snu-aidas/HVMap-dataset huggingface dataset:
The comparability of measurements of attitudes toward immigration in the European Social Survey : exact versus approximate measurement equivalence

International survey datasets are analyzed with increasing frequency to investigate and compare attitudes toward immigration and to examine the contextual factors that shape these attitudes. However, international comparisons of abstract, psychological constructs require the measurements to be equivalent–i.e., they should measure the same concept on the same measurement scale. Traditional approaches to assessing measurement equivalence quite often lead to the conclusion that measurements are cross-nationally incomparable but have been criticized for being overly strict. In the current study, we present an alternative Bayesian approach that assesses whether measurements are approximately (rather than exactly) equivalent. This approach allows small variations in measurement parameters across groups. Taking a multiple group confirmatory factor analysis framework as a starting point, this study applies approximate and exact equivalence tests to the anti-immigration attitudes scale that was implemented in the European Social Survey (ESS). Measurement equivalence is tested across the full set of 271,220 individuals in 35 ESS countries over six rounds. The results of the exact and the approximate approaches are quite different. Approximate scalar measurement equivalence is established in all ESS rounds, thus allowing researchers to meaningfully compare these mean scores and their relationships with other theoretical constructs of interest. The exact approach, however, eventually proves to be overly strict and leads to the conclusion that measurements are incomparable for a large number of countries and time points. DOI: https://doi.org/10.1093/poq/nfv008 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-110767 Published Version Originally published at: Davidov, Eldad; Cieciuch, Jan; Meuleman, Bart; Schmidt, Peter; Algesheimer, René; Hausherr, Mirjam (2015). The comparability of measurements of attitudes toward immigration in the European Social Survey: exact versus approximate measurement equivalence. Public Opinion Quarterly, 79(S1):244-266. DOI: https://doi.org/10.1093/poq/nfv008 © The Author 2015. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com THE COMPARABILITY OF MEASUREMENTS OF ATTITUDES TOWARD IMMIGRATION IN THE EUROPEAN SOCIAL SURVEY EXACT VERSUS APPROXIMATE MEASUREMENT EQUIVALENCE
What storytelling can do for information visualization

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

OBJECTIVE
Excessive and addictive internet use and computer game playing is reported as an increasing problem in outpatient care. The aim of this paper is to give an overview about the current scientific discussion of the overuse and addiction of internet and computer game playing.


METHODS
Pubmed was used for a systematic literature research considering original papers and review articles dealing with Internet/computer game addiction.


RESULTS
Recent epidemiological data from Germany suggest that 1.5-3.5 % of adolescent computer and internet users show signs of an overuse or addictive use of computer and video games. Moreover there is evidence that the disorder is associated with higher rates of depression, anxiety, as well as lower achievements e. g. at school. Although the nosological assignment still remains unclear there is some evidence from neurobiological data that the disorder can be conceptualized as behavioral addiction. As treatment strategy CBT-techniques have been proposed, but there is still a lack of controlled clinical trials concerning their efficacy.


CONCLUSIONS
Since the addicted persons often show little motivation for a behavioural change we consider it a promising approach to treat and train their relatives with the aim of increasing the motivation for a behavioural change of the addicted person.
Review of Face Recognition Techniques

Use of Biometrics and widespread acceptability for person authentication has instigated several techniques. Now a day’s security is very important. Biometrics has received a great attention. Face biometrics, is useful person’s authentication that recognizes face. In this paper first we represent an overview of face recognition and discuss the methodology and functioning. Thereafter we represent face recognition techniques which are recently used including their advantages and disadvantages. A large number of face recognition algorithms have been developed. In this paper an attempt is made to review a wide range of methods including PCA, Eigen face, LDA for recognition and hybrid combination if this techniques. Under the various illumination and expression condition of face images some techniques specified here also improves the efficiency of face recognition. Keywords— Face Recognition, Principal Component Analysis, Eigen Face, Linear Discriminant Analysis
BFC: High-performance distributed big-file cloud storage based on key-value store

Nowadays, cloud-based storage services are rapidly growing and becoming an emerging trend in data storage field. There are many problems when designing an efficient storage engine for cloud-based systems with some requirements such as big-file processing, lightweight meta-data, low latency, parallel I/O, deduplication, distributed, high scalability. Key-value stores played an important role and showed many advantages when solving those problems. This paper presents about Big File Cloud (BFC) with its algorithms and architecture to handle most of problems in a big-file cloud storage system based on key-value store. It is done by proposing low-complicated, fixed-size meta-data design, which supports fast and highly-concurrent, distributed file I/O, several algorithms for resumable upload, download and simple data deduplication method for static data. This research applied the advantages of ZDB - an in-house key-value store which was optimized with auto-increment integer keys for solving big-file storage problems efficiently. The results can be used for building scalable distributed data cloud storage that support big-file with size up to several terabytes.
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity and availability of the services. The speed of the IDS is very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The techniques J48, Random Forest, Random Tree, MLP, Naïve Bayes and Bayes Network classifiers have been chosen for this study. It has been proven that the Random forest classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type (DOS, R2L, U2R, and PROBE). Keywords— IDS, DDoS, MLP, Naïve Bayes, Random Forest
Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information

The possibility of automatic understanding of customers’ shopping behavior and acting according to their needs is relevant in the marketing domain, attracting a lot of attention lately. In this work, we focus on the task of automatic assessment of customers’ shopping behavior, by proposing a multilevel framework. The framework is supported at low-level by different types of cameras, which are synchronized, facilitating efficient processing of information. A fish-eye camera is used for tracking, while a high-definition one serves for the action recognition task. The experiments are performed on both laboratory and real-life recordings in a supermarket. From the video recordings, we extract features related to the spatio-temporal behavior of trajectories, the dynamics and the time spent in each region of interest (ROI) in the shop and regarding the customer-products interaction patterns. Next ∗Corresponding Author. Phone: +31(0)15 2781537. Fax: +31(0)15 2787141. Email address: m.c.popa@tudelft.nl (M.C. Popa) Preprint submitted to Pattern Recognition Letters December 24, 2011 we analyze the shopping sequences using a Hidden Markov Model (HMM). We conclude that it is possible to accurately classify trajectories (93%), discriminate between different shopping related actions (91.6%), and recognize shopping behavioral types by means of the proposed reasoning model in 95% of the cases.
The Analysis of the Mozart Effect on Visual Search

Over twenty-five years ago, researchers began to study the effects of music on the brain and cognition. The Mozart Effect is a term that was first coined by Dr. Alfred Tomatis in 1991 in his book titled “Porquoi Mozart” in which Tomatis advocated the use of Mozart music as alternative medicine for those suffering from dyslexia, autism, and other learning disorders. [Sorensen 2008] This effect, originally called “The Tomatis Effect” was observed by Tomatis during therapy sessions with his patients. From his observations, Tomatis claimed that Mozart music helped promote healing and could even cure depression. The definition of the term evolved over time; in the present day, the term “Mozart Effect” is typically used to refer to the phenomena of improved spatial reasoning after exposure to Mozart music.
Distributed Data Clustering in Peer -to-Peer Networks: A Technical Review

Clustering as one of the main branches of data mining, has gained an important place in the different applied fields. On the other hand, Peer-to-Peer (P2P) networks with features such as simplicity, low cost communication, and high availability resources, have gained a worldwide popularity in the present days. In P2P network, high volumes of data are distributed between dispersed data sources. In such distributed systems, sending data to a central site is not possible, due to processing, storage and handling costs. This issue makes the need for specific methods for distributed data clustering in P2P networks. This paper makes a technical review of distributed data clustering approaches in P2P network, to understand the research trends in this area. Keywords—Peer to Peer Networks, Distributed Data Mining, Distributed Data Clustering
A Unified Theory of Underreaction , Momentum Trading , and Overreaction in Asset Markets

We model a market populated by two groups of boundedly rational agents: “newswatchers” and “momentum traders.” Each newswatcher observes some private information, but fails to extract other newswatchers’ information from prices. If information diffuses gradually across the population, prices underreact in the short run. The underreaction means that the momentum traders can profit by trendchasing. However, if they can only implement simple ~i.e., univariate! strategies, their attempts at arbitrage must inevitably lead to overreaction at long horizons. In addition to providing a unified account of underand overreactions, the model generates several other distinctive implications. OVER THE LAST SEVERAL YEARS, a large volume of empirical work has documented a variety of ways in which asset returns can be predicted based on publicly available information. Although different studies have used a host of different predictive variables, many of the results can be thought of as belonging to one of two broad categories of phenomena. On the one hand, returns appear to exhibit continuation, or momentum, in the short to medium run. On the other hand, there is also a tendency toward reversals, or fundamental reversion, in the long run.1 It is becoming increasingly clear that traditional asset-pricing models— such as the capital asset pricing model ~CAPM! of Sharpe ~1964! and Lintner ~1965!, Ross’s ~1976! arbitrage pricing theory ~APT!, or Merton’s ~1973! intertemporal capital asset pricing model ~ICAPM!—have a hard time explaining the growing set of stylized facts. In the context of these models, all of the predictable patterns in asset returns, at both short and long horizons, must ultimately be traced to differences in loadings on economically meaningful risk factors. And there is little affirmative evidence at this point to suggest that this can be done. * Stanford Business School and MIT Sloan School of Management and NBER. This research is supported by the National Science Foundation and the Finance Research Center at MIT. We are grateful to Denis Gromb, René Stulz, an anonymous referee, and seminar participants at MIT, Michigan, Wharton, Duke, UCLA, Berkeley, Stanford, and Illinois for helpful comments and suggestions. Thanks also to Melissa Cunniffe for help in preparing the manuscript. 1 We discuss this empirical work in detail and provide references in Section I below. THE JOURNAL OF FINANCE • VOL. LIV, NO. 6 • DECEMBER 1999
ON THE PRICING OF CORPORATE DEBT : THE RISK STRUCTURE OF INTEREST RATES

On the Pricing of Corporate Debt: The Risk Structure of Interest Rates Author(s): Robert C. Merton Source: The Journal of Finance, Vol. 29, No. 2, Papers and Proceedings of the Thirty-Second Annual Meeting of the American Finance Association, New York, New York, December 28-30, 1973 (May, 1974), pp. 449-470 Published by: Blackwell Publishing for the American Finance Association Stable URL: http://www.jstor.org/stable/2978814 Accessed: 16/10/2008 10:09
Pvclust: an R package for assessing the uncertainty in hierarchical clustering

SUMMARY
Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstrap resampling techniques. Two types of p-values are available: approximately unbiased (AU) p-value and bootstrap probability (BP) value. Multiscale bootstrap resampling is used for the calculation of AU p-value, which has superiority in bias over BP value calculated by the ordinary bootstrap resampling. In addition the computation time can be enormously decreased with parallel computing option.
Finding scientific gems with Google's PageRank algorithm

We apply the Google PageRank algorithm to assess the relative importance of all publications in the Physical Review family of journals from 1893 to 2003. While the Google number and the number of citations for each publication are positively correlated, outliers from this linear relation identify some exceptional papers or “gems” that are universally familiar to physicists. © 2006 Published by Elsevier Ltd. PACS: 02.50.Ey; 05.40.-a; 05.50.+q; 89.65.-s
IntEx: A Syntactic Role Driven Protein-Protein Interaction Extractor for Bio-Medical Text

In this paper, we present a fully automated extraction system, named IntEx, to identify gene and protein interactions in biomedical text. Our approach is based on first splitting complex sentences into simple clausal structures made up of syntactic roles. Then, tagging biological entities with the help of biomedical and linguistic ontologies. Finally, extracting complete interactions by analyzing the matching contents of syntactic roles and their linguistically significant combinations. Our extraction system handles complex sentences and extracts multiple and nested interactions specified in a sentence. Experimental evaluations with two other state of the art extraction systems indicate that the IntEx system achieves better performance without the labor intensive pattern engineering requirement. ∗
Approximate Gaussian Elimination

Approximate Gaussian Elimination
Verifying Anaconda's expert rating by competing against Chinook: experiments in co-evolving a neural checkers player

Since the early days of arti5cial intelligence, there has been interest in having a computer teach itself how to play a game of skill, like checkers, at a level that is competitive with human experts. To be truly noteworthy, such e7orts should minimize the amount of human intervention in the learning process. Recently, co-evolution has been used to evolve a neural network (called Anaconda) that, when coupled with a minimax search, can evaluate checker-boards and play to the level of a human expert, as indicated by its rating of 2045 on an international web site for playing checkers. The neural network uses only the location, type, and number of pieces on the board as input. No other features that would require human expertise are included. Experiments were conducted to verify the neural network’s expert rating by competing it in 10 games against a “novice-level” version of Chinook, a world-champion checkers program. The neural network had 2 wins, 4 losses, and 4 draws in the 10-game match. Based on an estimated rating of Chinook at the novice level, the results corroborate Anaconda’s expert rating. c © 2002 Elsevier Science B.V. All rights reserved.
Coping with child sexual abuse among college students and post-traumatic stress disorder: the role of continuity of abuse and relationship with the perpetrator.

OBJECTIVE
The purpose of this study was to examine the effects of child sexual abuse (CSA) on the use of coping strategies and post-traumatic stress disorder (PTSD) scores in young adults, as well as the role of avoidance and approach coping strategies in those PTSD scores in CSA victims. The role of coping strategies was studied by considering their possible interactive effect with the continuity of abuse and the relationship with the perpetrator; the effect of coping strategies on PTSD was also compared between CSA victim and non-CSA victim participants.


METHOD
The sample was comprised of 138 victims of CSA and another 138 participants selected as a comparison group. Data about child sexual abuse were obtained from a questionnaire developed for this purpose. Coping strategies were assessed with the How I Deal with Things Scale (Burt & Katz, 1987), while PTSD scores were assessed with the "Escala de Gravedad de Síntomas del Trastorno de Estrés Postraumático" (Severity of Symptoms of PTSD Scale; Echeburúa et al., 1997).


RESULTS
Participants who had been victims of CSA showed significantly higher PTSD scores and lower approach coping strategies scores. However, differences in avoidance coping strategies between groups were not consistent and did not always follow the expected direction. Only the use of avoidance coping strategies was related to PTSD, participants who used these showing higher scores. The effects of avoidance strategies were stronger in continued than in isolated abuse, in intrafamilial than in extrafamilial abuse and in CSA victims than in non-victims.


CONCLUSIONS
These results confirm the idea of CSA as a high-risk experience that can affect the victim's coping strategies and lead to PTSD to a lesser or greater extent depending on the coping strategy used. Moreover, the role of these strategies varies depending on whether or not the participant is a victim of CSA and on the characteristics of abuse (continuity and relationship with the perpetrator).


PRACTICE IMPLICATIONS
In terms of intervention, a reduction of avoidance-type strategies appears to have a beneficial effect, especially in the case of intrafamilial and/or continued CSA victims. The encouragement of "spontaneous" approach strategies (devised by the victim herself, without counseling) would probably not lead to more positive outcomes in terms of PTSD symptomatology. However, encouraging CSA survivors to engage in therapy aimed at developing effective approach strategies, as other studies have suggested, may help reduce PTSD symptoms.
Solving traveling salesman problems via artificial intelligent search techniques

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics and combinatorial optimization. It is also considered as the class of the NPcomplete combinatorial optimization problems. By literatures, many algorithms and approaches have been launched to solve such the TSP. However, no current algorithms that can provide the exactly optimal solution of the TSP problem are available. This paper proposes the application of AI search techniques to solve the TSP problems. Three AI search methods, i.e. genetic algorithms (GA), tabu search (TS), and adaptive tabu search (ATS), are conducted. They are tested against ten benchmark real-world TSP problems. As results compared with the exactly optimal solutions, the AI search techniques can provide very satisfactory solutions for all TSP problems. Key-Words: Traveling Salesman Problem, Genetic Algorithm, Tabu Search, Adaptive Tabu Search