Query: What to believe: Bayesian methods for data analysis

Relevant passages:
A Language and Program for Complex Bayesian Modelling <=SEP=> http://www.jstor.org A Language and Program for Complex Bayesian Modelling Author(s): W. R. Gilks, A. Thomas and D. J. Spiegelhalter Source: Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 43, No. 1, Special Issue: Conference on Practical Bayesian Statistics, 1992 (3) (1994), pp. 169-177 Published by: for the Wiley Royal Statistical Society Stable URL: http://www.jstor.org/stable/2348941 Accessed: 19-08-2014 17:40 UTC

Statistical Methods in Psychology Journals Guidelines and Explanations <=SEP=> n the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was "to elucidate some of the controversial issues surrounding applications of statistics including significance testing and its alternatives; alternative underlying models and data transformation; and newer methods made possible by powerful computers" (BSA, personal communication, February 28, 1996). Robert Rosenthal, Robert Abelson, and Jacob Cohen (cochairs) met initially and agreed on the desirability of having several types of specialists on the task force: statisticians, teachers of statistics, journal editors, authors of statistics books, computer experts, and wise elders. Nine individuals were subsequently invited to join and all agreed. These were Leona Aiken, Mark Appelbaum, Gwyneth Boodoo, David A. Kenny, Helena Kraemer, Donald Rubin, Bruce Thompson, Howard Wainer, and Leland Wilkinson. In addition, Lee Cronbach, Paul Meehl, Frederick Mosteller and John Tukey served as Senior Advisors to the Task Force and commented on written materials. The TFSI met twice in two years and corresponded throughout that period. After the first meeting, the task force circulated a preliminary report indicating its intention to examine issues beyond null hypothesis significance testing. The task force invited comments and used this feedback in the deliberations during its second meeting. After the second meeting, the task force recommended several possibilities for further action, chief of which would be to revise the statistical sections of the American Psychological Association Publication Manual (APA, 1994). After extensive discussion, the BSA recommended that "before the TFSI undertook a revision of the APA Publication Manual, it might want to consider publishing an article in American Psychologist, as a way to initiate discussion in the field about changes in current practices of data analysis and reporting" (BSA, personal communication, November 17, 1997). This report follows that request. The sections in italics are proposed guidelines that the TFSI recommends could be used for revising the APA publication manual or for developing other BSA supporting materials. Following each guideline are comments, explanations, or elaborations assembled by Leland Wilkinson for the task force and under its review. This report is concerned with the use of statistical methods only and is not meant as an assessment of research methods in general. Psychology is a broad science. Methods appropriate in one area may be inappropriate in another. The title and format of this report are adapted from a similar article by Bailar and Mosteller (1988). That article should be consulted, because it overlaps somewhat with this one and discusses some issues relevant to research in psychology. Further detail can also be found in the publications on this topic by several committee members (Abelson, 1995, 1997; Rosenthal, 1994; Thompson, 1996; Wainer, in press; see also articles in Harlow, Mulaik, & Steiger, 1997).

A generative theory of similarity <=SEP=> We argue that similarity judgments are inferences about generative processes, and that two objects appear similar when they are likely to have been generated by the same process. We describe a formal model based on this idea and show how featural and spatial models emerge as special cases. We compare our approach to the transformational approach, and present an experiment where our model performs better than a transformational model. Every object is the outcome of a generative process. An animal grows from a fertilized egg into an adult, a city develops from a settlement into a metropolis, and an artifact is assembled from a pile of raw materials according to the plan of its designer. Observations like these motivate the generative approach, which proposes that an object may be understood by thinking about the process that generated it. The promise of the approach is that apparently complex objects may be produced by simple processes, an insight that has proved productive across disciplines including biology [18], physics [21], and architecture [1]. To give two celebrated examples from biology, the shape of a pinecone and the markings on a cheetah’s tail can be generated by remarkably simple processes of growth. These patterns can be characterized much more compactly by describing their causal history than by attempting to describe them directly. Leyton has argued that the generative approach provides a general framework for understanding cognition. Applications of the approach can be found in generative theories of perception [12], memory [12], language [3], categorization [2], and music [11]. This paper offers a generative theory of similarity, a notion often invoked by models of high-level cognition. We argue that two objects are similar to the extent that they seem to have been generated by the same underlying process. The literature on similarity covers settings that extend from the comparison of simple stimuli like tones and colored patches to the comparison of highly-structured objects like narratives. The generative approach is relevant to the entire spectrum of applications, but we are particularly interested in high-level similarity. In particular, we are interested in how similarity judgments draw on intuitive theories, or systems of rich conceptual knowledge [15]. Generative processes and theories are intimately linked. Murphy [14], for example, defines a theory as ‘a set of causal relations that collectively generate or explain the phenomena in a domain.’ We hope that our generative theory provides a framework in which to model how similarity judgments emerge from intuitive theories. We develop a formal theory of similarity and compare it to three existing theories. The featural account [20] suggests that the similarity of two objects is a function of their common and distinctive features, the spatial account suggests that similarity is inversely proportional to distance in a spatial representation, [19] and the transformation account suggests that similarity depends on the number of operations required to transform one object into the other [6]. We show that versions of each of these approaches emerge as special cases of our generative approach, and present an experiment that directly compares our approach with the transformation account. A fourth theory suggests that similarity relies on a process of analogical mapping [5]. We will not discuss this approach in detail, but finish by suggesting how a generative approach to analogy differs from the standard view. Generative processes and similarity Before describing our formal model, we give an informal motivation for a generative approach to similarity. Suppose we are shown a prototype object and asked to describe similar objects we might find in the world. There are two kinds of answers: small perturbations of the prototype, or objects produced by small perturbations of the process that generated the prototype. The second strategy is likely to be more successful than the first, since many perturbations of the prototype will not arise from any plausible generative process, and thus could never appear in practice. By construction, however, an object produced by a perturbation of an existing generative process will have a plausible causal history. To give a concrete example, suppose the prototype is a bug generated by a biological process of growth (Figure 1ii). The bug in i is a small perturbation of the prototype, but seems unlikely to arise since legs are generated in pairs. A perturbation of the generative process might produce a bug with more segments, such as the bug in iii. If we hope to find a bug that is similar but not identical to the prototype, iii is a better bet than i. A sceptic might argue that this one-shot learning problem can be solved by taking the intersection of the set of objects similar to the prototype and the set of obi) ii) Prototype iii) Figure 1: Three bugs. Which is more similar to the prototype — i or iii? jects that are likely to exist. The second set depends critically on generative processes, but the first set (and therefore the notion of similarity) need not. We think it more likely that the notion of similarity is ultimately grounded in the world, and that it evolved for the purpose of comparing real-world objects. If so, then knowledge about what kinds of objects are likely to exist may be deeply bound up with the notion of similarity. The one-shot learning problem is of practical importance, but is not the standard context in which similarity is discussed. More commonly, subjects are shown a pair of objects and asked to rate the similarity of the pair. Note that both objects are observed to exist and the previous argument does not apply. Yet generative processes are still important, since they help pick out the features critical for the similarity comparison. Suppose, for instance, that a forest-dweller discovers a nutritious mushroom. Which is more similar to the mushroom: a mushroom identical except for its size, or a mushroom identical except for its color? Knowing how mushrooms are formed suggests that size is not a key feature. Mushrooms grow from small to large, and the final size of a plant depends on factors like the amount of sunlight it received and the fertility of the soil that it grew in. Reflections like these suggest that the differently-sized mushroom should be judged more similar. A final reason why generative processes matter is that they are deeply related to essentialism. Medin and Ortony [13] note that ‘surface features are frequently constrained by, and sometimes generated by, the deeper, more central parts of objects.’ Even if we observe only the surface features of two objects, it may make sense to judge their similarity by comparing the deeper properties inferred to generate the surface features. Yet we can say more: just as surface features are generated by the essence of the object, the essence itself has a generative history. Surface features are often reliable guides to the essence of an object, but the object’s causal history is a still more reliable indicator, if not a defining criterion of its essence. Keil [9] discusses the case of an animal that is born a skunk, then undergoes surgery that leaves it looking exactly like a raccoon. Since the animal is generated in the same way as a skunk (born of skunk parents), we conclude that it remains a skunk, no matter how it appears on the surface. These examples suggest that the generative approach may help to explain a broad class of theory-dependent inferences. We now present a formal model that attempts to capture the intuitions behind all of these cases. A computational theory of similarity Given a domain D, we develop a theory that specifies the similarity between any two samples from D. A sample from D will usually contain a single object, but working with similarities between sets of objects is useful for some applications. We formalize a generative process as a probability distribution over D that depends on parameter vector θ. Suppose that s1 and s2 are samples from D. We consider two hypotheses: H1 holds that s1 and s2 are independent samples from a single generative process, and H2 holds that the samples are generated from two independently chosen processes. Similarity is defined as the probability that the objects are generated by the same process: that is, the relative posterior probability of H1 compared to H2: sim(s1, s2) = P (H1|s1, s2)

Explaining the Gibbs Sampler <=SEP=> 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.

The Adaptive Nature of Human Categorization <=SEP=> A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Although the rational model considers just I level of categorization, it is shown how predictions can be enhanced by considering higher and lower levels. Considering prediction at the lower, individual level allows integration of this rational analysis of categorization with the earlier rational analysis of memory (Anderson & Milson, 1989).

Workflow mining: discovering process models from event logs <=SEP=> Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.

Yelp Restaurant Photo Classification <=SEP=> Everyday, millions of users upload numerous photos of different businesses to Yelp. Though most of them are tagged with simple information such as business names, the deep-level information typically are not revealed until looked by humans. Such deep-level information contains things like whether the restaurant is good for lunch or dinner, whether it has outdoor seating, etc. This information can be crucial in helping potential customers to determine whether its the ideal restaurant they want to dine in. Hand labeling these restaurants can be not only costly but also tedious since photos are uploaded almost every second to Yelp. In this project, we used machine learning techniques, specifically image classification methods, to identify the context and rich information of different photos on Yelp. We used three different models to train and test the given dataset. The dataset is provided by Yelp on Kaggle.com. In the training set, 234842 arbitrary-sized images submitted by Yelp users are correlated to one of 2000 Yelp businesses. The number of corresponding photos for each business ranges from 2 to 2974, with mean of 117. The image count distribution is shown by Fig1. Every business is tagged with multiple descriptive labels in 9 aspects. The labels includes: 0: good for lunch 1: good for dinner 2: takes reservations 3: outdoor seating 4: restaurant is expensive 5: has alcohol 6: has table service 7: ambience is classy 8: good for kids As an example, Fig2 illustrates the structure of the dataset. Our goal is to predict the labels of a business in these 9 aspects given its photos. A notable challenge is that the labels are not directly correlated to each image. Individual photos can possibly be indicative in one aspect, yet it is tagged with all the labels of the business. As a result, the dataset might exhibit high noise level. The fraction of each label across the training set is shown in Fig3. Though assumed independent, some label pairs exhibit high covariance, such as ”has alcohol” and ”has table service” (Fig4).

Be Conservative: Enhancing Failure Diagnosis with Proactive Logging <=SEP=> When systems fail in the field, logged error or warning messages are frequently the only evidence available for assessing and diagnosing the underlying cause. Consequently, the efficacy of such logging—how often and how well error causes can be determined via postmortem log messages—is a matter of significant practical importance. However, there is little empirical data about how well existing logging practices work and how they can yet be improved. We describe a comprehensive study characterizing the efficacy of logging practices across five large and widely used software systems. Across 250 randomly sampled reported failures, we first identify that more than half of the failures could not be diagnosed well using existing log data. Surprisingly, we find that majority of these unreported failures are manifested via a common set of generic error patterns (e.g., system call return errors) that, if logged, can significantly ease the diagnosis of these unreported failure cases. We further mechanize this knowledge in a tool called Errlog , that proactively adds appropriate logging statements into source code while adding only 1.4% performance overhead. A controlled user study suggests that Errlog can reduce diagnosis time by 60.7%.

Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions <=SEP=> Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time.

Analytic Performance Modeling and Optimization of Live VM Migration <=SEP=> Earlier virtual machine (VM) migration techniques consisted of stop-and-copy: the VM was stopped, its address space was copied to a different physical machine, and the VM was restarted at that machine. Recent VM hypervisors support live VM migration, which allows pages to be copied while the VM is running. If any copied page is dirtied (i.e., modified), it has to be copied again. The process stops when a fraction α of the pages need to be copied. Then, the VM is stopped and the remaining pages are copied. This paper derives a model to compute the downtime, total number of pages copied, and network utilization due to VM migration, as a function of α and other parameters under uniform and non-uniform dirtying rates. The paper also presents a non-linear optimization model to find the value of α that minimizes the downtime subject to network utilization constraints.

Trends, challenges and needs for lattice-based cryptography implementations: special session <=SEP=> Advances in computing steadily erode computer security at its foundation, calling for fundamental innovations to strengthen the weakening cryptographic primitives and security protocols. At the same time, the emergence of new computing paradigms, such as Cloud Computing and Internet of Everything, demand that innovations in security extend beyond their foundational aspects, to the actual design and deployment of these primitives and protocols while satisfying emerging design constraints such as latency, compactness, energy efficiency, and agility. While many alternatives have been proposed for symmetric key cryptography and related protocols (e.g., lightweight ciphers and authenticated encryption), the alternatives for public key cryptography are limited to post-quantum cryptography primitives and their protocols. In particular, lattice-based cryptography is a promising candidate, both in terms of foundational properties, as well as its application to traditional security problems such as key exchange, digital signature, and encryption/decryption. We summarize trends in lattice-based cryptographic schemes, some fundamental recent proposals for the use of lattices in computer security, challenges for their implementation in software and hardware, and emerging needs.

Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention <=SEP=> Traditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. They ignore that one discusses diverse topics when dynamically interacting with different people. Moreover, people who talk about the same topic have different effects on the topic. In this paper, we propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. A conversation network linking users based on reposting and replying relationship is constructed to mine the dynamic user behaviours. We model dynamic interactions and user attention so as to learn interaction-aware edge embeddings with social context. Then they are incorporated into neural variational inference for generating the more consistent topics. The experiments on three real-world datasets show that our proposed model is effective.

Dyslexia in English as a second language. <=SEP=> This study focused on English as L2 in a group of Norwegian dyslexic 12 year olds, compared to an age and gender matched control group. Norwegian school children learn English from the first grades on. The subjects were assessed with a test battery of verbal and written tasks. First, they were given a comprehension task; second, a model sentence task; third, two pragmatic tasks, and fourth, three tasks of literacy. The verbal tasks were scored according to comprehension, morphology, syntax and semantics, while the literacy tasks were scored by spelling, translation and reading skills. It was hypothesized that the results of the control group and the dyslexia group would differ on all tasks, but that subgrouping the dyslexia group by comprehension skills would show heterogeneity within the dyslexia group. The data analyses confirmed these hypotheses. Significant differences were seen between the dyslexia group and the control group. However, the subgrouping revealed minor differences between the control group and the subgroup with good comprehension skills, and major differences between the control group and the subgroup with poor comprehension skills. Especially morphology and spelling were difficult for the dyslexia group. The results were tentatively discussed within the framework of biological and cognitive models of how to interpret L2 performance in dyslexia, underlining the importance of further research in L2 acquisition in dyslexia.

Discovering Opinion Changes in Online Reviews via Learning Fine-Grained Sentiments <=SEP=> Recent years have shown rapid advancement in understanding consumers' behaviors and opinions through collecting and analyzing data from online social media platforms. While abundant research has been undertaken to detect users' opinions, few tools are available for understanding events where user opinions change drastically. In this paper, we propose a novel framework for discovering consumer opinion changing events. To detect subtle opinion changes over time, we first develop a novel fine-grained sentiment classification method by leveraging word embedding and convolutional neural networks. The method learns sentiment-enhanced word embedding, both for words and phrases, to capture their corresponding syntactic, semantic, and sentimental characteristics. We then propose an opinion shift detection algorithm that is based on the Kullback-Leibler divergence of temporal opinion category distributions, and conducted experiments on online reviews from Yelp. The results show that the proposed approach can effectively classify fine-grained sentiments of reviews and can discover key moments that correspond to consumer opinion shifts in response to events that relate to a product or service.

Impact of Social Media on Social Anxiety: A Systematic Review <=SEP=> Introduction: Online social networking sites are being used all around the world. However, only recently researchers have started to investigate their relationship with mental health. Evidence coming from literature suggests that they have both advantages and disadvantages for individuals. The aim of this study is to critically review the existent research conducted on the relationship between online social networking and social anxiety. Method: According to PRISMA guidelines, comprehensive systematic searches of electronic databases were conducted (PsychInfo, Cochrane, PubMed, Scopus, Web of Science). Terms related to online social networking were combined with social anxiety terms. After identifying relevant papers, the relationship between social media networking and social anxiety as well as limitations of each study were presented. All of the papers included were cross-sectional, relying on self-report assessments. Conclusion: There are several papers published on the relationship between social media and social anxiety; however, more research needs to be done in order to clarify the influence of one variable over the other. Rigorous experimental studies need to be conducted with different time-point assessments, and bidirectionality should be investigated. Important mediators and moderators have to be considered in order to explain the relationship between social media networking and social anxiety.

Self-organizing neural integration of pose-motion features for human action recognition <=SEP=> The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions.

Bag-of-Discriminative-Words (BoDW) Representation via Topic Modeling <=SEP=> Many of the words in a given document either deliver facts (<italic>objective</italic>) or express opinions (<italic> subjective</italic>), respectively, depending on the topics they are involved in. For example, given a bunch of documents, the word “bug” assigned to the topic “<italic>order Hemiptera</italic>” apparently remarks one object (i.e., one kind of insects), while the same word assigned to the topic “<italic>software </italic>” probably conveys a negative opinion. Motivated by the intuitive assumption that different words have varying degrees of <italic>discriminative</italic> power in delivering the objective sense or the subjective sense with respect to their assigned topics, a model named as <italic>d</italic>iscriminatively <italic>o</italic>bjective- <italic>s</italic>ubjective LDA (dosLDA) is proposed in this paper. The essential idea underlying the proposed dosLDA is that a pair of objective and subjective selection variables are explicitly employed to encode the interplay between topics and discriminative power for the words in documents in a supervised manner. As a result, each document is appropriately represented as “<italic>bag-of-discriminative-words</italic>” (BoDW). The experiments reported on documents and images demonstrate that dosLDA not only performs competitively over traditional approaches in terms of topic modeling and document classification, but also has the ability to discern the discriminative power of each word in terms of its objective or subjective sense with respect to its assigned topic.

Forensic mycology: current perspectives <=SEP=> (unported, v3.0) License. The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. Permissions beyond the scope of the License are administered by Dove Medical Press Limited. Information on how to request permission may be found at: http://www.dovepress.com/permissions.php Research and Reports in Forensic Medical Science 2015:5 75–83 Research and Reports in Forensic Medical Science Dovepress

Guidelines for the process of cross-cultural adaptation of self-report measures. <=SEP=> With the increase in the number of multinational and multicultural research projects, the need to adapt health status measures for use in other than the source language has also grown rapidly. Most questionnaires were developed in English-speaking countries, but even within these countries, researchers must consider immigrant populations in studies of health, especially when their exclusion could lead to a systematic bias in studies of health care utilization or quality of life. The cross-cultural adaptation of a health status selfadministered questionnaire for use in a new country, culture, and/or language necessitates use of a unique method, to reach equivalence between the original source and target versions of the questionnaire. It is now recognized that if measures are to be used across cultures, the items must not only be translated well linguistically, but also must be adapted culturally to maintain the content validity of the instrument at a conceptual level across different cultures. Attention to this level of detail allows increased confidence that the impact of a disease or its treatment is described in a similar manner in multinational trials or outcome evaluations. The term “cross-cultural adaptation” is used to encompass a process that looks at both language (translation) and cultural adaptation issues in the process of preparing a questionnaire for use in another setting. Cross-cultural adaptations should be considered for several different scenarios. In some cases, this is more obvious than in others. Guillemin et al suggest five different examples of when attention should be paid to this adaptation by comparing the target (where it is going to be used) and source (where it was developed) language and culture. The first scenario is that it is to be used in the same language and culture in which it was developed. No adaptation is necessary. The last scenario is the opposite extreme, the application of a questionnaire in a different culture, language and country—moving the Short Form 36-item questionnaire from the United States (source) to Japan (target) which would necessitate translation and cultural adaptation. The other scenarios are summarized in Table 1 and reflect situations when some translation and/or adaptation is needed. The guidelines described in this document are based on a review of cross-cultural adaptation in the medical, sociological, and psychological literature. This review led to the description of a thorough adaptation process designed to maximize the attainment of semantic, idiomatic, experiential, and conceptual equivalence between the source and target questionnaires.. Further experience in cross-cultural adaptation of generic and diseasespecific instruments and alternative strategies driven by different research groups have led to some refinements in methodology since the 1993 publication.. These guidelines serve as a template for the translation and cultural adaptation process. The process involves the adaptation of individual items, the instructions for the questionnaire, and the response options. The text in the next section outlines the methodology suggested (Stages I–V). The subsequent section (Stage VI) presents a suggested appraisal process whereby an advisory committee or the developers review the process and determine whether this is an acceptable translation. Although such a committee or the developers may not be engaged in tracking translated versions of the instrument, this stage has been included in case there is a tracking system. Records of translated versions not only can save considerable time and effort (by using already available questionnaires) but also avoid erroneous comparisons of results across different translated versions. The process of cross-cultural adaptation tries to produce equivalency between source and target based on content. The assumption that is sometimes made is that this process will ensure retention of psychometric properties such as validity and reliability at an item and/or a scale level. However, this is not necessarily the case: For instance, if the new culture has a different way of approaching a task that makes it inherently more or less difficult compared with other items, it would change the validity, certainly in terms of item-level analyses (such as item response theory, similar to Rasch). Further tests should be conducted on the psychometric properties of the adapted questionnaire after the translation is complete. This will be discussed briefly at the end of the guidelines. In fact, the translation process outlined in this article is the first step in the three-step process adopted by the International Society for Quality of Life Assessment (IQOLA) project. The other two steps From the *Institute for Work and Health; †St. Michael’s Hospital; Departments of ‡Occupational Therapy and 4Medicine and the §Clinical Epidemiology and Health Care Research Program, University of Toronto ¶The University Health Network, Toronto General Hospital; and #Mt. Sinai Hospital, Toronto, Ontario, Canada; **Ecole de Sant, Publique, Facult, de M,decine,Vandoeuvre-les-Nancy, France; and the ††Division of Rheumatology, Department of Medicine, Escola Paulista de Medicina, Universidade Federal de Sõo Paulo, Sõo Paulo, Brazil. Supported in part by the American Academy of Orthopaedic Surgeons and the Institute for Work & Health who co-sponsored the development of these guidelines. DB was supported by a Medical Research Council of Canada PhD Fellowship during the preparation of this work.

Data processing and semantics for advanced internet of things (IoT) applications: modeling, annotation, integration, and perception <=SEP=> This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will describe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions related to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, domain knowledge integration and management, querying large-scale IoT data, and AI applications for automated knowledge extraction from real world data.

THE RELATIONSHIPS BETWEEN IT FLEXIBILITY, IT-BUSINESS STRATEGIC ALIGNMENT, AND IT CAPABILITY <=SEP=> What seems to still be the main concern for managers in the corporate world across the globe is ITbusiness strategic alignment. This study seeks to address the research problem about the lack of alignment between IT and business strategies. Upon reviewing various literature on this subject, it was found that IT flexibility is one of the most vital factors that help sustain strategic alignment. The researcher upon having a detailed discussion on the possible areas associated with the present body of knowledge has discovered gaps in the studies that have been undertaken on strategic alignment and IT flexibility. This is because IT capability in relation to IT flexibility and strategic alignment has been ignored in the previous studies. As a result, this research proposes a relationship between IT flexibility (i.e., modularity, connectivity and compatibility), IT capability, and strategic alignment.

Deep Blind Image Inpainting <=SEP=> Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing information in corrupted regions. However, directly using existing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.

A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users <=SEP=> Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.

Localizing Ashkenazic Jews to Primeval Villages in the Ancient Iranian Lands of Ashkenaz <=SEP=> The Yiddish language is over 1,000 years old and incorporates German, Slavic, and Hebrew elements. The prevalent view claims Yiddish has a German origin, whereas the opposing view posits a Slavic origin with strong Iranian and weak Turkic substrata. One of the major difficulties in deciding between these hypotheses is the unknown geographical origin of Yiddish speaking Ashkenazic Jews (AJs). An analysis of 393 Ashkenazic, Iranian, and mountain Jews and over 600 non-Jewish genomes demonstrated that Greeks, Romans, Iranians, and Turks exhibit the highest genetic similarity with AJs. The Geographic Population Structure analysis localized most AJs along major primeval trade routes in northeastern Turkey adjacent to primeval villages with names that may be derived from "Ashkenaz." Iranian and mountain Jews were localized along trade routes on the Turkey's eastern border. Loss of maternal haplogroups was evident in non-Yiddish speaking AJs. Our results suggest that AJs originated from a Slavo-Iranian confederation, which the Jews call "Ashkenazic" (i.e., "Scythian"), though these Jews probably spoke Persian and/or Ossete. This is compatible with linguistic evidence suggesting that Yiddish is a Slavic language created by Irano-Turko-Slavic Jewish merchants along the Silk Roads as a cryptic trade language, spoken only by its originators to gain an advantage in trade. Later, in the 9th century, Yiddish underwent relexification by adopting a new vocabulary that consists of a minority of German and Hebrew and a majority of newly coined Germanoid and Hebroid elements that replaced most of the original Eastern Slavic and Sorbian vocabularies, while keeping the original grammars intact.

A 6.25Gbps Feed-forward Equalizer in 0.18μm CMOS Technology for SerDes <=SEP=> This paper presents a 6.25Gbps feed-forward equalizer (FFE) to reduce the inter-symbol-interference (ISI) in high-speed transmission backplane. The 3-tap fractionally spaced equalizer consists of a delay line, multiplier & summer cells and an output circuit. Active-inductive peaking circuit and capacitor-degenerated circuit are used in the delay line and the output stage respectively to meet the bandwidth demand. The proposed FFE has been implemented in TSMC 0.18μm CMOS technology with a whole area of 0.5mm×0.51mm including I/O pads. The power consumption of the core circuit is 31.7mW under 1.8V power supply. Post simulation results show that the distorted signal through a 24-inch backplane is well recovered by this equalizer.

Spatiotemporal Sequential Influence Modeling for Location Recommendations: A Gravity-based Approach <=SEP=> Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L2TG), and utilizes the L2TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.

Orthogonal Coaxial Cavity Filters With Distributed Cross-Coupling <=SEP=> In this letter, six-pole filters are presented, in which the coaxial cavity resonators are arranged with 90 degree rotated spatial orientation. Cross-coupling between nonadjacent resonators will appear. In the new proposed filter topologies the cross-coupling elements are not arranged in separated triplet or quadruplet blocks. They interlace to form distributed cross-coupling. Synthesis of the distributed coupling matrix based on the symmetric canonical prototype is discussed. Two different bandpass filter topologies are presented. The first one has an inline arrangement of six orthogonal coaxial cavity resonators. Its distributed cross-coupling consists of four interlaced triplets, which generate two transmission zeros above the passband. The second filter design has a folded arrangement of two inline triplets with strong cross-coupling. An additional interlaced quadruplet pushes the resulting transmission zeros to the complex frequency domain. Measurement results of two corresponding filter prototypes confirm the predicted performance.

A virtual reality intervention to improve the understanding and empathy for people with dementia in informal caregivers: results of a pilot study. <=SEP=> OBJECTIVE
Informal caregivers often experience psychological distress due to the changing functioning of the person with dementia they care for. Improved understanding of the person with dementia reduces psychological distress. To enhance understanding and empathy in caregivers, an innovative technology virtual reality intervention Through the D'mentia Lens (TDL) was developed to experience dementia, consisting of a virtual reality simulation movie and e-course. A pilot study of TDL was conducted.


METHODS
A pre-test-post-test design was used. Informal caregivers filled out questionnaires assessing person-centeredness, empathy, perceived pressure from informal care, perceived competence and quality of the relationship. At post-test, additional questions about TDL's feasibility were asked.


RESULTS
Thirty-five caregivers completed the pre-test and post-test. Most participants were satisfied with TDL and stated that TDL gave more insight in the perception of the person with dementia. The simulation movie was graded 8.03 out of 10 and the e-course 7.66. Participants significantly improved in empathy, confidence in caring for the person with dementia, and positive interactions with the person with dementia.


CONCLUSION
TDL is feasible for informal caregivers and seems to lead to understanding of and insight in the experience of people with dementia. Therefore, TDL could support informal caregivers in their caregiving role.

Substrate Integrated Waveguide ( SIW ) to Microstrip Transition at X-Band <=SEP=> Substrate integrated waveguide (SIW) is a new form of transmission line. It facilitates the realization of nonplanar (waveguide based) circuits into planar form for easy integration with other planar (microstrip) circuits and systems. This paper describes the design of an SIW to microstrip transition. The transition is broadband covering the frequency range of 8 – 12GHz. The measured in-band insertion loss is below 0.6dB while the return loss is less than 10dB. The circuit is simulated in HFSS and results are measured on vector network analyzer (VNA). Keywords— Microstrip; Substrate Integrated Waveguide; Transition

Why experts make errors <=SEP=> Expert latent f ingerprint examiners were presented with fingerprints taken from real criminal cases. Half of the prints had been previously judged as individualizations and the other half as exclusions. We re-presented the same prints to the same experts who had judged them previously, but provided biasing contextual information in both the individualizations and exclusions. A control set of individualizations and exclusions was also re-presented as part of the study. The control set had no biasing contextual information associated with it. Each expert examined a total of eight past decisions. Two-thirds of the experts made inconsistent decisions. The f indings are discussed in terms of psychological and cognitive vulnerabilities.