PATTERNS OF INFORMATION SEEKING ON THE WEB: A QUALITATIVE STUDY OF DOMAIN EXPERTISE AND WEB EXPERTISE. This research examines the pattern of Web information seeking in four groups of nurses with different combinations of domain expertise and Web expertise. Protocols were gathered as the nurses carried out information-seeking tasks in the domain of osteoporosis. Domain and Web novices searched breadth-first and did little or no evaluation of the results. Domain expert/Web novices also searched breadth-first but evaluated information more thoroughly using osteoporosis knowledge. Domain novice/Web experts searched in a mixed, breadth-first/depth-first pattern and attempted to evaluate information using general criteria. Domain expert/Web experts carried out depth-first searches, following deep trails of information and evaluated information based on the most varied and sophisticated criteria. The results suggest that there are distinct differences in searching patterns related to expertise. Implications of these findings and suggestions for future research are provided. _______________________ Christine A. Jenkins is a Solution Architect with Sprint Corporation. christine_jenkin@hotmail.com. Cynthia L. Corritore is a professor at the College of Business Administration, Creighton University, Omaha, NE 68178 USA. cindy@creighton.edu. Susan Wiedenbeck is a professor at the College of Information Science and Technology, Drexel University, Philadelphia, PA 19104 USA. susan.wiedenbeck@cis.drexel.edu.

Question: What is the difference between a domain expert and a web expert?


Digital Libraries and Autonomous Citation Indexing. The Web is revolutionizing the way researchers access scientific literature, however scientific literature on the Web is largely disorganized. Autonomous citation indexing can help organize the literature by automating the construction of citation indices. Autonomous citation indexing aims to improve the dissemination and retrieval of scientific literature, and provides improvements in cost, availability, comprehensiveness, efficiency, and timeliness.

Question: What is the difference between autonomous citation indexing and citation indexing?


High-dimensional signature compression for large-scale image classification. We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 105 dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases — ImageNet and a dataset of lM Flickr images — showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.

Question: How to compress high-dimensional signatures?


Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called "semantic image segmentation"). We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation. This is due to the very invariance properties that make DCNNs good for high level tasks. We overcome this poor localization property of deep networks by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Qualitatively, our "DeepLab" system is able to localize segment boundaries at a level of accuracy which is beyond previous methods. Quantitatively, our method sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 71.6% IOU accuracy in the test set. We show how these results can be obtained efficiently: Careful network re-purposing and a novel application of the 'hole' algorithm from the wavelet community allow dense computation of neural net responses at 8 frames per second on a modern GPU.

Question: What is the state of the art in semantic image segmentation?


The game, the player, the world: looking for a heart of gameness. This paper proposes a definition of games. I describe the classic game model, a list of six features that are necessary and sufficient for something to be a game. The definition shows games to be transmedial: There is no single game medium, but rather a number of game media, each with its own strengths. The computer is simply the latest game medium to emerge. While computer games 1 are therefore part of the broader area of games, they have in many cases evolved beyond the classic game model.

Question: What is a game?


Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems. In this article, three approaches are proposed for measuring difficulty that can be useful in developing Dynamic Difficulty Adjustment (DDA) systems in different game genres. Our analysis of the existing DDA systems shows that there are three ways to measure the difficulty of the game: using the formal model of gameplay, using the features of the game, and direct examination of the player. These approaches are described in this article and supplemented by appropriate examples of DDA implementations. In addition, the article describes the distinction between task complexity and task difficulty in DDA systems. Separating task complexity (especially the structural one) is suggested, which is an objective characteristic of the task, and task difficulty, which is related to the interaction between the task and the task performer.

Question: What is the difference between task complexity and task difficulty?


An Energy-Efficient Architecture for Binary Weight Convolutional Neural Networks. Binary weight convolutional neural networks (BCNNs) can achieve near state-of-the-art classification accuracy and have far less computation complexity compared with traditional CNNs using high-precision weights. Due to their binary weights, BCNNs are well suited for vision-based Internet-of-Things systems being sensitive to power consumption. BCNNs make it possible to achieve very high throughput with moderate power dissipation. In this paper, an energy-efficient architecture for BCNNs is proposed. It fully exploits the binary weights and other hardware-friendly characteristics of BCNNs. A judicious processing schedule is proposed so that off-chip I/O access is minimized and activations are maximally reused. To significantly reduce the critical path delay, we introduce optimized compressor trees and approximate binary multipliers with two novel compensation schemes. The latter is able to save significant hardware resource, and almost no computation accuracy is compromised. Taking advantage of error resiliency of BCNNs, an innovative approximate adder is developed, which significantly reduces the silicon area and data path delay. Thorough error analysis and extensive experimental results on several data sets show that the approximate adders in the data path cause negligible accuracy loss. Moreover, algorithmic transformations for certain layers of BCNNs and a memory-efficient quantization scheme are incorporated to further reduce the energy cost and on-chip storage requirement. Finally, the proposed BCNN hardware architecture is implemented with the SMIC 130-nm technology. The postlayout results demonstrate that our design can achieve an energy efficiency over 2.0TOp/s/W when scaled to 65 nm, which is more than two times better than the prior art.

Question: What is the energy efficiency of a binary weight convolutional neural network?


CAWA: Coordinated warp scheduling and Cache Prioritization for critical warp acceleration of GPGPU workloads. The ubiquity of graphics processing unit (GPU) architectures has made them efficient alternatives to chip-multiprocessors for parallel workloads. GPUs achieve superior performance by making use of massive multi-threading and fast context-switching to hide pipeline stalls and memory access latency. However, recent characterization results have shown that general purpose GPU (GPGPU) applications commonly encounter long stall latencies that cannot be easily hidden with the large number of concurrent threads/warps. This results in varying execution time disparity between different parallel warps, hurting the overall performance of GPUs -- the warp criticality problem.  To tackle the warp criticality problem, we propose a coordinated solution, criticality-aware warp acceleration (CAWA), that efficiently manages compute and memory resources to accelerate the critical warp execution. Specifically, we design (1) an instruction-based and stall-based criticality predictor to identify the critical warp in a thread-block, (2) a criticality-aware warp scheduler that preferentially allocates more time resources to the critical warp, and (3) a criticality-aware cache reuse predictor that assists critical warp acceleration by retaining latency-critical and useful cache blocks in the L1 data cache. CAWA targets to remove the significant execution time disparity in order to improve resource utilization for GPGPU workloads. Our evaluation results show that, under the proposed coordinated scheduler and cache prioritization management scheme, the performance of the GPGPU workloads can be improved by 23% while other state-of-the-art schedulers, GTO and 2-level schedulers, improve performance by 16% and -2% respectively.

Question: What is the warp criticality problem?


Planar object recognition using projective shape representation. We describe a model based recognition system, called LEWIS, for the identification of planar objects based on a projectively invariant representation of shape. The advantages of this shape description include simple model acquisition (direct from images), no need for camera calibration or object pose computation, and the use of index functions. We describe the feature construction and recognition algorithms in detail and provide an analysis of the combinatorial advantages of using index functions. Index functions are used to select models from a model base and are constructed from projective invariants based on algebraic curves and a canonical projective coordinate frame. Examples are given of object recognition from images of real scenes, with extensive object libraries. Successful recognition is demonstrated despite partial occlusion by unmodelled objects, and realistic lighting conditions.

Question: What is a projectively invariant representation of shape?


Towards Secure Provenance in the Cloud: A Survey. Provenance information are meta-data that summarize the history of the creation and the actions performed on an artefact e.g. data, process etc. Secure provenance is essential to improve data forensics, ensure accountability and increase the trust in the cloud. In this paper, we survey the existing cloud provenance management schemes and proposed security solutions. We investigate the current related security challenges resulting from the nature of the provenance model and the characteristics of the cloud and we finally identify potential research directions which we feel necessary t should be covered in order to build a secure cloud provenance for the next generation.

Question: What is the cloud provenance?


Learning with Noisy Labels. In this paper, we theoretically study the problem of binary classification in the presence of random classification noise — the learner, instead of seeing the true labels, sees labels that have independently been flipped with some small probability. Moreover, random label noise is class-conditional — the flip probability depends on the class. We provide two approaches to suitably modify any given surrogate loss function. First, we provide a simple unbiased estimator of any loss, and obtain performance bounds for empirical risk minimization in the presence of iid data with noisy labels. If the loss function satisfies a simple symmetry condition, we show that the method leads to an efficient algorithm for empirical minimization. Second, by leveraging a reduction of risk minimization under noisy labels to classification with weighted 0-1 loss, we suggest the use of a simple weighted surrogate loss, for which we are able to obtain strong empirical risk bounds. This approach has a very remarkable consequence — methods used in practice such as biased SVM and weighted logistic regression are provably noise-tolerant. On a synthetic non-separable dataset, our methods achieve over 88% accuracy even when 40% of the labels are corrupted, and are competitive with respect to recently proposed methods for dealing with label noise in several benchmark datasets.

Question: What is the difference between the two approaches?


Performance of Reliable Transport Protocol over IEEE 802.11 Wireless LAN: Analysis and Enhancement. IEEE 802.11 Medium Access Control(MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is Distributed Coordination Function(DCF), which is a Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA) with binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Comparing with other models, this model is shown to be able to predict the behaviors of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.

Question: What is the difference between DCF and DCF+?


High Quality Uniform Random Number Generation Using LUT Optimised State-transition Matrices. This paper presents a family of uniform random number generators designed for efficient implementation in Lookup table (LUT) based FPGA architectures. A generator with a period of 2j1 can be implemented using k flip-flops and k LUTs, and provides k random output bits each cycle. Each generator is based on a binary linear recurrence, with a state-transition matrix designed to make best use of all available LUT inputs in a given FPGA architecture, and to ensure that the critical path between all registers is a single LUT. This class of generator provides a higher sample rate per area than LFSR and Combined Tausworthe generators, and operates at similar or higher clock-rates. The statistical quality of the generators increases with k, and can be used to pass all common empirical tests such as Diehard, Crush and the NIST cryptographic test suite. Theoretical properties such as global equidistribution can also be calculated, and best and average case statistics shown. Due to the large number of random bits generated per cycle these generators can be used as a basis for generators with even higher statistical quality, and an example involving combination through addition is demonstrated.

Question: What is a LUT?


Seeing Stars of Valence and Arousal in Blog Posts. Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two dimensions, based on the psychological model of Russell's circumplex model of affect and label a previously available corpus with multidimensional, real-valued annotations. Experimental results using regression and one-versus-all approaches of support vector machine classifiers show that although the latter approach provides better exact ordinal class prediction accuracy, regression techniques tend to make smaller scale errors.

Question: How to map discrete affective states into ordinal scales?


Nonoutsourceable Scratch-Off Puzzles to Discourage Bitcoin Mining Coalitions. An implicit goal of Bitcoin's reward structure is to diffuse network influence over a diverse, decentralized population of individual participants. Indeed, Bitcoin's security claims rely on no single entity wielding a sufficiently large portion of the network's overall computational power. Unfortunately, rather than participating independently, most Bitcoin miners join coalitions called mining pools in which a central pool administrator largely directs the pool's activity, leading to a consolidation of power. Recently, the largest mining pool has accounted for more than half of network's total mining capacity. Relatedly, "hosted mining" service providers offer their clients the benefit of economies-of-scale, tempting them away from independent participation. We argue that the prevalence of mining coalitions is due to a limitation of the Bitcoin proof-of-work puzzle -- specifically, that it affords an effective mechanism for enforcing cooperation in a coalition. We present several definitions and constructions for "nonoutsourceable" puzzles that thwart such enforcement mechanisms, thereby deterring coalitions. We also provide an implementation and benchmark results for our schemes to show they are practical.

Question: What is the largest mining pool?


Data-Driven Feature Characterization Techniques for Laser Printer Attribution. Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.

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


Human facial expressions as adaptations: Evolutionary questions in facial expression research.. The importance of the face in social interaction and social intelligence is widely recognized in anthropology. Yet the adaptive functions of human facial expression remain largely unknown. An evolutionary model of human facial expression as behavioral adaptation can be constructed, given the current knowledge of the phenotypic variation, ecological contexts, and fitness consequences of facial behavior. Studies of facial expression are available, but results are not typically framed in an evolutionary perspective. This review identifies the relevant physical phenomena of facial expression and integrates the study of this behavior with the anthropological study of communication and sociality in general. Anthropological issues with relevance to the evolutionary study of facial expression include: facial expressions as coordinated, stereotyped behavioral phenotypes, the unique contexts and functions of different facial expressions, the relationship of facial expression to speech, the value of facial expressions as signals, and the relationship of facial expression to social intelligence in humans and in nonhuman primates. Human smiling is used as an example of adaptation, and testable hypotheses concerning the human smile, as well as other expressions, are proposed.

Question: What is the relationship between facial expression and social intelligence?


Re-inventing PTPTN study loan with blockchain and smart contracts. The issue of default payments from borrowers of the National Higher Education Fund Corporation (PTPTN) is worrisome. Many borrowers fail to pay their loans and claims that the PTPTN has poor management and filing system. This study proposed a prototype for managing study loan repayment utilizing blockchain and smart contracts. Borrowers have full access toward their accounts and ledgers while corporation filing and management system get automatically up-to-date with the assistance of smart contracts.

Question: What is a smart contract?


Approximation Algorithms. There are many variations of local search: Hill-climbing: The name for the basic method described above. Metropolis: Pick a random y ∈ N(x). If f(y) < f(x), set x = y. If f(y) > f(x), set x = y with some probability. Simulated annealing: Like Metropolis, but where the probability of moving to a higher-cost neighbor decreases over time. Tabu search: Like the previous two, but with memory in order to avoid getting stuck in suboptimal regions or in plateaus ("taboo" areas). Parallel search: Do more than one search, and occasionally replace versions that are doing poorly with copies of versions that are doing well. Genetic algorithm: Keep a population of searches that changes over time via "breeding" of the best-performing searches.

Question: What is the difference between hill-climbing, simulated annealing, tabu search, and genetic algorithms?


Generalized State Coherence Transform for multidimensional localization of multiple sources. In our recent work an effective method for multiple source localization has been proposed under the name of cumulative State Coherence Transform (cSCT). Exploiting the physical meaning of the frequency-domain blind source separation and the sparse time-frequency dominance of the acoustic sources, multiple reliable TDOAs can be estimated with only two microphones, regardless of the permutation problem and of the microphone spacing. In this paper we present a multidimensional generalization of the cSCT which allows one to localize several sources in the P-dimensional space. An important approximation is made in order to perform a disjoint TDOA estimation over each dimension which reduces the localization problem to linear complexity. Furthermore the approach is invariant to the array geometry and to the assumed acoustic propagation model. Experimental results on simulated data show a precise 2-D localization of 7 sources by only using an array of three elements.

Question: How to localize multiple sources in the P-dimensional space?


SNAP and SPAN: Towards Dynamic Spatial Ontology. We propose a modular ontology of the dynamic features of reality. This amounts, on the one hand, to a purely spatial ontology supporting snapshot views of the world at successive instants of time and, on the other hand, to a purely spatiotemporal ontology of change and process. We argue that dynamic spatial ontology must combine these two distinct types of inventory of the entities and relationships in reality, and we provide characterizations of spatiotemporal reasoning in the light of the interconnections between them.

Question: What is the dynamic spatial ontology?


Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]. The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing field

Question: What is a grand challenge of robotics?


CPW to waveguide transition with tapered slotline probe. A new CPW to waveguide transition is developed based on the concept of tapered slot antenna and E-plane probe coupling. The transition consists of a tapered slotline probe and a slotline to CPW matching section. The current design has the advantages of broad bandwidth, compact size, low fabrication cost, and high reliability. The characteristics of a prototype transition are investigated by numerical simulation on Duroid substrate and WR-90 waveguide. The back-to-back combination is measured to verify the agreement with the simulated results and realization of this design.

Question: What is a slotline?


Probabilistic Horn Abduction and Bayesian Networks. This paper presents a simple framework for Horn clause abduc tion with probabilities associated with hypotheses The framework incorporates assumptions about the rule base and independence as sumptions amongst hypotheses It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework The main contribution is in nding a relationship between logical and probabilistic notions of evidential reasoning This provides a useful representation language in its own right providing a compromise between heuristic and epistemic ad equacy It also shows how Bayesian networks can be extended be yond a propositional language This paper also shows how a language with only unconditionally independent hypotheses can represent any probabilistic knowledge and argues that it is better to invent new hy potheses to explain dependence rather than having to worry about dependence in the language Scholar Canadian Institute for Advanced Research Probabilistic Horn abduction and Bayesian networks

Question: What is the Canadian Institute for Advanced Research?


Agricultural sustainability: concepts, principles and evidence.. Concerns about sustainability in agricultural systems centre on the need to develop technologies and practices that do not have adverse effects on environmental goods and services, are accessible to and effective for farmers, and lead to improvements in food productivity. Despite great progress in agricultural productivity in the past half-century, with crop and livestock productivity strongly driven by increased use of fertilizers, irrigation water, agricultural machinery, pesticides and land, it would be over-optimistic to assume that these relationships will remain linear in the future. New approaches are needed that will integrate biological and ecological processes into food production, minimize the use of those non-renewable inputs that cause harm to the environment or to the health of farmers and consumers, make productive use of the knowledge and skills of farmers, so substituting human capital for costly external inputs, and make productive use of people's collective capacities to work together to solve common agricultural and natural resource problems, such as for pest, watershed, irrigation, forest and credit management. These principles help to build important capital assets for agricultural systems: natural; social; human; physical; and financial capital. Improving natural capital is a central aim, and dividends can come from making the best use of the genotypes of crops and animals and the ecological conditions under which they are grown or raised. Agricultural sustainability suggests a focus on both genotype improvements through the full range of modern biological approaches and improved understanding of the benefits of ecological and agronomic management, manipulation and redesign. The ecological management of agroecosystems that addresses energy flows, nutrient cycling, population-regulating mechanisms and system resilience can lead to the redesign of agriculture at a landscape scale.

Question: What is the definition of agricultural sustainability?


Question: What is the difference between grid connected and grid connected PV systems?


Future Shipboard MVdc System Protection Requirements and Solid-State Protective Device Topological Tradeoffs. The search for the optimum architecture for shipboard medium voltage dc integrated power systems must take into account the short-circuit protection in addition to overarching goals of efficiency, survivability, reliability of power, and cost effectiveness. Presently, accepted approaches to protection are "unit-based," which means the power converter(s) feeding the bus coordinate with no-load electromechanical switches to isolate faulted portions of the bus. However, "breaker-based" approaches, which rely upon solid-state circuit breakers for fault mitigation, can result in higher reliability of power and potentially higher survivability. The inherent speed of operation of solid-state protective devices will also play a role in fault isolation, hence reducing stress level on all system components. A comparison study is performed of protective device topologies that are suitable for shipboard distribution systems rated between 4 and 30 kVdc from the perspectives of size and number of passive components required to manage the commutation energy during sudden fault events and packaging scalability to higher current and voltage systems. The implementation assumes a multichip silicon carbide (SiC) 10-kV, 240-A MOSFET/junction barrier Schottkey (JBS) diode module.

Question: What is the difference between a breaker and a solid-state protective device?


A Requirements-to-Implementation Mapping Tool for Requirements Traceability. Software quality is a major concern in software engineering, particularly when we are dealing with software services that must be available 24 hours a day, please the customer and be kept permanently. In software engineering, requirements management is one of the most important tasks that can influence the success of a software project. Maintain traceability information is a fundamental aspect in software engineering and can facilitate the process of software development and maintenance. This information can be used to support various activities such as analyzing the impact of changes and software verification and validation. However, traceability techniques are mainly used between requirements and software test cases. This paper presents a prototype of a tool that supports the mapping of functional requirements with the pages and HTML elements of a web site. This tool helps maintaining requirements traceability information updated and, ultimately, increasing the efficiency of requirements change management, which may contribute to the overall quality of the software service.

Question: What is a requirements-to-implementation mapping tool?


The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS. Nowadays, the performance of processors is primarily bound by a fixed energy budget, the power wall. This forces hardware vendors to optimize processors for specific tasks, which leads to an increasingly heterogeneous hardware landscape. Although efficient algorithms for modern processors such as GPUs are heavily investigated, we also need to prepare the database optimizer to handle computations on heterogeneous processors. GPUs are an interesting base for case studies, because they already offer many difficulties we will face tomorrow. In this paper, we present CoGaDB, a main-memory DBMS with built-in GPU acceleration, which is optimized for OLAP workloads. CoGaDB uses the self-tuning optimizer framework HyPE to build a hardware-oblivious optimizer, which learns cost models for database operators and efficiently distributes a workload on available processors. Furthermore, CoGaDB implements efficient algorithms on CPU and GPU and efficiently supports star joins. We show in this paper, how these novel techniques interact with each other in a single system. Our evaluation shows that CoGaDB quickly adapts to the underlying hardware by increasing the accuracy of its cost models at runtime.

Question: What is the design and implementation of CoGaDB?


An architectural pattern language of cloud-based applications. The properties of clouds -- elasticity, pay-per-use, and standardization of the runtime infrastructure -- enable cloud providers and users alike to benefit from economies of scale, faster provisioning times, and reduced runtime costs. However, to achieve these benefits, application architects and developers have to respect the characteristics of the cloud environment.  To reduce the complexity of cloud application architectures, we propose a pattern-based approach for cloud application design and development. We defined a pattern format to describe the principles of cloud computing, available cloud offerings, and cloud application architectures. Based on this format we developed an architectural pattern language of cloud-based applications: through interrelation of patterns for cloud offering descriptions and cloud application architectures, developers are guided during the identification of cloud environments and architecture patterns applicable to their problems. We cover the proceeding how we identified patterns in various information sources and existing productively used applications, give an overview of previously discovered patterns, and introduce one new pattern. Further, we propose a framework for the organizations of patterns and the guidance of developers during pattern instantiation.

Question: What is a pattern language?


Incremental Dialogue System Faster than and Preferred to its Nonincremental Counterpart. Current dialogue systems generally operate in a pipelined, modular fashion on one complete utterance at a time. Evidence from human language understanding shows that human understanding operates incrementally and makes use of multiple sources of information during the parsing process, including traditionally "later" components such as pragmatics. In this paper we describe a spoken dialogue system that understands language incrementally, provides visual feedback on possible referents during the course of the user's utterance, and allows for overlapping speech and actions. We further present findings from an empirical study showing that the resulting dialogue system is faster overall than its nonincremental counterpart. Furthermore, the incremental system is preferred to its nonincremental counterpart – beyond what is accounted for by factors such as speed and accuracy. These results indicate that successful incremental understanding systems will improve both performance and usability.

Question: What is the incremental dialogue system?


Omnidirectional conformal antenna array based on E-shaped patches. An antenna consisting of 4 element subarrays of strongly coupled E-shaped patches placed conformally on a metal cylinder is proposed for use in wireless local area networks (WLAN). The use of this special array configuration in the cylindrical case allows reaching a 8.3 % bandwidth and an omnidirectional radiation pattern in the horizontal plane with only 2 subarrays. CST Microwave Studio is used for validation purposes before manufacturing. To the knowledge of the authors, it is the first time that this recently introduced concept of strongly coupled E-shaped patches is used in a conformal antenna.

Question: How does the antenna work?


FraudFind: Financial fraud detection by analyzing human behavior. Financial fraud is commonly represented by the use of illegal practices where they can intervene from senior managers until payroll employees, becoming a crime punishable by law. There are many techniques developed to analyze, detect and prevent this behavior, being the most important the fraud triangle theory associated with the classic financial audit model. In order to perform this research, a survey of the related works in the existing literature was carried out, with the purpose of establishing our own framework. In this context, this paper presents FraudFind, a conceptual framework that allows to identify and outline a group of people inside an banking organization who commit fraud, supported by the fraud triangle theory. FraudFind works in the approach of continuous audit that will be in charge of collecting information of agents installed in user's equipment. It is based on semantic techniques applied through the collection of phrases typed by the users under study for later being transferred to a repository for later analysis. This proposal encourages to contribute with the field of cybersecurity, in the reduction of cases of financial fraud.

Question: What is the fraud triangle theory?


WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks. Cyber-physical systems depend on sensors to make automated decisions. Resonant acoustic injection attacks are already known to cause malfunctions by disabling MEMS-based gyroscopes. However, an open question remains on how to move beyond denial of service attacks to achieve full adversarial control of sensor outputs. Our work investigates how analog acoustic injection attacks can damage the digital integrity of a popular type of sensor: the capacitive MEMS accelerometer. Spoofing such sensors with intentional acoustic interference enables an out-of-spec pathway for attackers to deliver chosen digital values to microprocessors and embedded systems that blindly trust the unvalidated integrity of sensor outputs. Our contributions include (1) modeling the physics of malicious acoustic interference on MEMS accelerometers, (2) discovering the circuit-level security flaws that cause the vulnerabilities by measuring acoustic injection attacks on MEMS accelerometers as well as systems that employ on these sensors, and (3) two software-only defenses that mitigate many of the risks to the integrity of MEMS accelerometer outputs. We characterize two classes of acoustic injection attacks with increasing levels of adversarial control: output biasing and output control. We test these attacks against 20 models of capacitive MEMS accelerometers from 5 different manufacturers. Our experiments find that 75% are vulnerable to output biasing, and 65% are vulnerable to output control. To illustrate end-to-end implications, we show how to inject fake steps into a Fitbit with a $5 speaker. In our self-stimulating attack, we play a malicious music file from a smartphone's speaker to control the on-board MEMS accelerometer trusted by a local app to pilot a toy RC car. In addition to offering hardware design suggestions to eliminate the root causes of insecure amplification and filtering, we introduc

Question: How to spoof a fitbit with a $5 speaker?


Neighborhood playgrounds, fast food restaurants, and crime: relationships to overweight in low-income preschool children.. BACKGROUND We examined the relationship between overweight in preschool children and three environmental factors--the proximity of the children's residences to playgrounds and to fast food restaurants and the safety of the children's neighborhoods. We hypothesized that children who lived farther from playgrounds, closer to fast food restaurants, and in unsafe neighborhoods were more likely to be overweight.   METHODS This was a cross-sectional study of 7,020 low-income children, 36 through 59 months of age living in Cincinnati, OH. Overweight was defined as a measured body mass index > or =95th percentile. The distance between each child's residence and the nearest public playground and fast food restaurant was determined with geographic information systems. Neighborhood safety was defined by the number of police-reported crimes per 1,000 residents per year in each of 46 city neighborhoods.   RESULTS Overall, 9.2% of the children were overweight, 76% black, and 23% white. The mean (+/- SD) distances from a child's home to the nearest playground and fast food restaurant were 0.31 (+/- 0.22) and 0.70 (+/- 0.38) miles, respectively. There was no association between child overweight and proximity to playgrounds, proximity to fast food restaurants, or level of neighborhood crime. The association between child overweight and playground proximity did not differ by neighborhood crime level.   CONCLUSIONS Within a population of urban low-income preschoolers, overweight was not associated with proximity to playgrounds and fast food restaurants or with the level of neighborhood crime.

Question: What is the relationship between overweight and crime?


Question: What is ensemble methods?


Root coverage in molar teeth: a comparative controlled randomized clinical trial.. AIM To compare the clinical outcomes of laterally moved, coronally advanced flap (LMCAF) versus Bilaminar technique (BT) in the treatment of single gingival recession on molar teeth.   MATERIAL AND METHODS Fifty patients showing Miller I and II gingival recessions at first molar teeth were treated: 25 were randomly assigned to the BT group and 25 belonged to the LMCAF group. Patient's post-operative morbidity was assessed 1 week after the surgery, while aesthetic evaluation and the clinical evaluation were made 1 year later.   RESULTS No statistically significant difference was demonstrated in terms of recession and PPD reduction. Statistically greater probability of complete root coverage (CRC, Odds Ratio 22.1) and greater increase in gingival thickness were observed in the BT group. Greater increase in keratinized tissue was obtained in the LMCAF. Patient satisfaction with aesthetics was very high in both treatment groups. Better post-operative course was observed in the LMCAF, while better post-operative sensitivity and root coverage evaluation were demonstrated in patients treated with BT.   CONCLUSIONS Gingival recession at first molar teeth can be successfully treated with LMCAF and BT. Better CRC was achieved with BT, while more comfortable post-operative course was associated with the LMCAF.

Question: What is the difference between LMCAF and BT?


One-DOF Cylindrical Deployable Structures with Rigid Quadrilateral Panels. In this paper,we present a novel cylindrical deployable structure and variations of its design with the following characteristics: 1. Flat-foldable: The shape flattens into a compact 2D configuration. 2. Rigid-foldable: Each element does not deform throughout the transformation. 3. One-DOF: The mechanism has exactly one degree of freedom. 4. Thick: Facets can be substituted with thick or multilayered panels without introducing the distortion of elements.

Question: What is a one-DOF cylindrical deployable structure?


Annotating Opinions in the World Press. In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement for individual private state expressions, and show that these low-level annotations are useful for producing higher-level subjective sentence annotations.

Question: What is the inter-annotator agreement for annotating private states?


Social Media, News and Political Information during the US Election: Was Polarizing Content Concentrated in Swing States?. US voters shared large volumes of polarizing political news and information in the form of links to content from Russian, WikiLeaks and junk news sources. Was this low quality political information distributed evenly around the country, or concentrated in swing states and particular parts of the country? In this data memo we apply a tested dictionary of sources about political news and information being shared over Twitter over a ten day period around the 2016 Presidential Election. Using self-reported location information, we place a third of users by state and create a simple index for the distribution of polarizing content around the country. We find that (1) nationally, Twitter users got more misinformation, polarizing and conspiratorial content than professionally produced news. (2) Users in some states, however, shared more polarizing political news and information than users in other states. (3) Average levels of misinformation were higher in swing states than in uncontested states, even when weighted for the relative size of the user population in each state. We conclude with some observations about the impact of strategically disseminated polarizing information on public life. COMPUTATIONAL PROPAGANDA AND THE 2016 US ELECTION Social media plays an important role in the circulation of ideas about public policy and politics. Political actors and governments worldwide are deploying both people and algorithms to shape public life. Bots are pieces of software intended to perform simple, repetitive, and robotic tasks. They can perform legitimate tasks on social media like delivering news and information—real news as well as junk—or undertake malicious activities like spamming, harassment and hate speech. Whatever their uses, bots on social media platforms are able to rapidly deploy messages, replicate themselves, and pass as human users. They

Question: What is the difference between a bot and a spammer?


Microservice Ambients: An Architectural Meta-Modelling Approach for Microservice Granularity. Isolating fine-grained business functionalities byboundaries into entities called microservices is a core activityunderlying microservitization. We define microservitization asthe paradigm shift towards microservices. Determining theoptimal microservice boundaries (i.e. microservice granularity) is among the key microservitization design decisions thatinfluence the Quality of Service (QoS) of the microservice applicationat runtime. In this paper, we provide an architecturecentricapproach to model this decision problem. We build onambients — a modelling approach that can explicitly capturefunctional boundaries and their adaptation. We extend the aspect-oriented architectural meta-modelling approach of ambients—AMBIENT-PRISMA — with microservice ambients. A microservice ambient is a modelling concept that treatsmicroservice boundaries as an adaptable first-class entity. Weuse a hypothetical online movie subscription-based systemto capture a microservitization scenario using our aspectorientedmodelling approach. The results show the ability ofmicroservice ambients to express the functional boundary of amicroservice, the concerns of each boundary, the relationshipsacross boundaries and the adaptations of these boundaries. Additionally, we evaluate the expressiveness and effectivenessof microservice ambients using criteria from ArchitectureDescription Language (ADL) classification frameworkssince microservice ambients essentially support architecturedescription for microservices. The evaluation focuses on thefundamental modelling constructs of microservice ambientsand how they support microservitization properties such asutility-driven design, tool heterogeneity and decentralised governance. The evaluation highlights how microservice ambientssupport analysis, evolution and mobility/location awarenesswhich are significant to quality-driven microservice granularityadaptation. The

Question: What is microservice granularity?


Food insufficiency and American school-aged children's cognitive, academic, and psychosocial development.. OBJECTIVE This study investigates associations between food insufficiency and cognitive, academic, and psychosocial outcomes for US children and teenagers ages 6 to 11 and 12 to 16 years.   METHODS Data from the Third National Health and Nutrition Examination Survey (NHANES III) were analyzed. Children were classified as food-insufficient if the family respondent reported that his or her family sometimes or often did not get enough food to eat. Regression analyses were conducted to test for associations between food insufficiency and cognitive, academic, and psychosocial measures in general and then within lower-risk and higher-risk groups. Regression coefficients and odds ratios for food insufficiency are reported, adjusted for poverty status and other potential confounding factors.   RESULTS After adjusting for confounding variables, 6- to 11-year-old food-insufficient children had significantly lower arithmetic scores and were more likely to have repeated a grade, have seen a psychologist, and have had difficulty getting along with other children. Food-insufficient teenagers were more likely to have seen a psychologist, have been suspended from school, and have had difficulty getting along with other children. Further analyses divided children into lower-risk and higher-risk groups. The associations between food insufficiency and children's outcomes varied by level of risk.   CONCLUSIONS The results demonstrate that negative academic and psychosocial outcomes are associated with family-level food insufficiency and provide support for public health efforts to increase the food security of American families.

Question: What is the difference between a food insufficiency and a food insecurity?


Smarter universities: A vision for the fast changing digital era. In this paper we analyze the current situation of education in universities, with particular reference to the European scenario. Specifically, we observe that recent evolutions, such as pervasive networking and other enabling technologies, have been dramatically changing human life, knowledge acquisition, and the way works are performed and people learn. In this societal change, universities must maintain their leading role. Historically, they set trends primarily in education but now they are called to drive the change in other aspects too, such as management, safety, and environment protection. The availability of newer and newer technology reflects on how the relevant processes should be performed in the current fast changing digital era. This leads to the adoption of a variety of smart solutions in university environments to enhance the quality of life and to improve the performances of both teachers and students. Nevertheless, we argue that being smart is not enough for a modern university. In fact, universities should better become smarter. By "smarter university" we mean a place where knowledge is shared between employees, teachers, students, and all stakeholders in a seamless way. In this paper we propose, and discuss a smarter university model, derived from the one designed for the development of

Question: What is the smarter university model?


FOR PREDICTING STUDENT PERFORMANCE. Although data mining has been successfully implemented in the business world for some time now, its use in higher education is still relatively new, i.e. its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was evaluated with the passing grade at the exam. The impact of students' socio-demographic variables, achieved results from high school and from the entrance exam, and attitudes towards studying which can have an affect on success, were all investigated. In future investigations, with identifying and evaulating variables associated with process of studying, and with the sample increase, it would be possible to produce a model which would stand as a foundation for the development of decision support system in higher education.

Question: What is the difference between data mining and data analysis?


A Dual Path Deep Network for Single Image Super-Resolution Reconstruction. Super-resolution reconstruction based on deep learning has come a long way since the first proposed method in 2015. Numerous methods have been developed for this task using deep learning approaches. Among these methods, residual deep learning algorithms have shown better performance. Although all early proposed deep learning based super-resolution frameworks used bicubic upsampled versions of low resolution images as the main input, most of the current ones use the low resolution images directly by adding up-sampling layers to their networks. In this work, we propose a new method by using both low resolution and bicubic upsampled images as the inputs to our network. The final results confirm that decreasing the depth of the network in lower resolution space and adding the bicubic path lead to almost similar results to those of the deeper networks in terms of PSNR and SSIM, yet making the network computationally inexpensive and more efficient.

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


Nitro: Hardware-Based System Call Tracing for Virtual Machines. Virtual machine introspection (VMI) describes the method of monitoring and analyzing the state of a virtual machine from the hypervisor level. This lends itself well to security applications, though the hardware virtualization support from Intel and AMD was not designed with VMI in mind. This results in many challenges for developers of hardware-supported VMI systems. This paper describes the design and implementation of our prototype framework, Nitro, for system call tracing and monitoring. Since Nitro is a purely VMI-based system, it remains isolated from attacks originating within the guest operating system and is not directly visible from within the guest. Nitro is extremely flexible as it supports all three system call mechanisms provided by the Intel x86 architecture and has been proven to work in Windows, Linux, 32-bit, and 64-bit environments. The high performance of our system allows for real-time capturing and dissemination of data without hindering usability. This is supported by extensive testing with various guest operating systems. In addition, Nitro is resistant to circumvention attempts due to a construction called hardware rooting. Finally, Nitro surpasses similar systems in both performance and functionality.

Question: What is a hardware-based system call tracing for virtual machines?


Evolutionary Competition in Platform Ecosystems. I competition has received scant attention in prior studies, which predominantly study interplatform competition. We develop a middle-range theory of how complementarity between input control and a platform extension's modularization—by inducing evolution—influences its performance in a platform market. Primary and archival data spanning five years from 342 Firefox extensions show that such complementarity fosters performance by accelerating an extension's perpetual evolution.

Question: What is the middle-range theory of evolutionary competition in platform ecosystems?


A new miniaturized antenna for ISM 433 MHz frequency band. A new miniaturized antenna for 433MHz frequency band is introduced for sensor networks. The antenna is a magnetic monopole loaded with a capacitor, which can significantly decrease the resonant frequency of the structure. It is designed to operate in the ISM 433MHz band on a small PCB (103mm⋆55mm) and has at least 3MHz bandwidth. Moreover, its radiation efficiency is suitable for the possible applications and its pattern is omnidirectionnal.

Question: What is the frequency band of the antenna?


Zero-Knowledge Proofs of Knowledge Without Interaction (Extended Abstract). A zero-knowledge proof system of knowledge is a protocol between two parties called the prover and the verifier. The prover wants to convince the verifier that he "knows" the proof of a given theorem without revealing any additional information. This is different from a zero-knowledge proof system of membership where the prover convinces the verifier only of the veridicity of the statement. Zero-knowledge proofs of knowledge are very useful tools in the design of secure protocols. Though, the concept of a proof of knowledge is a very subtle one and great care is needed to obtain a satisfying formalization. In this paper, we investigate the concept of a zero-knowledge proof of knowledge in the noninteractive model of [5, 61. Here, the prover and the verifier share a short random string and the only communication allowed is from the prover to the verifier. Although this is a simpler model than the interactive one, still formalizing zeroknowledge proofs of knowledge is a delicate task. The main results of the paper are the following e We present formal definitions for the concept of non-interactive zero-knowledge proofs 'Dipartimento di Informatica ed Applicazioni, Universiti di Salerno, 84081 Baronissi (SA), Italy. Partially supported by Italian Ministry of Universit.y and Research (M.U.R.S.T.) and by National Council for Research (C.N.R.) under grant 91.02326.CT12. Email: ads@udsab.dia.unisa.it 'Aiken Computation Laboratory, Harvard University, Cambridge, MA 02138, USA and Dipart,imento di Informatica, Universiti di Salerno. Supported at Harvard by NSF Grant # NSF-CCR-90-07677. Email: persiano@endor.harvard.edu GIUSEPPE PERSIANO+

Question: What is the difference between a zero-knowledge proof of membership and a zero-knowledge proof of knowledge?


Quantum theory , the Church-Turing principle and the universal quantum computer. It is argued that underlying the Church-Turing hypothesis there is an implicit physical assertion. Here, this assertion is presented explicitly as a physical principle: 'every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means'. Classical physics and the universal Turing machine, because the former is continuous and the latter discrete, do not obey the principle, at least in the strong form above. A class of model computing machines that is the quantum generalization of the class of Turing machines is described, and it is shown that quantum theory and the 'universal quantum computer' are compatible with the principle. Computing machines resembling the universal quantum computer could, in principle, be built and would have many remarkable properties not reproducible by any Turing machine. These do not include the computation of non-recursive functions, but they do include 'quantum parallelism', a method by which certain probabilistic tasks can be performed faster by a universal quantum computer than by any classical restriction of it. The intuitive explanation of these properties places an intolerable strain on all interpretations of quantum theory other than Everett's. Some of the numerous connections between the quantum theory of computation and the rest of physics are explored. Quantum complexity theory allows a physically more reasonable definition of the 'complexity' or 'knowledge' in a physical system than does classical complexity theory. Current address: Centre for Quantum Computation, Clarendon Laboratory, Department of Physics, Parks Road, OX1 3PU Oxford, United Kingdom. Email: david.deutsch@qubit.org This version (Summer 1999) was edited and converted to L TEX by Wim van Dam at the Centre for Quantum Computation. Email:wimvdam@qubit.org 1 Computing machines and the Church-Turing principle The theory o

Question: What is the Church-Turing principle?


A Joint Reconstruction and Segmentation Method for Limited-Angle X-Ray Tomography. Limited-angle computed tomography (CT) is common in industrial applications, where incomplete projection data can cause artifacts. For objects made from homogeneous materials, we propose a joint reconstruction and segmentation method that performs joint image reconstruction and segmentation directly on the projection data. We describe an alternating minimization algorithm to solve the resulting optimization problem, and we modify the primal-dual hybrid gradient algorithm for the non-convex piecewise constant Mumford-Shah model, which is a popular approximation model in biomedical image segmentation. The effectiveness of the proposed approach is validated by simulation and by application to actual micro-CT data sets.

Question: What is the joint reconstruction and segmentation method for limited-angle x-ray tomography?


SAX-EFG: an evolutionary feature generation framework for time series classification. A variety of real world applications fit into the broad definition of time series classification. Using traditional machine learning approaches such as treating the time series sequences as high dimensional vectors have faced the well known "curse of dimensionality" problem. Recently, the field of time series classification has seen success by using preprocessing steps that discretize the time series using a Symbolic Aggregate ApproXimation technique (SAX) and using recurring subsequences ("motifs") as features.  In this paper we explore a feature construction algorithm based on genetic programming that uses SAX-generated motifs as the building blocks for the construction of more complex features. The research shows that the constructed complex features improve the classification accuracy in a statistically significant manner for many applications.

Question: What is a genetic programming?


Robust Influence Maximization. In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.

Question: How to solve the robust influence maximization problem?


An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition. ÐThis paper describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentationsymbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition. Index TermsÐHandwriting modeling, preprocessing, segmentation, feature extraction, hidden Markov models, word recognition, rejection.

Question: What is the size of the canadian military?


Formation and suppression of acoustic memories during human sleep. Sleep and memory are deeply related, but the nature of the neuroplastic processes induced by sleep remains unclear. Here, we report that memory traces can be both formed or suppressed during sleep, depending on sleep phase. We played samples of acoustic noise to sleeping human listeners. Repeated exposure to a novel noise during Rapid Eye Movements (REM) or light non-REM (NREM) sleep leads to improvements in behavioral performance upon awakening. Strikingly, the same exposure during deep NREM sleep leads to impaired performance upon awakening. Electroencephalographic markers of learning extracted during sleep confirm a dissociation between sleep facilitating memory formation (light NREM and REM sleep) and sleep suppressing learning (deep NREM sleep). We can trace these neural changes back to transient sleep events, such as spindles for memory facilitation and slow waves for suppression. Thus, highly selective memory processes are active during human sleep, with intertwined episodes of facilitative and suppressive plasticity. Though memory and sleep are related, it is still unclear whether new memories can be formed during sleep. Here, authors show that people could learn new sounds during REM or light non-REM sleep, but that learning was suppressed when sounds were played during deep NREM sleep.

Question: What is the difference between REM and non-REM sleep?


Exploiting potential citation papers in scholarly paper recommendation. To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, we identify "potential citation papers" through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigate which sections of papers can be leveraged to represent papers effectively.  On a scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when we discover potential citation papers using imputed similarities via collaborative filtering and represent candidate papers using both the full text and assigning more weight to the conclusion sections.

Question: How to exploit potential citation papers in scholarly paper recommendation?


Semantic similarity based evaluation for C programs through the use of symbolic execution. Automatic grading of programs has existed in various fields for many years ago. Within this paper, we propose a method for evaluating C programs. Two approaches are distinguished in this context: static and dynamic analysis methods. Unlike the dynamic analysis that requires an executable program to be evaluated, static analysis could evaluate a program even if it is not totally correct. The proposed method is based on static analysis of programs. It consists of comparing the evaluated program with the evaluator-provided program through their Control Flow Graphs. Here, the great challenge is to deal with the multiplicity of solutions that exists for the same programming problem. As a solution to this weakness, we propose an innovative similarity measure that compares two programs according to their semantic executions. In fact, the evaluated program is compared to the evaluator-provided program called model program by using the symbolic execution technique. The experimentations presented in this work are performed by using a basic implementation of the proposed method. The obtained results reveal a promising realization in the field of automated evaluation of programs. They also show that the proposed method guarantees a considerable approximation to the human program evaluation.

Question: What is the difference between static and dynamic analysis?


High aspect-ratio 3D microstructures via near-field electrospinning for energy storage applications. High-aspect-ratio, three dimensional (3D) microstructures have been constructed by a direct-write method via self-aligned near-field electrospinning process. Using papers as the collectors, the process enables both design and fabrication of a variety of 3D structures, such as walls, grids and other configurations based on layer-by-layer deposition of electrospun micro/nano fibers. Afterwards, these fabricated polymeric structures can be mechanically separated from the paper substrate for various applications. In this work, we have successfully fabricated 20-layer, ca. 100 μm in thickness, 2 × 2 cm2 3D grid-structures made of PVDF (Polyvinylidene fluoride) electrospun fibers. After a coating process using the dip-and-dry process in the CNT (carbon nanotube) ink, conductive and flexible electrodes made of CNT coated polymer grid-structures have been demonstrated and assembled as a micro-supercapacitor.

Question: How to make a micro-supercapacitor?


An improved algorithm for feature selection using fractal dimension. Dimensionality reduction is an important issue in data mining and machine learning. Traina[1] proposed a feature selection algorithm to select the most important attributes for a given set of n-dimensional vectors based on correlation fractal dimension. The author used a kind of multi-dimensional "quad-tree" structure to compute the fractal dimension. Inspired by his work, we propose a new and simpler algorithm to compute the fractal dimension, and design a novel and faster feature selection algorithm using correlation fractal dimension, whose time complexity is lower than that of Traina's. The main idea is when computing the fractal dimension of (d-1)-dimensional data, the intermediate generated results of the extended d-dimensional data is reused. It inherits the desirable properties described as in [1]. Also, Our algorithm does not require the grid sizes decrease by half as the original "quad-tree" algorithm. Experiments show our feature selection algorithm has a good efficiency over the test dataset.

Question: What is the time complexity of the algorithm?


Efficient Zero-Knowledge Contingent Payments in Cryptocurrencies Without Scripts. One of the most promising innovations offered by the cryptographic currencies (like Bitcoin) are the so-called smart contracts, which can be viewed as financial agreements between mutually distrusting participants. Their execution is enforced by the mechanics of the currency, and typically has monetary consequences for the parties. The rules of these contracts are written in the form of so-called "scripts", which are pieces of code in some "scripting language". Although smart contracts are believed to have a huge potential, for the moment they are not widely used in practice. In particular, most of Bitcoin miners allow only to post standard transactions (i.e.: those without the non-trivial scripts) on the blockchain. As a result, it is currently very hard to create non-trivial smart contracts in Bitcoin. Motivated by this, we address the following question: "is it possible to create non-trivial efficient smart contracts using the standard transactions only?" We answer this question affirmatively, by constructing efficient Zero-Knowledge Contingent Payment protocol for a large class of NP-relations. This includes the relations for which efficient sigma protocols exist. In particular, our protocol can be used to sell a factorization (p, q) of an RSA modulus n = pq, which is an example that we implemented and tested its efficiency in practice. As another example of the "smart contract without scripts" we show how our techniques can be used to implement the contract called "trading across chains".

Question: Is it possible to create non-trivial efficient smart contracts using the standard transactions only?


Ensemble learning for free with evolutionary algorithms?. Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-EEL) or incrementally along evolution (On-EEL). Experiments on a set of benchmark problems show that Off-EEL outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.

Question: What is the difference between ensemble learning and evolutionary learning?


Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate.. Clinical trials have suggested that neurofeedback may be efficient in treating attention-deficit/hyperactivity disorder (ADHD). We compared the effects of a 3-month electroencephalographic feedback program providing reinforcement contingent on the production of cortical sensorimotor rhythm (12-15 Hz) and betal activity (15-18 Hz) with stimulant medication. Participants were N = 34 children aged 8-12 years, 22 of which were assigned to the neurofeedback group and 12 to the methylphenidate group according to their parents' preference. Both neurofeedback and methylphenidate were associated with improvements on all subscales of the Test of Variables of Attention, and on the speed and accuracy measures of the d2 Attention Endurance Test. Furthermore, behaviors related to the disorder were rated as significantly reduced in both groups by both teachers and parents on the IOWA-Conners Behavior Rating Scale. These findings suggest that neurofeedback was efficient in improving some of the behavioral concomitants of ADHD in children whose parents favored a nonpharmacological treatment.

Question: What is the difference between neurofeedback and methylphenidate?


The psychology of globalization.. The influence of globalization on psychological functioning is examined. First, descriptions of how globalization is occurring in various world regions are presented. Then the psychological consequences of globalization are described, with a focus on identity issues. Specifically, it is argued that most people worldwide now develop a bicultural identity that combines their local identity with an identity linked to the global culture; that identity confusion may be increasing among young people in non-Western cultures as a result of globalization; that some people join self-selected cultures to maintain an identity that is separate from the global culture; and that a period of emerging adulthood increasingly extends identity explorations beyond adolescence, through the mid- to late twenties.

Question: What is the psychology of globalization?


Fully Convolutional Networks for Semantic Segmentation. Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.

Question: What is a fully convolutional network?


Scaling regression testing to large software systems. When software is modified, during development and maintenance, it is <i>regression tested</i> to provide confidence that the changes did not introduce unexpected errors and that new features behave as expected. One important problem in regression testing is how to select a subset of test cases, from the test suite used for the original version of the software, when testing a modified version of the software. Regression-test-selection techniques address this problem. Safe regression-test-selection techniques select every test case in the test suite that may behave differently in the original and modified versions of the software. Among existing safe regression testing techniques, efficient techniques are often too imprecise and achieve little savings in testing effort, whereas precise techniques are too expensive when used on large systems. This paper presents a new regression-test-selection technique for Java programs that is safe, precise, and yet scales to large systems. It also presents a tool that implements the technique and studies performed on a set of subjects ranging from 70 to over 500 KLOC. The studies show that our technique can efficiently reduce the regression testing effort and, thus, achieve considerable savings.

Question: How to select a subset of test cases from a test suite when testing a modified version of the software?


Service Level Agreement in Cloud Computing. Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS) attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. This paper proposes a mechanism for managing SLAs in a cloud computing environment using the Web Service Level Agreement(WSLA) framework, developed for SLA monitoring and SLA enforcement in a Service Oriented Architecture (SOA). We use the third party support feature of WSLA to delegate monitoring and enforcement tasks to other entities in order to solve the trust issues. We also present a real world use case to validate our proposal.

Question: What is the difference between cloud computing and traditional IT infrastructure?


On Poisson Graphical Models. Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables. These standard instances, however, are ill-suited to modeling count data, which are increasingly ubiquitous in big-data settings such as genomic sequencing data, user-ratings data, spatial incidence data, climate studies, and site visits. Existing classes of Poisson graphical models, which arise as the joint distributions that correspond to Poisson distributed node-conditional distributions, have a major drawback: they can only model negative conditional dependencies for reasons of normalizability given its infinite domain. In this paper, our objective is to modify the Poisson graphical model distribution so that it can capture a rich dependence structure between count-valued variables. We begin by discussing two strategies for truncating the Poisson distribution and show that only one of these leads to a valid joint distribution. While this model can accommodate a wider range of conditional dependencies, some limitations still remain. To address this, we investigate two additional novel variants of the Poisson distribution and their corresponding joint graphical model distributions. Our three novel approaches provide classes of Poisson-like graphical models that can capture both positive and negative conditional dependencies between count-valued variables. One can learn the graph structure of our models via penalized neighborhood selection, and we demonstrate the performance of our methods by learning simulated networks as well as a network from microRNA-sequencing data.

Question: What is the difference between the three models?


A Weighted Finite-State Transducer (WFST)-Based Language Model for Online Indic Script Handwriting Recognition. Though designing of classifies for Indic script handwriting recognition has been researched with enough attention, use of language model has so far received little exposure. This paper attempts to develop a weighted finite-state transducer (WFST) based language model for improving the current recognition accuracy. Both the recognition hypothesis (i.e. the segmentation lattice) and the lexicon are modeled as two WFSTs. Concatenation of these two FSTs accept a valid word(s) which is (are) present in the recognition lattice. A third FST called error FST is also introduced to retrieve certain words which were missing in the previous concatenation operation. The proposed model has been tested for online Bangla handwriting recognition though the underlying principle can equally be applied for recognition of offline or printed words. Experiment on a part of ISI-Bangla handwriting database shows that while the present classifiers (without using any language model) can recognize about 73% word, use of recognition and lexicon FSTs improve this result by about 9% giving an average word-level accuracy of 82%. Introduction of error FST further improves this accuracy to 93%. This remarkable improvement in word recognition accuracy by using FST-based language model would serve as a significant revelation for the research in handwriting recognition, in general and Indic script handwriting recognition, in particular.

Question: How to improve the accuracy of Indic script handwriting recognition?


Speed and power scaling of SRAM's. Simple models for the delay, power, and area of a static random access memory (SRAM) are used to determine the optimal organizations for an SRAM and study the scaling of their speed and power with size and technology. The delay is found to increase by about one gate delay for every doubling of the RAM size up to 1 Mb, beyond which the interconnect delay becomes an increasingly significant fraction of the total delay. With technology scaling, the nonscaling of threshold mismatches in the sense amplifiers is found to significantly impact the total delay in generations of 0.1 /spl mu/m and below.

Question: What is the area of an SRAM?


Skin Detection Using Color, Texture and Space Information. Among various skin detection methods, Skin Probability Map (SPM) method is an effective one. Though SPM method possesses high true acceptance rate (TAR), its false acceptance rate (FAR) is unacceptable in some cases. The reason is that SPM method only use pixel-level color information. This paper proposes an improved skin detection method that integrates color, texture and space information. After color filter, a texture filter is constructed based on texture features extracted form Gabor wavelet transform. Texture filter will further filter non-skin pixels, meanwhile, it may also filter some skin pixel. To compensate the loss, after texture filter, a marker driven watershed transform is then used to grow already obtained skin regions. Experimental results show that the proposed method can achieve better performance than that of SPM.

Question: What is the difference between SPM and the proposed method?


Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding. Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.

Question: What is the role of feature selection, training data selection, and probabilistic thresholding in improving different multi-label classification approaches?


ThingTalk : A Distributed Language for a Social Internet of Things. The Internet of Things has increased the number of connected devices by an order of magnitude. This increase comes with a corresponding increase in complexity, which has led to centralized architectures that do not respect the privacy of the users and incur high development and deployment costs. We propose the Open Thing Platform as a new distributed architecture which is built around ThingTalk, a new high level declarative programming language that can express the interaction between multiple physical devices and online services. The architecture abstracts away the details of the communication without sacrificing privacy. We experiment with ThingTalk in multiple domains and we show that several applications can be written in a few lines of code and with no need for any server infrastructure.

Question: What is the open thing platform?


Toward a Science of Cyber–Physical System Integration. System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.

Question: What is the science of cyber-physical system integration?


Smart Refrigerator Based on Internet of Things (IoT): An Approach to Efficient Food Management. This paper focuses on the technology of the smart refrigerator connected through the Internet of Things (IoT) platform. It deals with the issue of food wastage and efficient food distribution through a network of connected refrigerators within a defined neighborhood. This paper defines the three-layer architecture: front-end layer, gateway layer and the back-end layer that can allow the implementation of the system. This low-cost network can be created through a microcontroller and wireless sensors at the front-end architecture. The process was discussed to determine: the stock item below a defined quantity or threshold limit, determining the product expiration date and eventually determining the purchase or exchange of food items across the network. It also includes the proof of concept technology by allowing connection of an ultrasonic sensor to an IoT platform that can allow the exchange of information on the positioning of the food items inside the refrigerator. This eventually can allow for a large-scale commercial implementation of the project to deal with the current issue of food distribution.

Question: What is the difference between a refrigerator and a smart refrigerator?


Taxonomy of attacks on industrial control protocols. Industrial control systems (ICS) are highly distributed information systems used to control and monitor critical infrastructures such as nuclear plants, power generation and distribution plants, Oil and Gas and many other facilities. The main architecture principles of ICS are; real time response, high availability and reliability. For these specific purposes, several protocols has been designed to ensure the control and supervision operations. Modbus and DNP3 are the most used protocols in the ICS world due to their compliance with real time needs. With the increasing of the connectivity to the internet world for business reasons, ICS adopted Internet based technologies and most of communication protocols are redesigned to work over IP. This openness exposed the ICS components as well as communication protocols to cyber-attacks with a higher risk than attacks on traditional IT systems. In order to facilitate the risk assessment of cyber-attacks on ICS protocols we propose a taxonomy model of different identified attacks on Modbus and DNP3.the model is based on the threat origin, threat type, attack type, attack scenario, vulnerability type and the impact of the attack. We populate this Taxonomy model with identified attacks on Modbus and DNP3 from previous academic and industrial works.

Question: What is the taxonomy of attacks on industrial control protocols?


Term-Weighting Approaches in Automatic Text Retrieval. The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared. 1. AUTOMATIC TEXT ANALYSIS In the late 195Os, Luhn [l] first suggested that automatic text retrieval systems could be designed based on a comparison of content identifiers attached both to the stored texts and to the users' information queries. Typically, certain words extracted from the texts of documents and queries would be used for content identification; alternatively, the content representations could be chosen manually by trained indexers familiar with the subject areas under consideration and with the contents of the document collections. In either case, the documents would be represented by term vectors of the form D= (ti,tj,...ytp) (1) where each tk identifies a content term assigned to some sample document D. Analogously, the information requests, or queries, would be represented either in vector form, or in the form of Boolean statements. Thus, a typical query Q might be formulated as Q = (qa,qbr.. . ,4r) (2)

Question: What is the difference between a term vector and a term frequency vector?


Brand Name Logo Recognition of Fast Food and Healthy Food among Children. The fast food industry has been increasingly criticized for creating brand loyalty in young consumers. Food marketers are well versed in reaching children and youth given the importance of brand loyalty on future food purchasing behavior. In addition, food marketers are increasingly targeting the Hispanic population given their growing spending power. The fast food industry is among the leaders in reaching youth and ethnic minorities through their marketing efforts. The primary objective of this study was to determine if young children recognized fast food restaurant logos at a higher rate than other food brands. Methods Children (n = 155; 53% male; 87% Hispanic) ages 4–8 years were recruited from elementary schools and asked to match 10 logo cards to products depicted on a game board. Parents completed a survey assessing demographic and psychosocial characteristics associated with a healthy lifestyle in the home. Results Older children and children who were overweight were significantly more likely to recognize fast food restaurant logos than other food logos. Moreover, parents' psychosocial and socio-demographic characteristics were associated with the type of food logo recognized by the children. Conclusions Children's high recognition of fast food restaurant logos may reflect greater exposure to fast food advertisements. Families' socio-demographic characteristics play a role in children's recognition of food logos.

Question: How do children recognize fast food logos?


An active volumetric model for 3D reconstruction. In this paper, we present an active volumetric model (AVM) for 3D reconstruction from multiple calibrated images of a scene. The AVM is a physically motivated 3D deformable model which shrinks actively under the influence of multiple simulated forces towards the real scene by throwing away some of its voxels. It provides a computational framework to integrate several constraints in an intelligible way. In the current work, we use three forces derived respectively from the smooth constraint, the compulsory silhouette constraint, and the color consistency constraint. Based on the composition of the 3 forces, our algorithm can significantly restrain holes and floating voxels, which plague voxel coloring algorithms, and produce precise and smooth models. We test our algorithm by experiments based on both synthetic and real data.

Question: What is a volumetric model?


A Planar Magic-T Using Substrate Integrated Circuits Concept. In this letter, a slotline to substrate integrated waveguide transition is proposed for the development of substrate integrated circuits. The insertion loss of the back-to-back transition is less than 1 dB from 8.7 to 9.0 GHz. With this transition, a planar magic-T is studied and designed. Measured results indicate a very good performance of the fabricated magic-T is observed within the experimental frequency range of 8.4-9.4 GHz. The amplitude and phase imbalances are less than 0.2 dB and 1.5deg, respectively.

Question: What is a magic-T?


Developing an Isolated Word Recognition System in MATLAB. The Development Workflow There are two major stages within isolated word recognition: a training stage and a testing stage. Training involves "teaching" the system by building its dictionary, an acoustic model for each word that the system needs to recognize. In our example, the dictionary comprises the digits 'zero' to 'nine'. In the testing stage we use acoustic models of these digits to recognize isolated words using a classification algorithm. The development workflow consists of three steps: Speech acquisition • Speech analysis • User interface development •

Question: How to develop an isolated word recognition system in matlab?


Age-Invariant Face Recognition. One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). These shape and texture changes degrade the performance of automatic face recognition systems. However, facial aging has not received substantial attention compared to other facial variations due to pose, lighting, and expression. We propose a 3D aging modeling technique and show how it can be used to compensate for the age variations to improve the face recognition performance. The aging modeling technique adapts view-invariant 3D face models to the given 2D face aging database. The proposed approach is evaluated on three different databases (i.g., FG-NET, MORPH, and BROWNS) using FaceVACS, a state-of-the-art commercial face recognition engine.

Question: How to improve face recognition performance?