ProteusTM: Abstraction Meets Performance in Transactional Memory. The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no universal implementation fits each and every workload. In fact, the best TM in a given workload can reveal to be disastrous for another one. This forces developers to face the complex task of tuning TM implementations, which significantly hampers their wide adoption. In this paper, we address the challenge of automatically identifying the best TM implementation for a given workload. Our proposed system, ProteusTM, hides behind the TM interface a large library of implementations. Underneath, it leverages a novel multi-dimensional online optimization scheme, combining two popular learning techniques: Collaborative Filtering and Bayesian Optimization.  We integrated ProteusTM in GCC and demonstrate its ability to switch between TMs and adapt several configuration parameters (e.g., number of threads). We extensively evaluated ProteusTM, obtaining average performance <3% from optimal, and gains up to 100x over static alternatives.

Question: What is the best TM implementation for a given workload?


Pay Attention to the Ending: Strong Neural Baselines for the ROC Story Cloze Task. We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings. We develop a model that uses hierarchical recurrent networks with attention to encode the sentences in the story and score candidate endings. By discarding the large training set and only training on the validation set, we achieve an accuracy of 74.7%. Even when we discard the story plots (sentences before the ending) and only train to choose the better of two endings, we can still reach 72.5%. We then analyze this "ending-only" task setting. We estimate human accuracy to be 78% and find several types of clues that lead to this high accuracy, including those related to sentiment, negation, and general ending likelihood regardless of the story context.

Question: What is the ROC story cloze task?


Best Practices for Data Centers : Lessons Learned from Benchmarking 22 Data Centers. Over the past few years, the authors benchmarked 22 data center buildings. From this effort, we have determined that data centers can be over 40 times as energy intensive as conventional office buildings. Studying the more efficient of these facilities enabled us to compile a set of "best-practice" technologies for energy efficiency. These best practices include: improved air management, emphasizing control and isolation of hot and cold air streams; rightsizing central plants and ventilation systems to operate efficiently both at inception and as the data center load increases over time; optimized central chiller plants, designed and controlled to maximize overall cooling plant efficiency, central air-handling units, in lieu of distributed units; "free cooling" from either air-side or water-side economizers; alternative humidity control, including elimination of control conflicts and the use of direct evaporative cooling; improved uninterruptible power supplies; high-efficiency computer power supplies; on-site generation combined with special chillers for cooling using the waste heat; direct liquid cooling of racks or computers; and lowering the standby losses of standby generation systems. Other benchmarking findings include power densities from 5 to nearly 100 Watts per square foot; though lower than originally predicted, these densities are growing. A 5:1 variation in cooling effectiveness index (ratio of cooling power to computer power) was found, as well as large variations in power distribution efficiency and overall center performance (ratio of computer power to total building power). These observed variations indicate the potential of energy savings achievable through the implementation of best practices in the design and operation of data centers. The Data Center Challenge The energy used by a typical rack of state-of-the art servers, drawing 20 kilowatts of power at 10 cen

Question: What is the best practices for data centers?


Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions. Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve, and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.

Question: How can we detect calcifications in low-dose chest CT scans?


Taxonomy for description of cross-domain attacks on CPS. The pervasiveness of Cyber-Physical Systems (CPS) in various aspects of the modern society grows rapidly. This makes CPS to increasingly attractive targets for various kinds of attacks. We consider cyber-security as an integral part of CPS security. Additionally, the necessity exists to investigate the CPS-specific aspects which are out of scope of cyber-security. Most importantly, attacks capable to cross the cyber-physical domain boundary should be analyzed. The vulnerability of CPS to such cross-domain attacks has been practically proven by numerous examples, e.g., by the currently most famous Stuxnet attack. In this paper, we propose taxonomy for description of attacks on CPS. The proposed taxonomy is capable of representing both conventional cyber-attacks as well as cross-domain attacks on CPS. Furthermore, based on the proposed taxonomy, we define the attack categorization. Several possible application areas of the proposed taxonomy are extensively discussed. Among others, it can be used to establish a knowledge base about attacks on CPS known in the literature. Furthermore, the proposed description structure will foster the quantitative and qualitative analysis of these attacks, both of which are necessarily to improve CPS security.

Question: What is the taxonomy for description of cross-domain attacks on CPS?


Continuous speech recognition using hidden Markov models. The use of hidden Markov models (HMMs) in continuous speech recognition is reviewed. Markov models are presented as a generalization of their predecessor technology, dynamic programming. A unified view is offered in which both linguistic decoding and acoustic matching are integrated into a single, optimal network search framework. Advances in recognition architectures are discussed. The fundamentals of Viterbi beam search, the dominant search algorithm used today in speed recognition, are presented. Approaches to estimating the probabilities associated with an HMM model are examined. The HMM-supervised training paradigm is examined. Several examples of successful HMM-based speech recognition systems are reviewed.<<ETX>>

Question: What is the difference between a Markov model and a Hidden Markov model?


A state-of-the-art 3D sensor for robot navigation. This paper relates first experiences using a state-of-the-art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor features a frame rate of up to 30 frames per second. Its capabilities in terms of basic obstacle avoidance and local path-planning are evaluated and compared to the performance of a standard laser scanner.

Question: What is a state-of-the-art 3D sensor?


Towards Knowledge-Driven Annotation. While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations.

Question: What is the size of the canadian military?


Smooth assembled mappings for large-scale real walking. Virtual reality applications prefer real walking to provide highly immersive presence than other locomotive methods. Mapping-based techniques are very effective for supporting real walking in small physical workspaces while exploring large virtual scenes. However, the existing methods for computing real walking maps suffer from poor quality due to distortion. In this paper, we present a novel divide-and-conquer method, called Smooth Assembly Mapping (SAM), to compute real walking maps with low isometric distortion for large-scale virtual scenes. First, the input virtual scene is decomposed into a set of smaller local patches. Then, a group of local patches is mapped together into a real workspace by minimizing a low isometric distortion energy with smoothness constraints between the adjacent patches. All local patches are mapped and assembled one by one to obtain a complete map. Finally, a global optimization is adopted to further reduce the distortion throughout the entire map. Our method easily handles teleportation technique by computing maps of individual regions and assembling them with teleporter conformity constraints. A large number of experiments, including formative user studies and comparisons, have shown that our method succeeds in generating high-quality real walking maps from large-scale virtual scenes to small real workspaces and is demonstrably superior to state-of-the-art methods.

Question: How large is the canadian military?


Artificial Intelligence as an Effective Classroom Assistant. The field of artificial intelligence in education (AIED) uses techniques from AI and cognitive science to better understand the nature of learning and teaching and to build systems to help learners gain new skills or understand new concepts. This article studies metareviews and meta-analyses to make the case for blended learning, wherein the teacher can offload some work to AIED systems.

Question: What is the difference between metareviews and meta-analyses?


Single Image Super-Resolution via Multiple Mixture Prior Models. Example learning-based single image super-resolution (SR) is a promising method for reconstructing a high-resolution (HR) image from a single-input low-resolution (LR) image. Lots of popular SR approaches are more likely either time-or space-intensive, which limit their practical applications. Hence, some research has focused on a subspace view and delivered state-of-the-art results. In this paper, we utilize an effective way with mixture prior models to transform the large nonlinear feature space of LR images into a group of linear subspaces in the training phase. In particular, we first partition image patches into several groups by a novel selective patch processing method based on difference curvature of LR patches, and then learning the mixture prior models in each group. Moreover, different prior distributions have various effectiveness in SR, and in this case, we find that student-t prior shows stronger performance than the well-known Gaussian prior. In the testing phase, we adopt the learned multiple mixture prior models to map the input LR features into the appropriate subspace, and finally reconstruct the corresponding HR image in a novel mixed matching way. Experimental results indicate that the proposed approach is both quantitatively and qualitatively superior to some state-of-the-art SR methods.

Question: What is the difference between the Gaussian prior and the Student-t prior?


Deep Contrast Learning for Salient Object Detection. Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.

Question: Deep Contrast Learning for Salient Object Detection


The sil Locus in Streptococcus Anginosus Group: Interspecies Competition and a Hotspot of Genetic Diversity. The Streptococcus Invasion Locus (Sil) was first described in Streptococcus pyogenes and Streptococcus pneumoniae, where it has been implicated in virulence. The two-component peptide signaling system consists of the SilA response regulator and SilB histidine kinase along with the SilCR signaling peptide and SilD/E export/processing proteins. The presence of an associated bacteriocin region suggests this system may play a role in competitive interactions with other microbes. Comparative analysis of 42 Streptococcus Anginosus/Milleri Group (SAG) genomes reveals this to be a hot spot for genomic variability. A cluster of bacteriocin/immunity genes is found adjacent to the sil system in most SAG isolates (typically 6-10 per strain). In addition, there were two distinct SilCR peptides identified in this group, denoted here as SilCRSAG-A and SilCRSAG-B, with corresponding alleles in silB. Our analysis of the 42 sil loci showed that SilCRSAG-A is only found in Streptococcus intermedius while all three species can carry SilCRSAG-B. In S. intermedius B196, a putative SilA operator is located upstream of bacteriocin gene clusters, implicating the sil system in regulation of microbe-microbe interactions at mucosal surfaces where the group resides. We demonstrate that S. intermedius B196 responds to its cognate SilCRSAG-A, and, less effectively, to SilCRSAG-B released by other Anginosus group members, to produce putative bacteriocins and inhibit the growth of a sensitive strain of S. constellatus.

Question: What is the sil locus?


Privacy and rationality in individual decision making. Traditional theory suggests consumers should be able to manage their privacy. Yet, empirical and theoretical research suggests that consumers often lack enough information to make privacy-sensitive decisions and, even with sufficient information, are likely to trade off long-term privacy for short-term benefits

Question: What is the difference between privacy and rationality?


Exploring knowledge sharing in ERP implementation: an organizational culture framework. This is a multi-site case study of firms that have implemented enterprise resource planning (ERP) systems. It examines eight dimensions of culture and their impact on how ERP implementation teams are able to effectively share knowledge across diverse functions and perspectives during ERP implementation. Through synthesizing the data, we develop a cultural configuration that shows the dimensions of culture that best facilitate knowledge sharing in ERP implementation. The results also indicate ways that firms may overcome cultural barriers to knowledge sharing. A model is developed that demonstrates the link between the dimensions of culture and knowledge sharing during ERP implementation. Possible research questions on which future research can be based are also identified. D 2004 Elsevier B.V. All rights reserved.

Question: How can we share knowledge in ERP implementation?


A Combined Corner and Edge Detector. The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.

Question: What is a monocular image sequence?


Security in Infancy , Childhood , and Adulthood : A Move to the Level of Representation. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How to use JSTOR?


Building Program Vector Representations for Deep Learning. Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.

Question: How to build program vector representations?


"Applying TAM to E-Services Adoption: The Moderating Role of Perceived Risk". Consumer adoption of e-services is an important goal for many service providers, however little is known about how different consumer segments perceive and evaluate them for adoption. The Technology Acceptance Model (TAM) explains information systems evaluation and adoption, however the Internetdelivered e-services context presents additional variance that requires supplemental measures to be added to TAM. This research extends TAM to include a perceived usage risk main effect and also tested whether perceived risk moderated several of TAM's relationships. Results indicate that higher levels of perceived risk deflated ease of use's effect and inflated subjective norm's effect on perceived usefulness and adoption intention.

Question: How does perceived risk affect the adoption of e-services?


Differential effects of mindful breathing, progressive muscle relaxation, and loving-kindness meditation on decentering and negative reactions to repetitive thoughts.. Decentering has been proposed as a potential mechanism of mindfulness-based interventions but has received limited empirical examination to date in experimental studies comparing mindfulness meditation to active comparison conditions. In the present study, we compared the immediate effects of mindful breathing (MB) to two alternative stress-management techniques: progressive muscle relaxation (PMR) and loving-kindness meditation (LKM) to test whether decentering is unique to mindfulness meditation or common across approaches. Novice meditators (190 female undergraduates) were randomly assigned to complete one of three 15-min stress-management exercises (MB, PMR, or LKM) presented by audio recording. Immediately after the exercise, participants completed measures of decentering, frequency of repetitive thoughts during the exercise, and degree of negative reaction to thoughts. As predicted, participants in the MB condition reported greater decentering relative to the other two conditions. The association between frequency of repetitive thought and negative reactions to thoughts was relatively weaker in the MB condition than in the PMR and LKM conditions, in which these two variables were strongly and positively correlated. Consistent with the construct of decentering, the relative independence between these two variables in the MB condition suggests that mindful breathing may help to reduce reactivity to repetitive thoughts. Taken together, results help to provide further evidence of decentering as a potential mechanism that distinguishes mindfulness practice from other credible stress-management approaches.

Question: What is the difference between mindful breathing and other stress-management techniques?


Anonymizing sequential releases. An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.

Question: How to anonymize sequential releases?


Improved random redundant iterative HDPC decoding. An iterative algorithm for soft-input soft-output (SISO) decoding of classical algebraic cyclic block codes is presented below. Inspired by other approaches for high performance belief propagation (BP) decoding, this algorithm requires up to 10 times less computational complexity than other methods that achieve similar performance. By utilizing multiple BP decoders, and using random permutation taken from the permutation group of the code, this algorithm reaches near maximum likelihood performance. A computational complexity comparison of the proposed algorithm versus other methods is presented as well. This includes complexity versus performance analysis, allowing one to trade between the former and the latter, according to ones needs.

Question: What is the complexity of the algorithm?


A Simple Parallel Algorithm for the Maximal Independent Set Problem. Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial intelligence. One of the main contributions of this paper is the development of powerful and general techniques for converting Monte Carlo algorithms into deterministic algorithms. These techniques are used to convert the Monte Carlo algorithm for the MIS problem into a simple deterministic algorithm with the same parallel running time.

Question: What is a simple parallel algorithm for the maximal independent set problem?


Scheduling Multithreaded Computations by Work Stealing. This paper studies the problem of efficiently schedulling fully strict (i.e., well-structured) multithreaded computations on parallel computers. A popular and practical method of scheduling this kind of dynamic MIMD-style computation is "work stealing," in which processors needing work steal computational threads from other processors. In this paper, we give the first provably good work-stealing scheduler for multithreaded computations with dependencies. Specifically, our analysis shows that the expected time to execute a fully strict computation on <italic>P</italic> processors using our work-stealing scheduler is <italic>T</italic><subscrpt>1</subscrpt>/<italic>P</italic> + <italic>O</italic>(<italic>T</italic><subscrpt><inline-equation> <f> ∞ </f></inline-equation></subscrpt>, where  <italic>T</italic><subscrpt>1</subscrpt> is the minimum serial execution time of the multithreaded computation and (<italic>T</italic><subscrpt><inline-equation> <f> ∞</f></inline-equation></subscrpt> is the minimum execution time with an infinite number of processors. Moreover, the space required by the execution is at most <italic>S</italic><subscrpt>1</subscrpt><italic>P</italic>, where  <italic>S</italic><subscrpt>1</subscrpt> is the minimum serial space requirement. We also show that the expected total communication of the algorithm is at most <italic>O</italic>(<italic>PT</italic><subscrpt><inline-equation> <f> ∞</f> </inline-equation></subscrpt>( 1 +  <italic>n<subscrpt>d</subscrpt></italic>)<italic>S</italic><subscrpt>max</subscrpt>), where <italic>S</italic><subscrpt>max</subscrpt> is the size of the largest activation record of any thread and  <italic>n<subscrpt>d</subscrpt></italic> is the maximum number of times that any thread synchronizes with its parent. This communication bound justifies the folk wisdom that work-stealing schedulers are more communication efficient than their work-sharing counterparts. All three

Question: What is the size of the largest activation record of any thread?


Character-based movie summarization. A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scent analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding.

Question: What is the movie theory?


GENDER AND LEADERSHIP BEHAVIOR AMONG SENIOR PASTORS. Research findings have been equivocal as to the existence of gender difference in leadership across settings. However, some studies based on transformational leadership theory and employing the Multifactor Leadership Questionnaire to measure leadership behaviors have indicated a difference in the leadership styles of female and male leaders. This study sought to test whether there were gender differences in the use of transformational, transactional, and laissez-faire leadership behaviors by Senior Pastors in an Australian Christian Church. The study did not detect any significant gender differences in leadership behaviors. Gender and Leadership Behavior among Senior Pastors Gender and Leadership Leadership has been typically a male prerogative in most sectors of society, including the corporate, political, military, and church sectors (Eagly & Karau, 2002). However, over the last 30 years women have made steady progress in moving into leadership roles. In 1972, women held 17% of all management and professional positions in Fortune 500 Companies. By 2006, this number had grown to 50.3% (Hoyt, Simon, and Reid, 2009). Women typically tend to occupy lower and middle management ranks while men cluster around the most powerful positions at the top. Women managers still receive significantly less remuneration for their work, with female managers receiving 24 percent less pay than men performing the equivalent function (Haslam and Ryan, 2008). Nonetheless, despite continuing 138 Asian Journal of Pentecostal Studies 12:2 (2009) inequity, it is clear that women are gradually occupying an increasing number of management and leadership positions. Although women have increasingly gained access to supervisory and middle management positions, they remain quite rare as elite leaders. For example, in 2006 women represented 5.2% of top earners, 14.7% of board members, 7.9% of the highest earners, and less than 2% of CEOs in Fortun

Question: What is the percentage of women in leadership positions?


Fast Text Searching Allowing Errors. T h e string-matching problem is a very c o m m o n problem. We are searching for a string P = PtP2. . "Pro i n s i d e a la rge t ex t f i le T = t l t2. . . t . , b o t h sequences of characters from a f i n i t e character set Z. T h e characters may be English characters in a text file, DNA base pairs, lines of source code, angles between edges in polygons, machines or machine parts in a production schedule, music notes and tempo in a musical score, and so fo r th . We w a n t to f i n d a l l occurrences of P i n T; n a m e l y , we are searching for the set of starting posit ions F = {i[1 --i--n m + 1 s u c h t h a t titi+ l " " t i + m 1 = P } " T h e two most famous algorithms for this problem are t h e B o y e r M o o r e algorithm [3] and t h e K n u t h Morris Pratt algorithm [10]. There are many extensions to t h i s problem; for example, we may be looking for a set of patterns, a pattern w i t h "wi ld cards," or a regular expression. String-matching tools are included in every reasonable text editor, word processor, and many other applications.

Question: What is the most famous algorithm for string matching?


Machine Comprehension Based on Learning to Rank. Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest. However, this dataset is too simple and too small for learning true reasoning abilities. (Hermann et al., 2015) therefore release a large scale news article dataset and propose a deep LSTM reader system for machine comprehension. However, the training process is expensive. We therefore try feature-engineered approach with semantics on the new dataset to see how traditional machine learning technique and semantics can help with machine comprehension. Meanwhile, our proposed L2R reader system achieves good performance with efficiency and less training data.

Question: What is the training process of machine comprehension?


Prevalence of Vitamin D Insufficiency in an Adult Normal Population. The vitamin D status of a general adult urban population was estimated between November and April in 1569 subjects selected from 20 French cities grouped in nine geographical regions (between latitude 43° and 51° N). Major differences in 25-hydroxyvitamin D (25(OH)D) concentration were found between regions, the lowest values being seen in the North and the greatest in the South, with a significant 'sun' effect (r = 0.72; p = 0.03) and latitude effect (r = -0.79; p = 0.01). In this healthy adult population, 14% of subjects exhibited 25(OH)D values ≤ 30 nmol/l (12 ng/ml), which represents the lower limit (< 2 SD) for a normal adult population measured in winter with the same method (RIA Incstar). A significant negative correlation was found between serum intact parathyroid hormone (iPTH) and serum 25(OH)D values (p < 0.01). Serum iPTH held a stable plateau level at 36 pg/ml as long as serum 25(OH)D values were higher than 78 nmol/l (31 ng/ml), but increased when the serum 25(OH)D value fell below this. When the 25(OH)D concentration became equal to or lower than 11.3 nmol/l (4.6 ng/ml), the PTH values reached the upper limit of normal values (55 pg/ml) found in vitamin D replete subjects. These results showed that in French normal adults living in an urban environment with a lack of direct exposure to sunshine, diet failed to provide an adequate amount of vitamin D. It is important to pay attention to this rather high prevalence of vitamin D insufficiency in the general adult population and to discuss the clinical utility of winter supplementation with low doses of vitamin D.

Question: What is the prevalence of vitamin D insufficiency in an adult normal population?


Bioaccumulation of nutrients and metals in sediment, water, and phoomdi from Loktak Lake (Ramsar site), northeast India: phytoremediation options and risk assessment.. In order to determine the potential of phoomdi to accumulate nutrients and metals, 11 dominant species belonging to 10 different families, sediment, and water were analyzed for a period of 2 years from the largest freshwater wetland of north-east India, Loktak (Ramsar site). Results revealed nutrient (TN and TP) and metal (Fe, Mn, Zn, and Cu) compartmentalization in the order phoomdi > sediment > water. Iron concentrations in water (0.37 ± 0.697 to 0.57 ± 1.010 mg L(-1)) and sediments (81.8 ± 0.45 to 253.1 ± 0.51 mg kg(-1)) show high metal discharge into the wetland. Metal accumulation in phoomdi ranged up to 212.3 ± 0.46-9461.4 ± 1.09 mg kg(-1) for Fe; 85.9 ± 0.31-3565.1 ± 0.87 mg kg(-1) for Mn; 9.6 ± 0.41-85.39 ± 0.58 mg kg(-1) for Zn; and 0.31 ± 0.04-9.2 ± 0.04 mg kg(-1) for Cu, respectively. High bioaccumulation factors (BAF) for metals (S. cucullata, 5.8 × 10(4) Fe, 3.9 × 10(4) Mn, and 1.7 × 10(4) Cu, and O. javanica, 4.9 × 10(3) Zn) and nutrients (S. polyrhiza, 9.7 × 10(2) TN, and Z. latifolia, 7.9 × 10(4) TP) revealed good accumulation in phoomdi compared to the wetland water column and indicate their potential to maintain a safe environment of Loktak. Further, the paper analyzed the health hazard of metals via phoomdi wild edible consumption, with the results confirming potential risk. Thus, the paper showed the need of in-depth monitoring and ample management strategies to ensure nutritional safety conditions of locals from the metals.

Question: What is the health hazard of metals via phoomdi wild edible consumption?


Stakeholder engagement, social auditing and corporate sustainability. Purpose – To identify the applicability of social auditing as an approach of engaging stakeholders in assessing and reporting on corporate sustainability and its performance. Design/methodology/approach – Drawing upon the framework of AA1000 and the social auditing studies, this paper links stakeholder engagement, social auditing and corporate sustainability with a view to applying dialogue-based social auditing to address corporate sustainability. Findings – This paper identifies a "match" between corporate sustainability and social auditing, as both aim at improving the social, environmental and economic performance of an organisation, considering the well-being of a wider range of stakeholders and requiring the engagement of stakeholders in the process. This paper suggests that social auditing through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote co-operation amongst stakeholders and corporations. Research limitations/implications – This research requires further empirical research into the practicality of social auditing in addressing corporate sustainability and the determination of the limitations of dialogue-based social auditing. Practical implications – Social auditing has been identified as a useful mechanism of balancing differing interests among stakeholders and corporations in a democratic business society. The application of social auditing in developing and achieving corporate sustainability has apparently practical implications. Originality/value – This paper examines the applicability of dialogue-based social auditing in helping business to move towards sustainability. Social auditing as a process of assessing and reporting on corporate social and environmental performance through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote cooperation amongst stakeholders and corporatio

Question: What is the difference between stakeholder engagement and social auditing?


Application of Spreading Activation Techniques in Information Retrieval. This paper surveys the use of Spreading Activation techniques onSemantic Networks in Associative Information Retrieval. The majorSpreading Activation models are presented and their applications toIR is surveyed. A number of works in this area are criticallyanalyzed in order to study the relevance of Spreading Activation forassociative IR.

Question: What is the majorSpreading Activation models?


Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees. Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying classification with respect to specific fairness metrics, modeled the corresponding fair classification problem as constrained optimization problems, and developed tailored algorithms to solve them. Despite this, there still remain important metrics for which there are no fair classifiers with theoretical guarantees; primarily because the resulting optimization problem is non-convex. The main contribution of this paper is a meta-algorithm for classification that can take as input a general class of fairness constraints with respect to multiple non-disjoint and multi-valued sensitive attributes, and which comes with provable guarantees. In particular, our algorithm can handle non-convex "linear fractional" constraints (which includes fairness constraints such as predictive parity) for which no prior algorithm was known. Key to our results is an algorithm for a family of classification problems with convex constraints along with a reduction from classification problems with linear fractional constraints to this family. Empirically, we observe that our algorithm is fast, can achieve near-perfect fairness with respect to various fairness metrics, and the loss in accuracy due to the imposed fairness constraints is often small.

Question: What is a meta-algorithm?


FML-based feature similarity assessment agent for Japanese/Taiwanese language learning. In this paper, we propose a fuzzy markup language (FML)-based feature similarity assessment agent with machine-learning ability to evaluate easy-to-learn degree of the Japanese and Taiwanese words. The involved domain experts define knowledge base (KB) and rule base (RB) of the proposed agent. The KB and RB are stored in the constructed ontology, including features of pronunciation similarity, writing similarity, and culture similarity. Next, we calculate feature similarity in pronunciation, writing, and culture for each word pair between Japanese and Taiwanese. Finally, we infer the easy-to-learn degree for one Japanese word and its corresponding Taiwanese one. Additionally, a genetic learning is also adopted to tune the KB and RB of the intelligent agent. The experimental results show that after-learning results perform better than before-learning ones.

Question: What is the difference between the Japanese and Taiwanese languages?


A Study of Android Malware Detection Techniques and Machine Learning. Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine learning algorithms focused

Question: What is the difference between a signature based system and a machine learning based system?


DBpedia: A Multilingual Cross-domain Knowledge Base. The DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world knowledge. The knowledge base contains textual descriptions (titles and abstracts) of concepts in up to 97 languages. It also contains structured knowledge that has been extracted from the infobox systems of Wikipedias in 15 different languages and is mapped onto a single consistent ontology by a community effort. The knowledge base can be queried using a structured query language and all its data sets are freely available for download. In this paper, we describe the general DBpedia knowledge base and extended data sets that specifically aim at supporting computational linguistics tasks. These task include Entity Linking, Word Sense Disambiguation, Question Answering, Slot Filling and Relationship Extraction. These use cases are outlined, pointing at added value that the structured data of DBpedia provides.

Question: What is the DBpedia project?


Active Damping in DC/DC Power Electronic Converters: A Novel Method to Overcome the Problems of Constant Power Loads. Multi-converter power electronic systems exist in land, sea, air, and space vehicles. In these systems, load converters exhibit constant power load (CPL) behavior for the feeder converters and tend to destabilize the system. In this paper, the implementation of novel active-damping techniques on dc/dc converters has been shown. Moreover, the proposed active-damping method is used to overcome the negative impedance instability problem caused by the CPLs. The effectiveness of the new proposed approach has been verified by PSpice simulations and experimental results.

Question: How to overcome the negative impedance instability problem caused by the constant power load?


GENERATIVE ADVERSARIAL NETWORK BASED ACOUSTIC SCENE TRAINING SET AUGMENTATION AND SELECTION USING SVM HYPERPLANE. Although it is typically expected that using a large amount of labeled training data would lead to improve performance in deep learning, it is generally difficult to obtain such DataBase (DB). In competitions such as the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge Task 1, participants are constrained to use a relatively small DB as a rule, which is similar to the aforementioned issue. To improve Acoustic Scene Classification (ASC) performance without employing additional DB, this paper proposes to use Generative Adversarial Networks (GAN) based method for generating additional training DB. Since it is not clear whether every sample generated by GAN would have equal impact in classification performance, this paper proposes to use Support Vector Machine (SVM) hyper plane for each class as reference for selecting samples, which have class discriminative information. Based on the crossvalidated experiments on development DB, the usage of the generated features could improve ASC performance.

Question: How to improve the performance of deep learning?


Help your mobile applications with fog computing. Cloud computing has paved a way for resource-constrained mobile devices to speed up their computing tasks and to expand their storage capacity. However, cloud computing is not necessary a panacea for all mobile applications. The high network latency to cloud data centers may not be ideal for delay-sensitive applications while storing everything on public clouds risks users' security and privacy. In this paper, we discuss two preliminary ideas, one for mobile application offloading and the other for mobile storage expansion, by leveraging the edge intelligence offered by fog computing to help mobile applications. Preliminary experiments conducted based on implemented prototypes show that fog computing can provide an effective and sometimes better alternative to help mobile applications.

Question: What is fog computing?


Otologic and Audiology Concerns of Microtia Repair.. Microtia is a congenital auricular deformity that commonly presents with associated congenital aural atresia. The most acute concern in these patients is concomitant hearing loss at birth. A team-based approach by plastic surgeons and otologists is necessary to address both the otologic and audiologic concerns of microtia and atresia. Hearing rehabilitation is imperative; yet it should not compromise the aesthetic components of reconstruction and vice versa. Here, the authors propose a framework to evaluate and manage patients with microtia and atresia with the goal of optimizing functional and cosmetic outcomes.

Question: What is the most common congenital auricular deformity?


Geometric Correspondence Network for Camera Motion Estimation. In this paper, we propose a new learning scheme for generating geometric correspondences to be used for visual odometry. A convolutional neural network (CNN) combined with a recurrent neural network (RNN) are trained together to detect the location of keypoints as well as to generate corresponding descriptors in one unified structure. The network is optimized by warping points from source frame to reference frame, with a rigid body transform. Essentially, learning from warping. The overall training is focused on movements of the camera rather than movements within the image, which leads to better consistency in the matching and ultimately better motion estimation. Experimental results show that the proposed method achieves better results than both related deep learning and hand crafted methods. Furthermore, as a demonstration of the promise of our method we use a naive SLAM implementation based on these keypoints and get a performance on par with ORB-SLAM.

Question: How does the proposed method work?


Automatic fusion and classification using random forests and features extracted with deep learning. Fusion of different sensor modalities has proven very effective in numerous remote sensing applications. However, in order to benefit from fusion, advanced feature extraction mechanisms that rely on domain expertise are typically required. In this paper we present an automated feature extraction scheme based on deep learning. The feature extraction is unsupervised and hierarchical. Furthermore, computational efficiency (often a challenge for deep learning methods) is a primary goal in order to make certain that the method can be applied in large remote sensing datasets. Promising classification results show the applicability of the approach for both reducing the gap between naive feature extraction and methods relying on domain expertise, as well as further improving the performance of the latter in two challenging datasets.

Question: What is the difference between naive feature extraction and methods relying on domain expertise?


Driver Gaze Region Estimation Without Using Eye Movement. Postmaster: Send undelivered copies and address changes to IEEE Intelligent Systems, Membership Processing Dept., IEEE Service Center, 445 Hoes Lane, Piscataway, NJ 08854-4141. Periodicals Postage Paid at New York, NY, and at additional mailing offices. Canadian GST #125634188. Canada Post Publications Mail Agreement Number 40013885. Return undeliverable Canadian addresses to 4960-2 Walker Rd., Windsor, ON N9A 6J3. Printed in the USA. Reuse Rights and Reprint Permissions: Educational or personal use of this material is permitted without fee, provided such use 1) is not made for profit, 2) includes this notice and a full citation to the original work on the first page of the copy, and 3) does not imply IEEE endorsement of any third-party products or services. Authors and their companies are permitted to post the accepted version of IEEE-copyrighted material on their own Web servers without permission, provided that the IEEE copyright notice and a full citation to the original work appear on the first screen of the posted copy. An accepted manuscript is a version that has been revised by the author to incorporate review suggestions but not the published version with copyediting, proofreading, and formatting added by IEEE. For more information, please go to http://www. ieee.org/publications_standards/publications/rights/ paperversionpolicy.html. Permission to reprint/republish this material for commercial, advertising, or promotional purposes or for creating new collective works for resale or redistribution must be obtained from IEEE by writing to the IEEE Intellectual Property Rights Office, 445 Hoes Lane, Piscataway, NJ 08854-4141 or pubs-permissions@ ieee.org. Copyright © 2016 IEEE. All rights reserved. Abstracting and Library Use: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy for private use of patrons, provided the per-copy fee indicated in the code at the bottom o

Question: How to get a copy of the paper?


Emerging tobacco hazards in China: 1. Retrospective proportional mortality study of one million deaths.. OBJECTIVE To assess the hazards at an early phase of the growing epidemic of deaths from tobacco in China.   DESIGN Smoking habits before 1980 (obtained from family or other informants) of 0.7 million adults who had died of neoplastic, respiratory, or vascular causes were compared with those of a reference group of 0.2 million who had died of other causes.   SETTING 24 urban and 74 rural areas of China.   SUBJECTS One million people who had died during 1986-8 and whose families could be interviewed.   MAIN OUTCOME MEASURES Tobacco attributable mortality in middle or old age from neoplastic, respiratory, or vascular disease.   RESULTS Among male smokers aged 35-69 there was a 51% (SE 2) excess of neoplastic deaths, a 31% (2) excess of respiratory deaths, and a 15% (2) excess of vascular deaths. All three excesses were significant (P<0.0001). Among male smokers aged >/70 there was a 39% (3) excess of neoplastic deaths, a 54% (2) excess of respiratory deaths, and a 6% (2) excess of vascular deaths. Fewer women smoked, but those who did had tobacco attributable risks of lung cancer and respiratory disease about the same as men. For both sexes, the lung cancer rates at ages 35-69 were about three times as great in smokers as in non-smokers, but because the rates among non-smokers in different parts of China varied widely the absolute excesses of lung cancer in smokers also varied. Of all deaths attributed to tobacco, 45% were due to chronic obstructive pulmonary disease and 15% to lung cancer; oesophageal cancer, stomach cancer, liver cancer, tuberculosis, stroke, and ischaemic heart disease each caused 5-8%. Tobacco caused about 0.6 million Chinese deaths in 1990 (0.5 million men). This will rise to 0.8 million in 2000 (0.4 million at ages 35-69) or to more if the tobacco attributed fractions increase.   CONCLUSIONS At current age specific death rates in smokers and

Question: What is the difference between a retrospective and a prospective study?


Personality and Performance at the Beginning of the New Millennium : What Do We Know and Where Do We Go Next ?. As we begin the new millennium, it is an appropriate time to examine what we have learned about personality-performance relationships over the past century and to embark on new directions for research. In this study we quantitatively summarize the results of 15 prior meta-analytic studies that have investigated the relationship between the Five Factor Model (FFM) personality traits and job performance. Results support the previous findings that conscientiousness is a valid predictor across performance measures in all occupations studied. Emotional stability was also found to be a generalizable predictor when overall work performance was the criterion, but its relationship to specific performance criteria and occupations was less consistent than was conscientiousness. Though the other three Big Five traits (extraversion, openness and agreeableness) did not predict overall work performance, they did predict success in specific occupations or relate to specific criteria. The studies upon which these results are based comprise most of the research that has been conducted on this topic in the past century. Consequently, we call for a moratorium on meta-analytic studies of the type reviewed in our study and recommend that researchers embark on a new research agenda designed to further our understanding of personalityperformance linkages.

Question: What is the relationship between personality and performance?


TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility. Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules. This work introduces an algebraic topology-based method, called element-specific persistent homology (ESPH), as a new representation of small molecules that is entirely different from conventional chemical and/or physical representations. ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants. Such topological representation is systematical, comprehensive, and scalable with respect to molecular size and composition variations. However, it cannot be literally translated into a physical interpretation. Fortunately, it is readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between solubility and partition coefficient, a uniform ESPH representation is developed for both properties, which facilitates multi-task deep neural networks for their simultaneous predictions. This strategy leads to a more accurate prediction of relatively small datasets. A total of six datasets is considered in this work to validate the proposed topological and multitask deep learning approaches. It is demonstrated that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at http://weilab.math.msu.edu/TopP-S/. © 2018 Wiley Periodicals, Inc.

Question: What is the difference between aqueous solubility and partition coefficient?


Simultaneous estimation of food categories and calories with multi-task CNN. In this paper, we propose simultaneous estimation of food categories and calories for food photos. Since there exists strong correlation between food categories and calories in general, we expect that simultaneous training of both brings performance boosting compared to independent single training. To this end, we use a multitask CNN. In the experiments, we collected calorie-annotated recipe data from the online cooking recipe sites, and trained multi-task and single-task CNNs. As results, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs.

Question: How to estimate calories of food?


Frontomaxillary facial angle in trisomy 21 fetuses at 16-24 weeks of gestation.. OBJECTIVES To establish a normal range for the frontomaxillary facial (FMF) angle by three-dimensional (3D) ultrasound imaging and to examine the FMF angle in trisomy 21 fetuses at 16-24 weeks of gestation.   METHODS We measured the FMF angle using 3D volumes of the fetal profile obtained with the transducer parallel to the long axis of the nose and at 45 degrees to the palate, which had been acquired from 150 normal fetuses and 23 fetuses with trisomy 21.   RESULTS In the normal group there was no significant association between the FMF angle and gestational age; the mean FMF angle was 83.9 degrees (range, 76.9-90.2 degrees ) and the 95(th) centile was 88.5 degrees . In 15 (65.2%) of the fetuses with trisomy 21 the FMF angle was greater than 88.5 degrees . Repeatability studies demonstrated that in 95% of cases the difference between two measurements of FMF angle by the same operator and different operators was less than 5 degrees .   CONCLUSIONS In the majority of second-trimester fetuses with trisomy 21 the FMF angle is increased.

Question: What is the normal range for the frontomaxillary facial angle?


Mobile Forensic Data Analysis: Suspicious Pattern Detection in Mobile Evidence. Culprits' identification by the means of suspicious pattern detection techniques from mobile device data is one of the most important aims of the mobile forensic data analysis. When criminal activities are related to entirely automated procedures such as malware propagation, predicting the corresponding behavior is a rather achievable task. However, when human behavior is involved, such as in cases of traditional crimes, prediction and detection become more compelling. This paper introduces a combined criminal profiling and suspicious pattern detection methodology for two criminal activities with moderate to the heavy involvement of mobile devices, cyberbullying and low-level drug dealing. Neural and Neurofuzzy techniques are applied on a hybrid original and simulated dataset. The respective performance results are measured and presented, the optimal technique is selected, and the scenarios are re-run on an actual dataset for additional testing and verification.

Question: What is the optimal technique?


Automatic Driving on Ill-defined Roads: An Adaptive, Shape-constrained, Color-based Method. Autonomous following of ill-defined roads is an important part of visual navigation systems. This paper presents an adaptive method that uses a statistical model of the colour of the road surface within a trapezoidal shape that approximately corresponds to the projection of the road on the image plane. The method does not perform an explicit segmentation of the images but instead expands the shape sideways until the match between shape and road worsens, simultaneously computing the colour statistics. Results show that the method is capable of reactively following roads, at driving speeds typical of the robots used, in a variety of situations while coping with variable conditions of the road such as surface type, puddles and shadows. We extensively evaluate the proposed method using a large number of datasets with ground truth (that will be made public on our server once the paper is published). We moreover evaluate many colour spaces in the context of road following and find that the colour spaces that separate luminance from colour information perform best, especially if the luminance information is discarded.

Question: What is the colour space that performs best for road following?


Learning Visual Similarity Measures for Comparing Never Seen Objects. In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled "same" or "different". This is far less informative than the commonly used individual image labels (e.g., "car model X"), but it is cheaper to obtain. The proposed algorithm learns the characteristic differences between local descriptors sampled from pairs of "same" and "different" images. These differences are vector quantized by an ensemble of extremely randomized binary trees, and the similarity measure is computed from the quantized differences. The extremely randomized trees are fast to learn, robust due to the redundant information they carry and they have been proved to be very good clusterers. Furthermore, the trees efficiently combine different feature types (SIFT and geometry). We evaluate our innovative similarity measure on four very different datasets and consistently outperform the state-of-the-art competitive approaches.

Question: How to learn a similarity measure for comparing never seen objects?


Recommending citations for academic papers. We approach the problem of academic literature search by considering an unpublished manuscript as a query to a search system. We use the text of previous literature as well as the citation graph that connects it to find relevant related material. We evaluate our technique with manual and automatic evaluation methods, and find an order of magnitude improvement in mean average precision as compared to a text similarity baseline.

Question: How to find relevant citations for academic papers?


Model-predictive target defense by team of unmanned surface vehicles operating in uncertain environments. In this paper, we present a heuristic planning approach for guarding a valuable asset by a team of autonomous unmanned surface vehicles (USVs) operating in a continuous state-action space. The team's objective is to maximize the amount of time it takes an intruder boat to reach the asset. The team must cooperatively deal with uncertainty about which boats are actual intruders, employ active blocking to slow down intruders' movement towards the asset, and intelligently distribute themselves around the target to optimize future guarding opportunities. Our planner incorporates a market-based algorithm for allocating tasks to individual USVs by forward-simulating the mission and assigning estimated utilities to candidate task-allocation plans. The planner can be automatically adapted to a specific mission by optimizing the behaviors used to fulfil individual tasks. We present detailed simulation results that demonstrate the effectiveness of our approach.

Question: How do you model a mission?


Privacy-Aware Tag Recommendation for Image Sharing. Image tags are very important for indexing, sharing, searching, and surfacing images with private content that needs protection. As the tags are at the sole discretion of users, they tend to be noisy and incomplete. In this paper, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images' original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image obtained from a large collection. Experimental results show that privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction. Crowd-sourcing predicted tags exhibit the quality of the recommended tags.

Question: What is the privacy-aware approach to automatic image tagging?


FACTORS INFLUENCING CONSUMERS ' ATTITUDE TOWARDS E-COMMERCE PURCHASES THROUGH ONLINE SHOPPING. Online shopping is the process of buying goods and services from merchants who sell on the internet. Shoppers can visit web stores from the comfort of their homes and shop as they sit in front of the computer. The main purpose of this study is to determine the factors influencing consumers' attitude towards e-commerce purchases through online shopping. The study also investigate how socio-demographic (age, income and occupation), pattern of online buying (types of goods, e-commerce experience and hours use on internet) and purchase perception (product perception, customers' service and consumers' risk) affect consumers' attitude towards online shopping. Convenience sampling method was conducted in this study and the sample comparison of 100 respondents in Taman Tawas Permai, Ipoh. Data were collected via self-administered questionnaire which contains 15 questions in Part A (respondents' background and their pattern of using internet and online buying), 34 questions in Part B (attitude towards online purchase) and 36 questions in Part C (purchase perception towards online shopping). One-way ANOVA were used to assess the differences between independent variable such as age, income, occupation and pattern of online buying (type of goods) and dependant variable such as attitude towards online shopping. The findings revealed that there is no significant difference in attitude towards online shopping among age group (F = 1.020, p < 0.05) but there is a significant difference in attitude towards online shopping among income group (F = 0.556, p > 0.05). The research finding also showed that there is no significant difference in attitude towards online shopping among occupation group (F = 1.607, p < 0.05) and types of goods group (F = 1.384, p < 0.05). Pearson's correlation were used to assess the relationship between independent variable such as e-commerce experience, hours spent

Question: What is the difference between attitude towards online shopping and attitude towards online purchase?


Towards deep learning with segregated dendrites. Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

Question: What is the difference between deep learning and deep learning with segregated dendrites?


Slice Sampling for Probabilistic Programming. We introduce the first, general purpose, slice sampling inference engine for probabilistic programs. This engine is released as part of StocPy, a new Turing-Complete probabilistic programming language, available as a Python library. We present a transdimensional generalisation of slice sampling which is necessary for the inference engine to work on traces with different numbers of random variables. We show that StocPy compares favourably to other PPLs in terms of flexibility and usability, and that slice sampling can outperform previously introduced inference methods. Our experiments include a logistic regression, HMM, and Bayesian Neural Net.

Question: What is a Turing-Complete probabilistic programming language?


Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.. Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.

Question: How accurate are the measurements?


An IoT-Oriented Data Storage Framework in Cloud Computing Platform. The Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of Radio Frequency IDentification (RFID) and wireless sensors devices. Benefiting from RFID and sensor network technology, common physical objects can be connected, and are able to be monitored and managed by a single system. Such a network brings a series of challenges for data storage and processing in a cloud platform. IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. In order to address these potential problems, this paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository, which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework's effectiveness.

Question: What is the data storage framework?


Curved double-layer strip folded dipole antenna for WBAN applications. This paper presents an optimal design of strip folded dipole antenna with a stacking technique to stabilize the resonance frequency. The designed double-layer strip folded dipole antenna worked well with the cylindrically curved surface on the human body. While being bent, the antenna will change the extended length in each layer within the stacking flexible copper-clad laminate. In this case, the properties of the antenna will be automatically adjusted. However, the extended length can occur at the contact point in each layer. So, we can choose between two points and four points by selecting different types of the designs. This reason, the performance of the extended length can be added by two and four when being compared to the previous multilayer design and the total layer of the new antenna was only two. The extended length of the antenna resulted in making antenna resonance frequency improvement become more stable. The contribution of proposed antenna was used only two layers of flexible copper-clad laminate that reduces a number of antenna's layers with a folded dipole antenna, which causes the designed less complicated, and the flexibility is advantageous for WBAN applications. The double-layer strip folded dipole antenna is designed for 2.45GHz WBAN application.

Question: What is a folded dipole antenna?


Platforms in healthcare innovation ecosystems: The lens of an innovation intermediary. Healthcare innovation has made progressive strides. Innovative solutions now tend to incorporate device integration, data collection and data analysis linked across a diverse range of actors building platform-centric healthcare ecosystems. The interconnectedness and inter-disciplinarity of the ecosystems bring with it a number of vital issues around how to strategically manage such a complex system. This paper highlights the importance of innovation intermediaries particularly in a platform-centric ecosystem such as the healthcare industry. It serves as a reminder of why it is important for healthcare technologists to consider proactive ways to contribute to the innovation ecosystem by creating devices with the platform perspective in mind.

Question: What is a platform-centric healthcare ecosystem?


Analysis of Software Aging in a Web Server. Several recent studies have reported & examined the phenomenon that long-running software systems show an increasing failure rate and/or a progressive degradation of their performance. Causes of this phenomenon, which has been referred to as "software aging", are the accumulation of internal error conditions, and the depletion of operating system resources. A proactive technique called "software rejuvenation" has been proposed as a way to counteract software aging. It involves occasionally terminating the software application, cleaning its internal state and/or its environment, and then restarting it. Due to the costs incurred by software rejuvenation, an important question is when to schedule this action. While periodic rejuvenation at constant time intervals is straightforward to implement, it may not yield the best results. The rate at which software ages is usually not constant, but it depends on the time-varying system workload. Software rejuvenation should therefore be planned & initiated in the face of the actual system behavior. This requires the measurement, analysis, and prediction of system resource usage. In this paper, we study the development of resource usage in a web server while subjecting it to an artificial workload. We first collect data on several system resource usage & activity parameters. Non-parametric statistical methods are then applied toward detecting & estimating trends in the data sets. Finally, we fit time series models to the data collected. Unlike the models used previously in the research on software aging, these time series models allow for seasonal patterns, and we show how the exploitation of the seasonal variation can help in adequately predicting the future resource usage. Based on the models employed here, proactive management techniques like software rejuvenation triggered by actual measurements can be built

Question: How to predict the future resource usage of a web server?


Single motor actuated peristaltic wave generator for a soft bodied worm robot. This paper presents the design and development of a single motor actuated peristaltic worm robot with three segments using a bio-inspired method of locomotion with one actuator that achieves optimised worm like peristaltic motion. Each segment consists of two solid circular disks that have a tendon connected through to the drive mechanism using a Bowden cable and a soft rubber skin that deforms under the compression of the tendon. Our hypothesis that a tuned peristaltic waveform can achieve improved performance of locomotion distance and clamping strength is proven using an initial test platform capable of demonstrating varying waveform types with multiple actuators. Three experiments were undertaken: (i) moving along a flat surface, (ii) moving through a confined tunnel (iii) moving through a confined tunnel whilst pulling a payload. Results from these experiments have identified the optimal parameters for a smart gearbox capable of achieving the same optimal peristaltic waveform signal as the more complex test platform but with only one actuator driving all three worm segments. Unlike other examples of peristaltic worm robots, this example uses a control method embedded within the mechanics of the design removing excessive number of actuators which contributes to miniaturisation, reduces power consumption and simplifies the overall design.

Question: What is a peristaltic waveform?


Schedulability Analysis for Tasks with Static and Dynamic Offsets. In this paper we present an extension to current schedulability analysis techniques for periodic tasks with offsets, scheduled under a preemptive fixed priority scheduler. Previous techniques allowed only static offsets restricted to being smaller than the task periods. With the extension presented in this paper, we eliminate this restriction and we allow both static and dynamic offsets. The most significant application of this extension is in the analysis of multiprocessor and distributed systems. We show that we can achieve a significant increase of the maximum schedulable utilization by using the new technique, as opposed to using previously known worst-case analysis techniques for distributed systems. ___________________ This work has been supported in part by the Comisión Interministerial de Ciencia y Tecnología of the Spanish Government, under grant number TAP97-892

Question: What is the maximum schedulable utilization?


Context-Awareness for Mobile Sensing: A Survey and Future Directions. The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.

Question: What is the context-aware mobile sensing?


What characterizes a shadow boundary under the sun and sky?. Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets from Lalonde et al. and Zhu et al., our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning.

Question: What is a shadow boundary under the sun and sky?


Keyword extraction for social snippets. Today, a huge amount of text is being generated for social purposes on social networking services on the Web. Unlike traditional documents, such text is usually extremely short and tends to be informal. Analysis of such text benefit many applications such as advertising, search, and content filtering. In this work, we study one traditional text mining task on such new form of text, that is extraction of meaningful keywords. We propose several intuitive yet useful features and experiment with various classification models. Evaluation is conducted on Facebook data. Performances of various features and models are reported and compared.

Question: What is the purpose of this work?


Idea Generation Techniques among Creative Professionals. The creative process has been a key topic research over the past century, but it wasn't until the last decade that creativity became a hot topic of research in the HCI. It is an important commodity to businesses and individuals alike spawning numerous research studies in business, psychology and design. However, it wasn't until recently that researchers became interested in developing technologies to support creative behavior. This article outlines the role of creativity in design from the designer's perspective, provides a model for the creative process and provides a foundation and direction for future creativity support research by identifying nineteen idea generation techniques utilized by creative professionals.

Question: What is the creative process?


Peppermint oil for treatment of irritable bowel syndrome.. 22 AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER 2 | JANUARY 15, 2016 Peppermint oil for treatment of irritable bowel syndrome Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder that affects 5–15% of people worldwide. Typically, patients with IBS experience abdominal pain or discomfort and constipation or diarrhea with other gastrointestinal symptoms, such as abdominal distention and bloating; these symptoms reduce quality of life and can be difficult to control with currently available prescription agents. For these reasons, patients are often compelled to try nonprescription therapies, including natural products. In studies conducted in Australia and the United Kingdom, about 20–50% of patients with IBS reported using complementary and alternative medicines. Thus, it is important that healthcare professionals are knowledgeable of these therapies, including dietary supplements such as peppermint oil. Historically, the ingestion of peppermint oil has been associated with effects on the gastrointestinal tract. It is a classic essential oil known to have carminative properties (i.e., it is a naturally occurring remedy thought to help decrease bloating and gas by allowing passage of flatus) and may even be beneficial as an antiemetic. Peppermint oil comes from a perennial herb (Mentha × piperita), a plant found across North America and Europe. Mentha × piperita is a sterile hybrid of two herbs, spearmint (Menthaspicata) and water mint (Menthaaquatica). The main constituents of peppermint oil include menthol (35–55%), menthone (20–31%), menthyl acetate (3–10%), isomenthone, 1,8-cineole, limonene, b-myrcene, and carvone. Peppermint oil may be obtained through steam distillation of flowering parts that grow aboveground. It is a volatile oil, with menthol accounting for a majority of its potency. Due to the various constituents of peppermint oil, it has a variety of uses, including topical applicat

Question: What is the difference between menthol and menthone?


An FPGA-based processor for training convolutional neural networks. Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, training a CNN model is computation-intensive and time-consuming. Hence training is mainly processed on large clusters of high-performance processors like server CPUs and GPUs. In this paper, we propose an FPGA-based processor design to accelerate the training process of CNNs. We first analyze the operations in all types of CNN layers in the training process. A uniform computation engine design is proposed to efficiently carry out all kinds of operations based on the analysis. Then a scalable accelerator framework is presented that exploits the parallelism further by unrolling the loops in two levels. The proposed accelerator design is demonstrated by implementing a processor on the Xilinx ZU19EG FPGA working at 200 MHz. The evaluation results on a group of CNN models show that our processor is 5.7 to 10.7-fold faster than the software implementations on the Intel Core i5-4440 CPU(@3.10GHz).

Question: What is a convolutional neural network?


Routing in multi-radio, multi-hop wireless mesh networks. We present a new metric for routing in multi-radio, multi-hop wireless networks. We focus on wireless networks with stationary nodes, such as community wireless networks.The goal of the metric is to choose a high-throughput path between a source and a destination. Our metric assigns weights to individual links based on the Expected Transmission Time (ETT) of a packet over the link. The ETT is a function of the loss rate and the bandwidth of the link. The individual link weights are combined into a path metric called Weighted Cumulative ETT (WCETT) that explicitly accounts for the interference among links that use the same channel. The WCETT metric is incorporated into a routing protocol that we call Multi-Radio Link-Quality Source Routing.We studied the performance of our metric by implementing it in a wireless testbed consisting of 23 nodes, each equipped with two 802.11 wireless cards. We find that in a multi-radio environment, our metric significantly outperforms previously-proposed routing metrics by making judicious use of the second radio.

Question: What is the difference between a wireless mesh network and a wireless ad hoc network?


Material property of on-metal magnetic sheet attached on NFC/HF-RFID antenna and research of its proper pattern and size on. When a payment system with NFC/HF-RFID is installed in smart-phone, their communication with 13.56MHz is blocked by such metal as battery, coil and PCB. To solve this problem, magnetic sheet with high permeability is set between NFC/HF-RFID antenna and near-by metal. So far thin compound magnetic sheet or thin sintered ferrite has been used. In order to make thickness of these materials thinner, we propose amorphous magnetic sheet. Besides this amorphous magnetic sheet causes to increase the transmission effect.

Question: What is the material property of on-metal magnetic sheet attached on NFC/HF-RFID antenna and research of its proper pattern and size on?


Orthogonal AMP. Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for linear system models. When the system transform matrix has independent identically distributed (IID) Gaussian entries, the performance of AMP can be asymptotically characterized by a simple scalar recursion called state evolution (SE). However, SE may become unreliable for other matrix ensembles, especially for ill-conditioned ones. This imposes limits on the applications of AMP. In this paper, we propose an orthogonal AMP (OAMP) algorithm based on de-correlated linear estimation (LE) and divergence-free non-linear estimation (NLE). The Onsager term in standard AMP vanishes as a result of the divergence-free constraint on NLE. We develop an SE procedure for OAMP and show numerically that the SE for OAMP is accurate for general unitarily-invariant matrices, including IID Gaussian matrices and partial orthogonal matrices. We further derive optimized options for OAMP and show that the corresponding SE fixed point coincides with the optimal performance obtained via the replica method. Our numerical results demonstrate that OAMP can be advantageous over AMP, especially for ill-conditioned matrices.

Question: What is the difference between AMP and OAMP?


A weighted additive fuzzy programming approach for multi-criteria supplier selection. In supply chain management, to build strategic and strong relationships, firms should select best suppliers by applying appropriate method and selection criteria. In this paper, to handle ambiguity and fuzziness in supplier selection problem effectively, a new weighted additive fuzzy programming approach is developed. Firstly, linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the factors. By applying the distances of each factor between Fuzzy Positive Ideal Rating and Fuzzy Negative Ideal Rating, weights are obtained. Then applying suppliers' constraints, goals and weights of the factors, a fuzzy multi-objective linear model is developed to overcome the selection problem and assign optimum order quantities to each supplier. The proposed model is explained by a numerical example. 2010 Elsevier Ltd. All rights reserved.

Question: What is the difference between a fuzzy and a fuzzy programming approach?


Fast and Concurrent RDF Queries with RDMA-Based Distributed Graph Exploration. Many public knowledge bases are represented and stored as RDF graphs, where users can issue structured queries on such graphs using SPARQL. With massive queries over large and constantly growing RDF data, it is imperative that an RDF graph store should provide low latency and high throughput for concurrent query processing. However, prior systems still experience high perquery latency over large datasets and most prior designs have poor resource utilization such that each query is processed in sequence. We present Wukong, a distributed graph-based RDF store that leverages RDMA-based graph exploration to provide highly concurrent and low-latency queries over large data sets. Wukong is novel in three ways. First, Wukong provides an RDMA-friendly distributed key/value store that provides differentiated encoding and fine-grained partitioning of graph data to reduce RDMA transfers. Second, Wukong leverages full-history pruning to avoid the cost of expensive final join operations, based on the observation that the cost of one-sided RDMA operations is largely oblivious to the payload size to a certain extent. Third, countering conventional wisdom of preferring migration of execution over data, Wukong seamlessly combines data migration for low latency and execution distribution for high throughput by leveraging the low latency and high throughput of onesided RDMA operations, and proposes a worker-obliger model for efficient load balancing. Evaluation on a 6-node RDMA-capable cluster shows that Wukong significantly outperforms state-of-the-art systems like TriAD and Trinity.RDF for both latency and throughput, usually at the scale of orders of magnitude. Short for Sun Wukong, who is known as the Monkey King and is a main character in the Chinese classical novel "Journey to the West". Since Wukong is known for his extremely fast speed (21,675 kilometers in one somersault) and the ability to fork hi

Question: What is the name of the monkey king?


A Fast Approach for Semantic Similar Short Texts Retrieval. Retrieving semantic similar short texts is a crucial issue to many applications, e.g., web search, ads matching, questionanswer system, and so forth. Most of the traditional methods concentrate on how to improve the precision of the similarity measurement, while current real applications need to efficiently explore the top similar short texts semantically related to the query one. We address the efficiency issue in this paper by investigating the similarity strategies and incorporating them into the FAST framework (efficient FrAmework for semantic similar Short Texts retrieval). We conduct comprehensive performance evaluation on real-life data which shows that our proposed method outperforms the state-ofthe-art techniques.

Question: What is the fast approach for semantic similar short texts retrieval?


Automotive hardware development according to ISO 26262. This paper briefly explains ISO 26262, the new automotive standard for functional safety. The standard is for electrical/electronic systems mounted on series of passenger cars. In the standard, Automotive Safety Integrity Level (ASIL) is classified into four levels according to the required safety level. Each of the ASIL has its own process and steps for item development. This paper focuses on the item development at hardware level, especially for ASIL C and D. The hardware development procedure includes the calculation of single point metric and latent fault metric. Furthermore, we present overall calculation steps of controlling random hardware failure.

Question: What is the difference between ASIL C and D?


A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss. We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph. In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated. Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions. By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-theart ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.

Question: What is the unified model for extractive and abstractive summarization?


Is it possible to cure Internet addiction with the Internet?. Significant technological advancements over the last two decades have led to enhanced accessibility to computing devices and the Internet. Our society is experiencing an ever-growing integration of the Internet into everyday lives, and this has transformed the way we obtain and exchange information, communicate and interact with one another as well as conduct business. However, the term 'Internet addiction' (IA) has emerged from problematic and excessive Internet usage which leads to the development of addictive cyber-behaviours, causing health and social problems. The most commonly used intervention treatments such as motivational interviewing, cognitive-behavioural therapy, and retreat or inpatient care mix a variety of psychotherapy theories to treat such addictive behaviour and try to address underlying psychosocial issues that are often coexistent with IA, but the efficacy of these approaches is not yet proved. The aim of this paper is to address the question of whether it is possible to cure IA with the Internet. After detailing the current state-of-the-art including various IA definitions, risk factors, assessment methods and IA treatments, we outline the main research challenges that need to be solved. Moreover, we propose an Internet-based IA Recovery Framework (IARF) which uses AI to closely observe, visualize and analyse patient's Internet usage behaviour for possible staged intervention. The proposal to use smart Internet-based systems to control IA can be expected to be controversial. This paper is intended to stimulate further discussion and research in IA recovery through Internet-based frameworks.

Question: Is it possible to cure internet addiction with the internet?


Videogame Addiction and its Treatment. For many, the concept of videogame addiction seems far-fetched, particularly if their concepts and definitions of addiction involve the taking of drugs. This paper overviews the small, but growing area of videogame addiction and then examines the treatment options available for those affected by excessive videogame playing. An overview of the available empirical literature appears to indicate that adverse effects are likely to affect only a relatively small subgroup of players and that frequent players are the most at-risk for developing problems. Worldwide, there are relatively few practitioners that specialise in the treatment of videogame addiction and this may be because there are so few players who are genuinely addicted to playing videogames. However, the Internet may be facilitating excessive online game playing as evidenced by the increasing number of specialist addiction treatment clinics for online videogame addiction. This paper overviews the various approaches that have been used as an intervention in treating videogame addicts, including online support groups, 12-step programmes, behavioural and cognitive-behavioural therapies, and motivational interviewing.

Question: What is the definition of addiction?