impedances. For verification, both simulated and measured results of a microstrip crossover operating at 800 MHz and 1.2 GHz are given.

Question: What is a dual-band crossover?


The Golden Beauty: Brain Response to Classical and Renaissance Sculptures. Is there an objective, biological basis for the experience of beauty in art? Or is aesthetic experience entirely subjective? Using fMRI technique, we addressed this question by presenting viewers, naïve to art criticism, with images of masterpieces of Classical and Renaissance sculpture. Employing proportion as the independent variable, we produced two sets of stimuli: one composed of images of original sculptures; the other of a modified version of the same images. The stimuli were presented in three conditions: observation, aesthetic judgment, and proportion judgment. In the observation condition, the viewers were required to observe the images with the same mind-set as if they were in a museum. In the other two conditions they were required to give an aesthetic or proportion judgment on the same images. Two types of analyses were carried out: one which contrasted brain response to the canonical and the modified sculptures, and one which contrasted beautiful vs. ugly sculptures as judged by each volunteer. The most striking result was that the observation of original sculptures, relative to the modified ones, produced activation of the right insula as well as of some lateral and medial cortical areas (lateral occipital gyrus, precuneus and prefrontal areas). The activation of the insula was particularly strong during the observation condition. Most interestingly, when volunteers were required to give an overt aesthetic judgment, the images judged as beautiful selectively activated the right amygdala, relative to those judged as ugly. We conclude that, in observers naïve to art criticism, the sense of beauty is mediated by two non-mutually exclusive processes: one based on a joint activation of sets of cortical neurons, triggered by parameters intrinsic to the stimuli, and the insula (objective beauty); the other based on the activation of the amygdala, driven by one's own emotional experien

Question: What is the golden beauty?


Transformational leadership in nursing: a concept analysis.. AIM To analyse the concept of transformational leadership in the nursing context.   BACKGROUND Tasked with improving patient outcomes while decreasing the cost of care provision, nurses need strategies for implementing reform in health care and one promising strategy is transformational leadership. Exploration and greater understanding of transformational leadership and the potential it holds is integral to performance improvement and patient safety.   DESIGN Concept analysis using Walker and Avant's (2005) concept analysis method.   DATA SOURCES PubMed, CINAHL and PsychINFO.   METHODS This report draws on extant literature on transformational leadership, management, and nursing to effectively analyze the concept of transformational leadership in the nursing context.   IMPLICATIONS FOR NURSING This report proposes a new operational definition for transformational leadership and identifies model cases and defining attributes that are specific to the nursing context. The influence of transformational leadership on organizational culture and patient outcomes is evident. Of particular interest is the finding that transformational leadership can be defined as a set of teachable competencies. However, the mechanism by which transformational leadership influences patient outcomes remains unclear.   CONCLUSION Transformational leadership in nursing has been associated with high-performing teams and improved patient care, but rarely has it been considered as a set of competencies that can be taught. Also, further research is warranted to strengthen empirical referents; this can be done by improving the operational definition, reducing ambiguity in key constructs and exploring the specific mechanisms by which transformational leadership influences healthcare outcomes to validate subscale measures.

Question: What is the difference between transformational leadership and transformational management?


A Random Linear Network Coding Approach to Multicast. We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks

Question: What is a random linear network coding approach?


Guided filtering for PRNU-based localization of small-size image forgeries. PRNU-based techniques guarantee a good forgery detection performance irrespective of the specific type of forgery. The presence or absence of the camera PRNU pattern is detected by a correlation test. Given the very low power of the PRNU signal, however, the correlation must be averaged over a pretty large window, reducing the algorithm's ability to reveal small forgeries. To improve resolution, we estimate correlation with a spatially adaptive filtering technique, with weights computed over a suitable pilot image. Implementation efficiency is achieved by resorting to the recently proposed guided filters. Experiments prove that the proposed filtering strategy allows for a much better detection performance in the case of small forgeries.

Question: How to detect small forgeries?


The impact of social and conventional media on firm equity value: A sentiment analysis approach. Available online 30 December 2012

Question: How to find the impact of social and conventional media on firm equity value: A sentiment analysis approach.


Piecewise smooth surface reconstruction. We present a general method for automatic reconstruction of accurate, concise, piecewise smooth surface models from scattered range data. The method can be used in a variety of applications such as reverse engineering—the automatic generation of CAD models from physical objects. Novel aspects of the method are its ability to model surfaces of arbitrary topological type and to recover sharp features such as creases and corners. The method has proven to be effective, as demonstrated by a number of examples using both simulated and real data. A key ingredient in the method, and a principal contribution of this paper, is the introduction of a new class of piecewise smooth surface representations based on subdivision. These surfaces have a number of properties that make them ideal for use in surface reconstruction: they are simple to implement, they can model sharp features concisely, and they can be fit to scattered range data using an unconstrained optimization procedure.

Question: What is a piecewise smooth surface?


Unsupervised deformable image registration with fully connected generative neural network. In this paper, a new deformable image registration method based on a fully connected neural network is proposed. Even though a deformation field related to the point correspondence between fixed and moving images are high-dimensional in nature, we assume that these deformation fields form a low dimensional manifold in many real world applications. Thus, in our method, a neural network generates an embedding of the deformation field from a low dimensional vector. This lowdimensional manifold formulation avoids the intractability associated with the high dimensional search space that most other methods face during image registration. As a result, while most methods rely on explicit and handcrafted regularization of the deformation fields, our algorithm relies on implicitly regularizing the network parameters. The proposed method generates deformation fields from latent low dimensional space by minimizing a dissimilarity metric between a fixed image and a warped moving image. Our method removes the need for a large dataset to optimize the proposed network. The proposed method is quantitatively evaluated using images from the MICCAI ACDC challenge. The results demonstrate that the proposed method improves performance in comparison with a moving mesh registration algorithm, and also it correlates well with independent manual segmentations by an expert.

Question: How does the proposed method improve performance in comparison with a moving mesh registration algorithm?


Heuristic evaluation of user interfaces. Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four experiments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found between 20 and 51% of the usability problems in the interfaces they evaluated. On the other hand, we could aggregate the evaluations from several evaluators to a single evaluation and such aggregates do rather well, even when they consist of only three to five people.

Question: How many people are needed to do heuristic evaluation?


Polynomial method for Procedural Terrain Generation. A systematic fractal brownian motion approach is proposed for generating coherent noise, aiming at procedurally generating realistic terrain and textures. Two models are tested and compared to Perlin noise method for two-dimensional height map generation. A fractal analysis is performed in order to compare fractal behaviour of generated data to real terrain coastlines from the point of view of fractal dimension. Performance analysis show that one of the described schemes requires half as many primitive operations than Perlin noise while producing data of equivalent quality.

Question: What is a fractal?


Better Summarization Evaluation with Word Embeddings for ROUGE. ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank co-

Question: What is the best summarization evaluation measure?


Multilingual speech-to-speech translation system for mobile consumer devices. Along with the advancement of speech recognition technology and machine translation technology in addition to the fast distribution of mobile devices, speech-to-speech translation technology no longer remains as a subject of research as it has become popularized throughout many users. In order to develop a speech-to-speech translation system that can be widely used by many users, however, the system needs to reflect various characteristics of utterances by the users who are actually to use the speech-to-speech translation system other than improving the basic functions under the experimental environment. This study has established a massive language and speech database closest to the environment where speech-to- speech translation device actually is being used after mobilizing plenty of people based on the survey on users' demands. Through this study, it was made possible to secure excellent basic performance under the environment similar to speech-to-speech translation environment, rather than just under the experimental environment. Moreover, with the speech-to-speech translation UI, a user-friendly UI has been designed; and at the same time, errors were reduced during the process of translation as many measures to enhance user satisfaction were employed. After implementing the actual services, the massive database collected through the service was additionally applied to the system following a filtering process in order to procure the best-possible robustness toward both the details and the environment of the users' utterances. By applying these measures, this study is to unveil the procedures where multi-language speech-to-speech translation system has been successfully developed for mobile devices.

Question: How to develop a speech-to-speech translation system for mobile consumer devices?


The Timeboxing process model for iterative software development. In today's business where speed is of essence, an iterative development approach that allows the functionality to be delivered in parts has become a necessity and an effective way to manage risks. In an iterative process, the development of a software system is done in increments, each increment forming of an iteration and resulting in a working system. A common iterative approach is to decide what should be developed in an iteration and then plan the iteration accordingly. A somewhat different iterative is approach is to time box different iterations. In this approach, the length of an iteration is fixed and what should be developed in an iteration is adjusted to fit the time box. Generally, the time boxed iterations are executed in sequence, with some overlap where feasible. In this paper we propose the timeboxing process model that takes the concept of time boxed iterations further by adding pipelining concepts to it for permitting overlapped execution of different iterations. In the timeboxing process model, each time boxed iteration is divided into equal length stages, each stage having a defined function and resulting in a clear work product that is handed over to the next stage. With this division into stages, pipelining concepts are employed to have multiple time boxes executing concurrently, leading to a reduction in the delivery time for product releases. We illustrate the use of this process model through an example of a commercial project that was successfully executed using the proposed model.

Question: What is the timeboxing process model?


Information Systems and Healthcare XVI: Physician Adoption of Electronic Medical Records: Applying the UTAUT Model in a Healthcare Context. This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to the phenomenon of physician adoption of electronic medical records (EMR) technology. UTAUT integrates eight theories of individual acceptance into one comprehensive model designed to assist in understanding what factors either enable or hinder technology adoption and use. As such, it provides a useful lens through which to view what is currently taking place in the healthcare industry regarding EMR adoption. This is mutually beneficial to both the healthcare and MIS communities, as UTAUT offers valuable practical insight to the healthcare industry in explaining why EMR technology has not been more widely adopted as well as what prescriptions may facilitate future adoption, while offering the MIS community the opportunity to strengthen existing theory through an illustration of its application.

Question: What is the unified theory of acceptance and use of technology?


Psychosocial functioning and depression: distinguishing among antecedents, concomitants, and consequences.. In this article we attempt to distinguish empirically between psycho-social variables that are concomitants of depression, and variables that may serve as antecedents or sequelae of this disorder. We review studies that investigated the relationship between depression and any of six psychosocial variables after controlling for the effects of concurrent depression. The six variables examined are attributional style, dysfunctional attitudes, personality, social support, marital distress, and coping style. The review suggests that whereas there is little evidence in adults of a cognitive vulnerability to clinical depression, disturbances in interpersonal functioning may be antecedents or sequelae of this disorder. Specifically, marital distress and low social integration appear to be involved in the etiology of depression, and introversion and interpersonal dependency are identified as enduring abnormalities in the functioning of remitted depressives. We attempt to integrate what is known about the relationships among these latter variables, suggest ways in which they may influence the development of depression, and outline specific issues to be addressed in future research.

Question: What is the difference between antecedents and concomitants?


Work in Progress: K-Nearest Neighbors Techniques for ABAC Policies Clustering. In this paper, we present an approach based on the K-Nearest Neighbors algorithms for policies clustering that aims to reduce the ABAC policies dimensionality for high scale systems. Since ABAC considers a very large set of attributes for access decisions, it turns out that using such model for large scale systems might be very complicated. To date, researchers have proposed to use data mining techniques to discover roles for RBAC system construction. In this work in progress, we consider the usage of KNN-based techniques for the classification of ABAC policies based on similarity computations of rules in order to enhance the ABAC flexibility and to reduce the number of policy rules.

Question: What is the K-Nearest Neighbors algorithm?


The Role of Classification in Knowledge Representation and Discovery. The link between classification and knowledge is explored. Classification schemes have properties that enable the representation of entities and relationships in structures that reflect knowledge of the domain being classified. The strengths and limitations of four classificatory approaches are described in terms of their ability to reflect, discover, and create new knowledge. These approaches are hierarchies, trees, paradigms, and faceted analysis. Examples are provided of the way in which knowledge and the classification process affect each other. INTRODUCTION Developments in our ability to store and retrieve large amounts of' information have stimulated an interest in new ways to exploit this information for advancing human knowledge. This article describes the relationship between knowledge representation (as manifested in classifications) and the processes of knowledge discovery and creation. How does the classification process enable or constrain knowing something or discovering new knowledge about something? In what ways might we develop classifications that will enhance our ability to discover meaningful information in our data stores? The first part of the article describes several representative classificatory structures-hierarchies, trees, paradigms, and faceted analysis with the aim of identifying how these structures serve as knowledge representations and in what ways they can be used for knowledge discovery and creation. The second part of the discussion includes examples from existing classification schemes and discusses how the schemes reflect or fail to reflect knowledge. KNOWLEDGE, THEORY, AND CLASSIFICATION Scholars in many fields, from philosophy to cybernetics, have long discussed the concept of knowledge and the problems of representing knowledge in information systems. The distinction is drawn between merely observing, perceiving, or even describing things and truly knowing

Question: What is the difference between knowledge and information?


Paying for performance: Performance incentives increase desire for the reward object.. The current research examines how exposure to performance incentives affects one's desire for the reward object. We hypothesized that the flexible nature of performance incentives creates an attentional fixation on the reward object (e.g., money), which leads people to become more desirous of the rewards. Results from 5 laboratory experiments and 1 large-scale field study provide support for this prediction. When performance was incentivized with monetary rewards, participants reported being more desirous of money (Study 1), put in more effort to earn additional money in an ensuing task (Study 2), and were less willing to donate money to charity (Study 4). We replicated the result with nonmonetary rewards (Study 5). We also found that performance incentives increased attention to the reward object during the task, which in part explains the observed effects (Study 6). A large-scale field study replicated these findings in a real-world setting (Study 7). One laboratory experiment failed to replicate (Study 3). (PsycINFO Database Record

Question: How does performance incentives affect desire for the reward object?


The Use of Information Retrieval Techniques for Intrusion Detection. Intrusion detection is a broad problem, and we need a greater range of tools than is currently available. In this article, we report a new approach. We have applied information retrieval techniques to index audit trails. These indexes can be extremely e cient at detecting attacks whose signature is an unusual combination of events, and they may consume only a very small additional amount of storage. This approach allows the intrusion detection community to adopt a wide range of techniques developed in applications ranging from library science to web search engines.

Question: What is an intrusion detection system?


A comparative study of texture measures with classification based on featured distributions. -This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented Texture analysis Classification Feature distribution Kullback discriminant Performance evaluation Brodatz textures l. I N T R O D U C T I O N Texture is an important characteristic for the analysis of many types of images. A wide variety of measures for discriminating textures have been proposed, c1'2~ Comparative studies to evaluate the performance of some texture measures have been carried out by Weszka et al., ~3~ Du Bufet al. ~4~ and Ohanian and Dubes, ~s) for

Question: What is a comparative study?


Bridgeless half-bridge AC-DC converter with series-connected two transformers. This paper proposes a bridgeless half-bridge ac-dc converter with series-connected two transformers. The proposed converter integrates the operation of the bridgeless power factor correction (PFC) boost rectifier and the asymmetrical pulse-width modulation half-bridge dc-dc converter. The proposed converter provides an isolated dc output voltage without any full-bridge diode rectifier. Conduction losses are lowered by eliminating the full-bridge diode rectifier. Switching losses are reduced by achieving zero-voltage switching of the power switches. To improve the power efficiency of the bridgeless half-bridge ac-dc converter, this paper presents the study results about the bridgeless half-bridge ac-dc converter with the series-connected two transformers. By using series-connected two transformers, the proposed convert has a simpler power circuit. The proposed converter gives a high efficiency, high power factor, and low cost. The proposed converter achieves a high efficiency of 93.5 % for 250 W (50 V / 5 A) output power at 90 Vrms line voltage.

Question: What is the purpose of the paper?


Trajectory Tracking Control of a Four-Wheel Differentially Driven Mobile Robot. We consider the trajecto ry tracking control problem for a 4-wheel differentially driven mobile robot moving on an outdoor terrain. A dynamic model is presented accounting for the effects of wheel skidding. A model-based nonlinear controller is designed, following the dynamic feedback linearization paradigm. A n operational nonholonomic constraint is added at this stage, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The controller is then robustijied, using conventional linear techniques, against uncertainty in the soil parameters at the ground-wheel contact. Simulation results show the good performance in tracking spline-type trajectories on a virtual terrain with varying characteristics.

Question: How to design a controller for a robot?


Legal Compliance by Design (LCbD) and through Design (LCtD): Preliminary Survey. The purpose of this paper is twofold: (i) carrying out a preliminary survey of the literature and research projects on Compliance by Design (CbD); and (ii) clarifying the double process of (a) extending business managing techniques to other regulatory fields, and (b) converging trends in legal theory, legal technology and Artificial Intelligence. The paper highlights the connections and differences we found across different domains and proposals. We distinguish three different policydriven types of CbD: (i) business, (ii) regulatory, (iii) and legal. The recent deployment of ethical views, and the implementation of general principles of privacy and data protection lead to the conclusion that, in order to appropriately define legal compliance, Compliance through Design (CtD) should be differentiated from CbD.

Question: What is the difference between compliance by design and compliance through design?


Estimating relatedness via data compression. We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.

Question: How to estimate the average error of a hypothesis?


Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline). Employing part-level features offers fine-grained information for pedestrian image description.A prerequisite of part discovery is that each part should be well located. Instead of using external resources like pose estimator, we consider content consistency within each part for precise part location. Specifically, we target at learning discriminative part-informed features for person retrieval and make two contributions. (i) A network named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves competitive results with the state-of-the-art methods, proving itself as a strong convolutional baseline for person retrieval. (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs outliers in each part, which are in fact more similar to other parts. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, surpassing the state of the art by a large margin. Code is available at: https://github.com/syfafterzy/PCB_RPP

Question: What is the part-based convolutional baseline?


Design of a Dual-Band Microstrip Antenna With Enhanced Gain for Energy Harvesting Applications. In this letter, a dual-band circular patch antenna is introduced. The antenna consists of a circular patch with a direct feed through microstrip feedline designed to radiate at 2.45 GHz with fractional bandwidth of 4.5%. A circular slot is inserted into the ground plane that radiates by capacitive coupling between the patch and the ground plane. This slot radiates at 1.95 GHz with fractional impedance bandwidth of 5%. The antenna achieves good radiation characteristics by inserting a reflecting plane at optimum position behind it. The antenna has gain of 8.3 and 7.8 dBi at 1.95 and 2.45 GHz, respectively. This antenna is proposed for the rectenna; then it is designed to direct the main beam in a certain direction by increasing front-to-back (F/B) ratio with low cross-polarization levels by using defected reflector structure in the reflecting plane. The equivalent circuit of the proposed antenna is introduced to model the electrical behavior of the antenna. The proposed antenna can be used to harvest the energy from Wi-Fi and widely spread mobile networks. The proposed antenna was designed using CST Microwave Studio. The simulated and measured results show good agreement.

Question: How to design a dual-band antenna?


Securing SMS Based One Time Password Technique from Man in the Middle Attack. Security of financial transactions in E-Commerce is difficult to implement and there is a risk that user's confidential data over the internet may be accessed by hackers. Unfortunately, interacting with an online service such as a banking web application often requires certain degree of technical sophistication that not all Internet users possess. For the last couple of year such naive users have been increasingly targeted by phishing attacks that are launched by miscreants who are aiming to make an easy profit by means of illegal financial transactions. In this paper, we have proposed an idea for securing e-commerce transaction from phishing attack. An approach already exists where phishing attack is prevented using one time password which is sent on user's registered mobile via SMS for authentication. But this method can be counter attacked by "Man in the Middle". In our paper, a new idea is proposed which is more secure compared to the existing online payment system using OTP. In this mechanism OTP is combined with the secure key and is then passed through RSA algorithm to generate the Transaction password. A Copy of this password is maintained at the server side and is being generated at the user side using a mobile application; so that it is not transferred over the insecure network leading to a fraudulent transaction. Keywords—Phishing, Replay attack, MITM attack, RSA, Random Generator.

Question: How to secure SMS based one time password technique from man in the middle attack?


A 4-µW 0.8-V rail-to-rail input/output CMOS fully differential OpAmp. This paper presents an ultra low power rail-to-rail input/output operational amplifier (OpAmp) designed in a low cost 0.18 µm CMOS technology. In this OpAmp, rail-to-rail input operation is enabled by using complementary input pairs with gm control. To maximize the output swing a rail-to-rail output stage is employed. For low-voltage low-power operation, the operating transistors in the input and output stage are biased in the sub-threshold region. The simulated DC open loop gain is 51 dB, and the slew-rate is 0.04 V/µs with a 10 pF capacitive load connected to each of the amplifier outputs. For the same load, the simulated unity gain frequency is 131 kHz with a 64° phase margin. A common-mode feed-forward circuit (CMFF) increases CMRR, reducing drastically the variations in the output common mode voltage and keeping the DC gain almost constant. In fact, their relative error remains below 1.2 % for a (−20°C, +120°C) temperature span. In addition, the proposed OpAmp is very simple and consumes only 4 µW at 0.8 V supply.

Question: What is the difference between a rail-to-rail OpAmp and a rail-to-rail OpAmp?


Unemployment alters the set point for life satisfaction.. According to set-point theories of subjective well-being, people react to events but then return to baseline levels of happiness and satisfaction over time. We tested this idea by examining reaction and adaptation to unemployment in a 15-year longitudinal study of more than 24,000 individuals living in Germany. In accordance with set-point theories, individuals reacted strongly to unemployment and then shifted back toward their baseline levels of life satisfaction. However, on average, individuals did not completely return to their former levels of satisfaction, even after they became reemployed. Furthermore, contrary to expectations from adaptation theories, people who had experienced unemployment in the past did not react any less negatively to a new bout of unemployment than did people who had not been previously unemployed. These results suggest that although life satisfaction is moderately stable over time, life events can have a strong influence on long-term levels of subjective well-being.

Question: What is the set point for life satisfaction?


Single-layered complex-valued neural network for real-valued classification problems. This paper presents two models of complex-valued neurons (CVNs) for real-valued classification problems, incorporating two newly-proposed activation functions, and presents their abilities as well as differences between them on benchmark problems. In both models, each real-valued input is encoded into a phase between 0 and  of a complex number of unity magnitude, and multiplied by a complex-valued weight. The weighted sum of inputs is fed to an activation function. Activation functions of both models map complex values into real values, and their role is to divide the net-input (weighted sum) space into multiple regions representing the classes of input patterns. The gradient-based learning rule is derived for each of the activation functions. Ability of such CVNs are discussed and tested with two-class problems, such as two and three input Boolean problems, and symmetry detection in binary sequences. We exhibit here that both the models can form proper boundaries for these linear and nonlinear problems. For solving n-class problems, a complex-valued neural network (CVNN) consisting of n CVNs is also considered in this paper. We tested such single-layered CVNNs on several real world benchmark problems. The results show that the classification and generalization abilities of single-layered CVNNs are comparable to the conventional real-valued neural networks (RVNNs) having one hidden layer. Moreover, convergence of CVNNs is much faster than that of RVNNs in most of the cases.

Question: What is a complex-valued neural network?


Transformer Health Condition Monitoring Through GSM Technology. . Transformers are a vital part of the transmission and distribution system. Monitoring transformers for problems before they occur can prevent faults that are costly to repair and result in a loss of service. Current systems can provide information about the state of a transformer, but are either offline or very expensive to implement. Transformers being the essential part of power transmission system are expensive, as is the cost of power interruptions. Because of the cost of scheduled and unscheduled maintenance, especially at remote sites, the utility industry has begun investing in instrumentation and monitoring of transformer. On-line transformer diagnostics using conventional technologies like carrier power line communication, Radio frequency based control system, and Supervisory control and data acquiring systems, Distributed control systems and Internet based communications are having their own limitations. GSM is an open, digital cellular technology used for transmitting mobile voice and data services. This project objective is to develop low cost solution for monitoring health condition of remotely located distribution transformers using GSM technology to preent premature failure of distribution transformers and improving reliability of services to the customers. An Embedded based hardware design is developed to acquire data from electrical sensing system. It consists of a sensing system, signal conditioning electronic circuits, advanced embedded hardware for middle level computing, a powerful computer network for further transmission of data to various places. A powerful GSM networking is designed to send data from a network to other network for proper corrective action at the earliest. Any change in parameters of transmission is sensed to protect the entire transmission and distribution. The performance of prototype model developed is tested at laboratory for monitoring various parameters l

Question: What is the objective of the project?


An automatic tool for the analysis of natural language requirements. Using automatic tools for the quality analysis of Natural Language (NL) requirements is recognized as a key factor for achieving software quality. Unfortunately few tools and techniques for the NL requirements analysis are currently available. This paper presents a methodology and a tool (called QuARS Quality Analyzer for Requirement Specifications) for analyzing NL requirements in a systematic and automatic way. QuARS allows requirements engineers to perform an initial parsing of the requirements in order to automatically detect potential linguistic defects that could cause interpretation problems at subsequent stages in developing the software. This tool is also able to partially support the consistency and completeness analysis by clustering the requirements according to specific topics.

Question: What is a tool for the analysis of natural language requirements?


Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. The naive Bayes classiier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classiication, focusing on the distributional assumptions made about word occurrences in documents.

Question: What is the independence assumption in information retrieval?


Teaching Introductory Artificial Intelligence with Pac-Man. The projects that we have developed for UC Berkeley's introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the PacMan environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students' excitement about video games.

Question: What is the canadian military?


Computational Intelligence in Sports: A Systematic Literature Review. Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

Question: What is a systematic literature review?


Convolutional neural networks and multimodal fusion for text aided image classification. With the exponential growth of web meta-data, exploiting multimodal online sources via standard search engine has become a trend in visual recognition as it effectively alleviates the shortage of training data. However, the web meta-data such as text data is usually not as cooperative as expected due to its unstructured nature. To address this problem, this paper investigates the numerical representation of web text data. We firstly adopt convolutional neural network (CNN) for web text modeling on top of word vectors. Combined with CNN for image, we present a multimodal fusion to maximize the discriminative power of visual and textual modality data for decision level and feature level simultaneously. Experimental results show that the proposed framework achieves significant improvement in large-scale image classification on Pascal VOC-2007 and VOC-2012 datasets.

Question: What is the convolutional neural network?


Compact and Efficient Bipolar Coupler for Wireless Power Chargers: Design and Analysis. Compactness and efficiency are the two basic considerations of the wireless battery chargers for electric vehicles (EVs) and plug-in hybrid EVs. The double-sided LCC compensation topology for wireless power transfer (WPT) has been proved to be one of the efficient solutions lately. However, with the increase of the numbers of compensation components, the volume of the system may become larger, which makes it less attractive. To improve the compactness, a bipolar coupler structure with a compensation-integrated feature is proposed. The inductors of the LCC compensation networks are designed as planar-type and attached to the power-transferring main coils. Extra space and magnetic cores for the compensated inductors outside of the coupler are saved. The cost is that extra couplings between the compensated coils (inductors) and the main coils are induced. To validate the feasibility, the proposed coupler is modeled and investigated by 3-D finite-element analysis tool first. The positioning of the compensated coils, the range of the extra couplings, and the tolerance to misalignment are studied. This is followed by the circuit modeling and characteristic analysis of the proposed WPT topology based on the fundamental harmonic approximation. At last, a 600 mm × 600 mm with a nominal 150-mm-gap wireless charger prototype, operated at a resonant frequency of 95 kHz and a rated power of 5.6 kW has been built and tested. A peak efficiency of 95.36% from a dc power source to the battery load is achieved at rated operation condition.

Question: How to design a compact wireless charger?


Low-cost backdrivable motor control based on feed-forward/feed-back friction compensation. A low-cost but accurate and backdrivable motor control system running on a DC motor and a harmonic drive gear is proposed. It compensates internal friction of the gear and the counter-electromotive torque by combining a model-based feed-forward method and a disturbance observer using a cheap torque sensor. A complementary use of those techniques lowers requirements to their performances i.e. precision, bandwidth, etc, while it is equipped with a flexible property against the external torque. A 2-DOF servo controller is also built upon the system in order to simultaneously achieve smooth responses and robust convergences to the reference.

Question: What is a harmonic drive gear?


Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization. By reducing optimization to a sequence of small subproblems, working set methods achieve fast convergence times for many challenging problems. Despite excellent performance, theoretical understanding of working sets is limited, and implementations often resort to heuristics to determine subproblem size, makeup, and stopping criteria. We propose BLITZ, a fast working set algorithm accompanied by useful guarantees. Making no assumptions on data, our theory relates subproblem size to progress toward convergence. This result motivates methods for optimizing algorithmic parameters and discarding irrelevant variables as iterations progress. Applied to `1-regularized learning, BLITZ convincingly outperforms existing solvers in sequential, limited-memory, and distributed settings. BLITZ is not specific to `1-regularized learning, making the algorithm relevant to many applications involving sparsity or constraints.

Question: What is a principled meta-algorithm for scaling sparse optimization?


A novel planer transition structure from micro-strip line to waveguide. A planer micro-strip to waveguide transition structure is given in this paper. Coupled patch on the bottom of the substrate is excited by the micro-strip feed-line on the top, which realizes the transformation from the TE10 mode in the waveguide to the quasi-TEM mode on the micro-strip line. Modeling and simulation of the structure are conducted for the design by using electromagnetic simulation software HFSS. The measurement of the manufactured structure exhibits an insertion loss less than 0.5dB within 33GHz~37GHz. Compared to traditional waveguide E-plane probe or H-plane probe transition, this design has properties of low profile and easy integration.

Question: What is a micro-strip line?


Development and validation of the childhood narcissism scale.. In this article, we describe the development and validation of a short (10 item) but comprehensive self-report measure of childhood narcissism. The Childhood Narcissism Scale (CNS) is a 1-dimensional measure of stable individual differences in childhood narcissism with strong internal consistency reliability (Studies 1-4). The CNS is virtually unrelated to conventional measures of self-esteem but is positively related to self-appraised superiority, social evaluative concern and self-esteem contingency, agentic interpersonal goals, and emotional extremity (Study 5). Furthermore, the CNS is negatively related to empathic concern and positively related to aggression following ego threat (Study 6). These results suggest that childhood narcissism has similar psychological and interpersonal correlates as adult narcissism. The CNS provides researchers a convenient tool for measuring narcissism in children and young adolescents with strong preliminary psychometric characteristics.

Question: What is the childhood narcissism scale?


Modeling Relation Paths for Representation Learning of Knowledge Bases. Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text. The source code of this paper can be obtained from https://github.com/mrlyk423/ relation_extraction.

Question: How to model relation paths for knowledge base completion?


An autonomous polymerization motor powered by DNA hybridization.. We present a synthetic molecular motor capable of autonomous nanoscale transport in solution. Inspired by bacterial pathogens such as Rickettsia rickettsii, which locomote by inducing the polymerization of the protein actin at their surfaces to form 'comet tails', the motor operates by polymerizing a double-helical DNA tail2. DNA strands are propelled processively at the living end of the growing polymers, demonstrating autonomous locomotion powered by the free energy of DNA hybridization.

Question: How does the motor work?


Robust visual inertial odometry using a direct EKF-based approach. In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).

Question: Robust visual inertial odometry using a direct EKF-based approach. In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the


A Distributed Multi Agents Based Platform for High Performance Computing Infrastructures. This work introduces a novel, modular, layered web based platform for managing machine learning experiments on grid-based High Performance Computing infrastructures. The coupling of the communication services offered by the grid, with an administration layer and conventional web server programming, via a data synchronization utility, leads to the straightforward development of a web-based user interface that allows the monitoring and managing of diverse online distributed computing applications. It also introduces an experiment generation and monitoring tool particularly suitable for investigating machine learning in game playing. The platform is demonstrated with experiments for two different games.

Question: What is a distributed multi agents based platform for high performance computing infrastructures?


Human iris recognition in post-mortem subjects: Study and database. This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death. We used four different iris recognition methods to analyze the dynamics of iris quality decay in short-term comparisons (samples collected up to 60 hours after death) and long-term comparisons (for samples acquired up to 407 hours after demise). This study shows that post-mortem iris recognition is possible and occasionally works even 17 days after death. These conclusions contradict a promulgated rumor that iris is unusable shortly after decease. We make this dataset publicly available to let others verify our findings and to research new aspects of this important and unfamiliar topic. We are not aware of any earlier papers offering post-mortem human iris images and such comprehensive analysis employing four different matchers.

Question: What is the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death?


Game strategies in network security. This paper presents a game-theoretic method for analyzing the security of computer networks. We view the interactions between an attacker and the administrator as a two-player stochastic game and construct a model for the game. Using a nonlinear program, we compute Nash equilibria or best-response strategies for the players (attacker and administrator). We then explain why the strategies are realistic and how administrators can use these results to enhance the security of their network.

Question: What is a nonlinear program?


How to learn a graph from smooth signals. We propose a framework that learns the graph structure underlying a set of smooth signals. Given X ∈ Rm×n whose rows reside on the vertices of an unknown graph, we learn the edge weights w ∈ R + under the smoothness assumption that tr ( X>LX ) is small. We show that the problem is a weighted `-1 minimization that leads to naturally sparse solutions. We point out how known graph learning or construction techniques fall within our framework and propose a new model that performs better than the state of the art in many settings. We present efficient, scalable primal-dual based algorithms for both our model and the previous state of the art, and evaluate their performance on artificial and real data.

Question: How to learn a graph from smooth signals.


Achievable cases in an asynchronous environment. The paper deals with achievability of fault tolerant goals in a completely asynchronous distributed system. Fischer, Lynch, and Paterson [FLP] proved that in such a system "nontrivial agreement" cannot be achieved even in the (possible) presence of a single "benign" fault. In contrast, we exhibit two pairs of goals that are achievable even in the presence of up to t ≪ n/2 faulty processors, contradicting the widely held assumption that no nontrivial goals are attainable in such a system. The first pair deals with renaming processors so as to reduce the size of the initial name space. When only uniqueness is required of the new names, we present a lower bound of n + 1 on the size of the new name space, and a renaming algorithm which establishes an upper bound of n + t. In case the new names are required also to preserve the original order, a tight bound of 2t(n- t + 1) - 1 is obtained. The second pair of goals deals with the multi-slot critical section problem. We present algorithms for controlled access to a critical section. As for the number of slots required, a tight bound of t + 1 is proved in case the slots are identical. In the case of distinct slots the upper bound is 2t + 1.

Question: What is the size of the initial name space?


Online Media Use of False News to Frame the 2016 Trump Presidential Campaign. The 2016 U.S. presidential election campaigns witnessed an unprecedented viral false news -- a type of misinformation referred to as "factitious information blend" that is motivated to discredit political rivals. Despite the different speculations of factors that might have influenced Donald Trump's surprised victory, empirical and theoretical research on the potential impacts of false news propagated by online news media during election campaigns on influencing voters' attitudes and public opinion is seriously lacking. By drawing on the literature on framing political-effects research and by developing our computational text analytics programs, we addressed questions regarding how online news media used false news to negatively frame the Trump presidential campaign. Our text analytics results indicate that although the negative frames against Trump far outnumbered those against Hillary Clinton, weak frames of unverifiable misinformation might have failed to influence the mass audience, leaving them to the power of Trump's direct political communications via Twitter.

Question: How did online media use false news to frame the 2016 Trump presidential campaign?


A Broadband Passive Monopulse Comparator MMIC. A broadband monopulse comparator MMIC (Monolithic Microwave Integrated Circuit) based on GaAs process is presented in this Letter. The comparator network constructed by three magic tees and one lumped power divider is proposed for one sum channel and two delta channels. The measurement results show that very wide frequency band from 15 to 30 GHz (66.7% relatively frequency bandwidth) with less than 2.5-dB loss can be achieved for the sum channel. And the null depth is more than 22 dB in 15–27GHz and 17 dB in 27–30 GHz for two delta channels. The total chip size is 3.4 mm 3.4 mm (<inline-formula> <tex-math notation="LaTeX">$0.26\lambda _{0}~0.26\lambda _{0}$ </tex-math></inline-formula> at the center frequency of 22.5 GHz).

Question: What is a monopulse comparator?


Information Retrieval from Neurophysiological Signals. One of the ultimate goals of neuroscience is decoding someone's intentions directly from his/her brain activities. In this thesis, we aim at pursuing this goal in different scenarios. Firstly, we show the possibility of creating a user-centric music/movie recommender system by employing neurophysiological signals. Regarding this, we employed a brain decoding paradigm in order to classify the features extracted from brain signals of participants watching movie/music video clips, into our target classes (two broad music genres and four broad movie genres). Our results provide a preliminary experimental evidence towards user-centric music/movie content retrieval by exploiting brain signals. Secondly, we addressed one of the main issue of the applications of brain decoding algorithms. Generally, the performance of such algorithms suffers from the constraint of having few and noisy samples, which is the case in most of the neuroimaging datasets. In order to overcome this limitation, we employed an adaptation paradigm in order to transfer knowledge from another domain (e.g. large-scale image domain) to the brain domain. We experimentally show that such adaptation procedure leads to improved results. We performed such adaptation pipeline on different tasks (i.e. object recognition and genre classification) using different neuroimaging modalities (i.e. fMRI, EEG, and MEG). Thirdly, we aimed at one of the fundamental goals in brain decoding which is reconstructing the external stimuli using only the brain features. Under this scenario, we show the possibility of regressing the stimuli spectrogram using time-frequency analysis of the brain signals. Finally, we conclude the thesis by summarizing our contributions and discussing the future directions and applications of our research.

Question: What is the difference between a neurophysiological signal and a neuroimaging signal?


Physics-based hand interaction with virtual objects. We propose an intuitive user interaction system with virtual objects by directly using user's hands through a physics simulation. This system aims to provide a more direct way to manipulate and interact with virtual objects and to induce more realistic reactions of the virtual objects in the interaction than the existing methods. The key contribution of this paper is to model the deformations of the user's hands in two ways: global deformation by the hand poses and the local deformation by contacts with objects. By the two deformation models, objects can be manipulated with better stability and robustness without any predefinition of interaction methods depending on the object shapes, by solely using a physics simulation. The experiment shows that the proposed method enables a user to intuitively manipulate virtual object accurately.

Question: How to model the deformations of the user's hands?


Applying Machine Learning to Text Segmentation for Information Retrieval. We propose a self-supervised word segmentation technique for text segmentation in Chinese information retrieval. This method combines the advantages of traditional dictionary based, character based and mutual information based approaches, while overcoming many of their shortcomings. Experiments on TREC data show this method is promising. Our method is completely language independent and unsupervised, which provides a promising avenue for constructing accurate multi-lingual or cross-lingual information retrieval systems that are flexible and adaptive. We find that although the segmentation accuracy of self-supervised segmentation is not as high as some other segmentation methods, it is enough to give good retrieval performance. It is commonly believed that word segmentation accuracy is monotonically related to retrieval performance in Chinese information retrieval. However, for Chinese, we find that the relationship between segmentation and retrieval performance is in fact nonmonotonic; that is, at around 70% word segmentation accuracy an over-segmentation phenomenon begins to occur which leads to a reduction in information retrieval performance. We demonstrate this effect by presenting an empirical investigation of information retrieval on Chinese TREC data, using a wide variety of word segmentation algorithms with word segmentation accuracies ranging from 44% to 95%, including 70% word segmentation accuracy from our self-supervised word-segmentation approach. It appears that the main reason for the drop in retrieval performance is that correct compounds and collocations are preserved by accurate segmenters, while they are broken up by less accurate (but reasonable) segmenters, to a surprising advantage. This suggests that words themselves might be too broad a notion to conveniently capture the general semantic meaning of Chinese text. Our research suggests machine learning techniques can play

Question: What is the relationship between word segmentation accuracy and retrieval performance in Chinese information retrieval?


A stochastic data discrimination based autoencoder approach for network anomaly detection. Machine learning based network anomaly detection methods, which are already effective defense mechanisms against known network intrusion attacks, have also proven themselves to be more successful on the detection of zero-day attacks compared to other types of detection methods. Therefore, research on network anomaly detection using deep learning is getting more attention constantly. In this study we created an anomaly detection model based on a deterministic autoencoder, which discriminates normal and abnormal data by using our proposed stochastic threshold determination approach. We tested our proposed anomaly detection model on the NSL-KDD's test dataset KDDTest+ and obtained an accuracy of 88.28%. The experimental results show that our proposed anomaly detection model can perform almost the same as the most successful and up-to-date machine learning based anomaly detection models in the literature.

Question: What is the difference between a deterministic autoencoder and a stochastic autoencoder?


Graphical models for text mining: knowledge extraction and performance estimation. This thesis would not have been possible unless the constant support of my advisor Professor Fabio Stella. He gave me suggestions, advices and a constant incentive to learn and deeply understand how things works. Special thanks go to Professor Enrico Fagiuoli for its suggestions and its huge knowledge of probabilistic models. internship has been a precious occasion to learn and discuss and to find new friends. Special thanks go my colleagues at DISCo that shared the highs and lows of my Ph.D. In particular, i thank my laboratory colleague Elena Gatti for our discussion and friendship and all the other Ph.D students for the discussion and fun moments. I thanks all the administrative personnel at the department, without them a Ph.D student life is hell. Thanks to all the student who worked with me during this years, part of the work in this thesis has been developed thanks to their contribution. It is a pleasure to thank those who made my Ph.D and thus my thesis possible: Docflow S.p.A. They gave me the possibility to get a sense of business world, their needs and to propose solutions. A special thanks go to all who have tolerated my obsessions and complaints during this years: my family, my friends and Manoela.

Question: What is the name of the company that gave me the opportunity to get a sense of business world?


Standard high-resolution pelvic MRI vs. low-resolution pelvic MRI in the evaluation of deep infiltrating endometriosis. To compare the capabilities of standard pelvic MRI with low-resolution pelvic MRI using fast breath-hold sequences to evaluate deep infiltrating endometriosis (DIE). Sixty-eight consecutive women with suspected DIE were studied with pelvic MRI. A double-acquisition protocol was carried out in each case. High-resolution (HR)-MRI consisted of axial, sagittal, and coronal TSE T2W images, axial TSE T1W, and axial THRIVE. Low-resolution (LR)-MRI was acquired using fast single shot (SSH) T2 and T1 images. Two radiologists with 10 and 2 years of experience reviewed HR and LR images in two separate sessions. The presence of endometriotic lesions of the uterosacral ligament (USL), rectovaginal septum (RVS), pouch of Douglas (POD), and rectal wall was noted. The accuracies of LR-MRI and HR-MRI were compared with the laparoscopic and histopathological findings. Average acquisition times were 24 minutes for HR-MRI and 7 minutes for LR-MRI. The more experienced radiologist achieved higher accuracy with both HR-MRI and LR-MRI. The values of sensitivity, specificity, PPV, NPV, and accuracy did not significantly change between HR and LR images or interobserver agreement for all of the considered anatomic sites. LR-MRI performs as well as HR-MRI and is a valuable tool for the detection of deep endometriosis extension. • High- and low-resolution MRI perform similarly in deep endometriosis evaluation • Low-resolution MRI significantly reduces the duration of the examination • Radiologist experience is fundamental for evaluating deep pelvic endometriosis

Question: What is the difference between high-resolution and low-resolution pelvic MRI?


Exploring actor-object relationships for query-focused multi-document summarization. Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users' preferences. We note that, different users can generate somewhat different summaries on the basis of the same source data and query. This paper presents our study on how to exploit the information regards how users summarized their texts. Models of different users can be used either separately, or in an ensemble-like fashion. Machine learning methods are explored in the construction of the individual models. However, we explore yet another hypothesis. We believe that the sentences selected into the summary should be coherent and supplement each other in their meaning. One method to model this relationship between sentences is by detecting actor–object relationship (AOR). The sentences that satisfy this relationship have their importance value enhanced. This paper combines ensemble summarizing system and AOR to generate summaries. We have evaluated this method on DUC 2006 and DUC 2007 using ROUGE measure. Experimental results show the supervised method that exploits the ensemble summarizing system combined with AOR outperforms previous models when considering performance in query-based multidocument summarization tasks. Keyword User-based summarization · Actor–object relationship · Multi-document summarization · Ensemble summarizing system · Training data construction Communicated by V. Loia. M. Valizadeh (B) · P. Brazdil LIAAD INESC Tec, University of Porto, Porto, Portugal e-mail: valizadehmr@gmail.com P. Brazdil FEP, University of Porto, Porto, Portugal e-mail: pbrazdil@inescporto.pt

Question: How to exploit the information regards how users summarized their texts?


Fourier Lucas-Kanade Algorithm. In this paper, we propose a framework for both gradient descent image and object alignment in the Fourier domain. Our method centers upon the classical Lucas & Kanade (LK) algorithm where we represent the source and template/model in the complex 2D Fourier domain rather than in the spatial 2D domain. We refer to our approach as the Fourier LK (FLK) algorithm. The FLK formulation is advantageous when one preprocesses the source image and template/model with a bank of filters (e.g., oriented edges, Gabor, etc.) as 1) it can handle substantial illumination variations, 2) the inefficient preprocessing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, 3) unlike traditional LK, the computational cost is invariant to the number of filters and as a result is far more efficient, and 4) this approach can be extended to the Inverse Compositional (IC) form of the LK algorithm where nearly all steps (including Fourier transform and filter bank preprocessing) can be precomputed, leading to an extremely efficient and robust approach to gradient descent image matching. Further, these computational savings translate to nonrigid object alignment tasks that are considered extensions of the LK algorithm, such as those found in Active Appearance Models (AAMs).

Question: What is the Fourier LK algorithm?


Differentially Private Histogram Publication for Dynamic Datasets: an Adaptive Sampling Approach. Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on "one-time" release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods.

Question: How to release a series of dynamic datasets in real time with differential privacy?


Effects of weightlifting vs. kettlebell training on vertical jump, strength, and body composition.. Effects of weightlifting vs. kettlebell training on vertical jump, strength, and body composition. J Strength Cond Res 26(5): 1199-1202, 2012-The present study compared the effects of 6 weeks of weightlifting plus traditional heavy resistance training exercises vs. kettlebell training on strength, power, and anthropometric measures. Thirty healthy men were randomly assigned to 1 of 2 groups: (a) weightlifting (n = 13; mean ± SD: age, 22.92 ± 1.98 years; body mass, 80.57 ± 12.99 kg; height, 174.56 ± 5.80 cm) or (b) kettlebell (n = 17; mean ± SD: age, 22.76 ± 1.86 years; body mass, 78.99 ± 10.68 kg; height, 176.79 ± 5.08 cm) and trained 2 times a week for 6 weeks. A linear periodization model was used for training; at weeks 1-3 volume was 3 × 6 (kettlebell swings or high pull), 4 × 4 (accelerated swings or power clean), and 4 × 6 (goblet squats or back squats), respectively, and the volume increased during weeks 4-6 to 4 × 6, 6 × 4, and 4 × 6, respectively. Participants were assessed for height (in centimeters), body mass (in kilograms), and body composition (skinfolds). Strength was assessed by the back squat 1 repetition maximum (1RM), whereas power was assessed by the vertical jump and power clean 1RM. The results of this study indicated that short-term weightlifting and kettlebell training were effective in increasing strength and power. However, the gain in strength using weightlifting movements was greater than that during kettlebell training. Neither method of training led to significant changes in any of the anthropometric measures. In conclusion, 6 weeks of weightlifting induced significantly greater improvements in strength compared with kettlebell training. No between-group differences existed for the vertical jump or body composition.

Question: What is the difference between weightlifting and kettlebell training?


Monitoring and depth of strategy use in computer-based learning environments for science and history.. BACKGROUND Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance.   AIMS In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history.   SAMPLE This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area.   METHODS We collected think-aloud protocol SRL and knowledge measure data and conducted both structural equation modelling and path analysis to investigate our research questions.   RESULTS Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations.   CONCLUSIONS These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.

Question: What is the difference between deep and surface strategies?


Following My Head and My Heart: Integrating Preschoolers' Empathy, Theory of Mind, and Moral Judgments.. Associations among hypothetical, prototypic moral, and conventional judgments; theory of mind (ToM); empathy; and personal distress were examined in 108 socioeconomically diverse preschoolers (Mage  = 42.94 months, SD = 1.42). Repeated measures analysis of covariance with empathy, false beliefs, and their interaction as covariates indicated that empathy was significantly associated with judgments of greater moral but not conventional transgression severity, particularly for psychological harm, and with deserved punishment for unfairness. False beliefs were associated with (combined) moral criterion judgments of rule and authority independence and inalterability. Empathy also was positively associated with criterion judgments but only for children low in ToM. Personal distress was unrelated to judgments. Results demonstrate the importance of both affective and cognitive processes in preschoolers' moral judgments.

Question: What is the difference between moral and conventional judgments?


The draft genome of the transgenic tropical fruit tree papaya (Carica papaya Linnaeus). Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3× draft genome sequence of 'SunUp' papaya, the first commercial virus-resistant transgenic fruit tree to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far, may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica's distinguishing morpho-physiological, medicinal and nutritional properties.

Question: What is the draft genome of the transgenic tropical fruit tree papaya?


A model to support sign language content development for digital television. This paper proposes a model to support sign language content development and deployment in digital television scenarios. The model objective is to overcome traditional video limitations on the production of signed content and improve accessibility on digital television. Previous work on sign and gesture recognition, sign notation systems and avatar animation are reviewed. Sign communication requirements are investigated and adequate algorithms are identified to enable sign language processing and transmission. An application interface is proposed in order to access the model functionalities. The final discussion lists the benefits and limitations of the proposed model.

Question: What is a model to support sign language content development for digital television?


Design of impedance matching circuits for RF energy harvesting systems. This paper presents a systematic design methodology for impedance matching circuits of an RF energy harvester to maximize the harvested energy for a range of input power levels with known distribution. Design of input matching networks for RF rectifier differs from those for traditional RF circuits such as low-noise amplifier because the transistors in the RF rectifiers operate in different regions. In this work, it is shown that the input impedance of the rectifier estimated based on small signal simulation of transistors at the peak of input signal in the rectifier when the transistors are biased with stable DC operating voltage provides the largest harvested energy from a fixed input power. For variable input power levels, a matching network selection strategy is proposed to maximize expected harvested energy given the probability density distribution of input power levels. As an experimental sample, an RF energy harvester circuit is designed to maximize the harvested energy in the 902–928 MHz band employing an off-chip impedance matching circuit designed using the proposed methodology. The proposed RF energy harvester exhibits a measured maximum power conversion efficiency (PCE) of 32% at −15 dBm (32 μW) and delivers an output DC voltage of 3.2 V to a 1 MΩ load.

Question: How to design an impedance matching circuit for RF energy harvesting systems?


Implementation of behavior change techniques in mobile applications for physical activity.. BACKGROUND Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well.   PURPOSE To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps.   METHODS Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps.   RESULTS Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others' approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education.   CONCLUSIONS Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.

Question: What is the evidence base for the behavior change techniques used in physical activity apps?


Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces. We present a novel method for 3D surface reconstruction from an input cloud of 3D points augmented with visibility information. We observe that it is possible to reconstruct surfaces that do not contain input points. Instead of modeling the surface from input points, we model free space from visibility information of the input points. The complement of the modeled free space is considered full space. The surface occurs at interface between the free and the full space. We show that under certain conditions a part of the full space surrounded by the free space must contain a real object also when the real object does not contain any input points; that is, an occluder reveals itself through occlusion. Our key contribution is the proposal of a new interface classifier that can also detect the occluder interface just from the visibility of input points. We use the interface classifier to modify the state-of-the-art surface reconstruction method so that it gains the ability to reconstruct weakly supported surfaces. We evaluate proposed method on datasets augmented with different levels of noise, undersampling, and amount of outliers. We show that the proposed method outperforms other methods in accuracy and ability to reconstruct weakly supported surfaces.

Question: How to reconstruct a surface from visibility information?


Unsupervised Text Normalization Using Distributed Representations of Words and Phrases. Text normalization techniques that use rule-based normalization or string similarity based on static dictionaries are typically unable to capture domain-specific abbreviations (custy, cx → customer) and shorthands (5ever, 7ever → forever) used in informal texts. In this work, we exploit the property that noisy and canonical forms of a particular word share similar context in a large noisy text collection (millions or billions of social media feeds from Twitter, Facebook, etc.). We learn distributed representations of words to capture the notion of contextual similarity and subsequently learn normalization lexicons from these representations in a completely unsupervised manner. We experiment with linear and non-linear distributed representations obtained from log-linear models and neural networks, respectively. We apply our framework for normalizing customer care notes and Twitter. We also extend our approach to learn phrase normalization lexicons (g2g → got to go) by training distributed representations over compound words. Our approach outperforms Microsoft Word, Aspell and a manually compiled urban dictionary from the Web and achieves state-of-the-art results on a publicly available Twitter dataset.

Question: How to normalize customer care notes?


Bispectrum estimation: A digital signal processing framework. It is the purpose of this tutorial paper to place bispectrum estimation in a digital signal processing framework in order to aid engineers in grasping the utility of the available bispectrum estimation techniques, to discuss application problems that can directly benefit from the use of the bispectrum, and to motivate research in this area. Three general reasons are behind the use of bispectrum in signal processing and are addressed in the paper: to extract information due to deviations from normality, to estimate the phase of parametric signals, and to detect and characterize the properties of nonlinear mechanisms that generate time series.

Question: What is the purpose of this tutorial paper?


Current advances in the fabrication of microneedles for transdermal delivery.. The transdermal route is an excellent site for drug delivery due to the avoidance of gastric degradation and hepatic metabolism, in addition to easy accessibility. Although offering numerous attractive advantages, many available transdermal systems are not able to deliver drugs and other compounds as desired. The use of hypodermic needles, associated with phobia, pain and accidental needle-sticks has been used to overcome the delivery limitation of macromolecular compounds. The means to overcome the disadvantages of hypodermic needles has led to the development of microneedles for transdermal delivery. However, since the initial stages of microneedle fabrication, recent research has been conducted integrating various fabrication techniques for generating sophisticated microneedle devices for transdermal delivery including progress on their commercialization. A concerted effort has been made within this review to highlight the current advances of microneedles, and to provide an update of pharmaceutical research in the field of microneedle-assisted transdermal drug delivery systems.

Question: What is the current status of microneedle-assisted transdermal drug delivery systems?


Terrain-adaptive locomotion skills using deep reinforcement learning. Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.

Question: What is the canadian military?