Different Degrees Of Relevance Research Articles

The information-theoretic view of quantum mechanics and the measurement problem(s)

Until recently Jeffrey Bub and Itamar Pitowsky, in the framework of an information-theoretic view of quantum mechanics, claimed first that to the measurement problem in its ordinary formulation there correspond in effect two measurement problems (simply called the big and the small measurement problems), with a different degree of relevance and, second, that the analysis of a quantum measurement is a problem only if other assumptions – taken by Pitowsky and Bub to be unnecessary ‘dogmas’ – are assumed. Here I critically discuss this unconventional stance on the measurement problem and argue that the Bub-Pitowsky arguments are inconclusive, mainly because they rely on an unwarranted extension to the quantum realm of a distinction concerning the foundations of special relativity which is in itself rather controversial.

METHODOLOGICAL NOTES ON METAPHILOSOPHY AS A PHILOSOPHICAL DISCIPLINE

The paper deals with the explanation of the subject of metaphilosophy as a philosophical science. The main themes and problems of metaphilosophy are formulated, and its interrelation with other philosophical subjects and fields of research is clarified. The author proposes some methodological principles that are essential in dealing with such an important problem of metaphilosophy as the relationship between a philosophical doctrine and the life of the thinker who created it. This problem is examined through the life and philosophical doctrine of G. Skovoroda. The paper also identifies certain types (dimensions) of determination of the genesis of philosophical thought, and classifies particular cases associated with different degrees of relevance to the problem of “philosophical doctrine and the life of the philosopher”.

동영상 플랫폼상의 식품 광고 타겟팅의 효과: PC와 모바일 비교를 중심으로*

1. Introduction With the increasing popularity of video contents, companies are keen on utilizing video content as an advertising space. Targeted advertising is directed towards consumers with certain characteristics (Plummer et al. 2007), and food advertisements are usually exposed to consumers of a certain age at specific times along with popular TV programs favored by the target segment (Soni and Vohra 2014). As such, targeted advertising has received a lot of attention as an effective way for companies to reach potential customers who are more likely to purchase; however, research on targeted advertising still lacks. Therefore, this study focuses on various targeting strategies widely used in video advertisements and examines their effects on advertising effectiveness in the food industry. This study further investigates that these targeting effects vary between devices. Specifically, this study intends to answer the following research questions. First, how do the targeting strategies affect the effectiveness of food advertisement on the video platform? Second, how does the device type moderate the aforementioned effects? 2. Literature Review and Hypotheses Development Companies today have a lot of information on consumer preferences and media habits (Iyer, David and Miguel 2005), so targeted advertising can be executed to increase the interest of specific consumers, thereby increasing advertising effectiveness. Currently, there are three general targeting strategies on video platform: 1) contextual targeting, 2) popular content targeting, and 3) demographic and time targeting which can be classified as audience targeting. According to various targeting strategies, consumers view advertisements with different degrees of relevance to themselves when consuming media. As for devices, PC and mobile have some differences in internet usage. PC is effective for multitasking and thus PC users are exploration-oriented, so new information and interest provided by advertisements are important factors in determining ad completion. Exploration-oriented users are highly open to information that they did not know, and thus show relatively high interest and openness to advertisements that are not relevant to them. However, mobile is less convenient for multitasking than PC and thus mobile users are goal-orientated, so not only new information and interest provided by advertisements but also perceived intrusiveness caused by advertisements are important factors in determining ad completion. Goal-oriented users focus more on their tasks, and thus show relatively low interest and openness to advertisements that are not relevant to them. Based on the characteristics of targeting strategies and devices, we hypothesize the interplay of targeting strategies and devices on the effectiveness of food advertisement as follows: [H1] When watching video content on PC, popular content targeting in comparison with contextual targeting has a positive effect on ad completion. [H2] When watching video content on PC, audience targeting in comparison with contextual targeting has a positive effect on ad completion. [H3] When watching video content on mobile, the PC-based positive effect of popular content targeting compared to contextual targeting decreases. [H4] When watching video content on mobile, the PC-based positive effect of audience targeting compared to contextual targeting decreases. 3. Data and Analysis To verify the research hypothesis discussed above, the authors obtained users

A Risk Assessment Framework for Critical Infrastructure Based on the Analytic Hierarchy Process

Due to their essential role, critical infrastructures (e.g., water, gas, and power distribution systems) are subject to persistent monitoring in order to ensure their operational continuity. Because of this, they constitute appealing targets for malicious attackers who carry out physical or cyber attacks with the aim of compromising such critical systems. In this work, we provide a novel framework for an enhanced risk assessment process for critical infrastructures, which is based on the Analytic Hierarchy Process. Specifically, the proposed solution consists of a quantitative framework for site-specific risk assessment, and follows an approach designed to consider the presence of heterogeneous subsystem characterized by different degree of relevance in the infrastructures. A simulation campaign is carried out in a test-range environment, which emulates the behaviour of a water treatment system, in order to prove the effectiveness of the approach.

What influences individual perception of health? Using machine learning to disentangle self-perceived health

Self-perceived health is a subjective health outcome that summarizes all the health conditions and is widely used in population health studies. Yet, despite its well-known relationship with survival, it is still unclear as to which health conditions are actually taken into account when making an individual assessment of one's own health. The aim of this paper is to assess the influence of four objective health conditions – IADLs, ADLs, chronic diseases, and depression – in predicting self-perceived health among Europeans by age group (50–64 and 65–79) and by sex.Classification trees (J48 algorithm), which pertains to the emerging Machine Learning techniques, were applied to predict self-perceived health according to the four abovementioned objective health conditions of European individuals in the sixth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) (n = 55,611).The four variables present different degrees of relevance in establishing predictions of self-perceived health values by age and by sex. Before the age of 65, chronic diseases have the greatest importance, while IADL limitations are more important in the 65–79 age group. Likewise, ADL limitations are more important for women free of chronic diseases in the 50–64 age group; however, these differences disappear among women in the older group.There is an evident degree of interplay between the objective health indicators of chronic diseases, ADLs, IADLs, and depression when predicting self-perceived health with a high level of accuracy. This interplay implies that self-perceived health summarizes different health conditions depending on age. Gender differences are only evident for the younger age group, whereas construction of self-perceived is the same for women and men among the older group. Therefore, none of these four indicators on its own is able to totally substitute self-perceived health.

ВРЕМЕННАЯ И ДИСКУРСИВНАЯ ВАРИАТИВНОСТЬ КОНЦЕПТА «БОГУЧАНСКАЯ ГЭС»

Statement of the problem. One of the postulates of the modern cognitive-discursive paradigm is the thesis about the variability of structure and content of the concept. The variability of the concept is influenced by many factors: temporal, socio-cultural, discursive, ecological, political, and individual-psychological. The relevance of the study of variability of the concept is due to a number of reasons: 1) the need for theoretical justification of the heterogeneity of concepts within the same linguoculture; 2) the possibility of revealing the ratio of basic and variable informational and axiological structures in the content of the concept; 3) the need to establish the connection of cognitive mechanisms with worldview and with the emotional reflection of the individual and / or social groups; 4) the importance of studying the factors of “constructing reality” by actualizing those or other sides of the concept. The purpose of the article is to consider the variation of the value and semantic content of the “Boguchanskaya HPP” concept, to determine the causes and factors of variation of units of expression of figurative and value layers of this concept. In order to achieve this goal, linguistic, stylistic and linguopragmatic means of expression of the “Boguchanskaya HPP” concept in different types of discourse are analyzed. The material of the research. The empirical basis of the study is the data of the National Corpus of the Russian language; materials of publications in the media about Boguchanskaya HPP, placed in the section “Press” on the main website of the hydropower plant (http://www.boges.ru/press-tsentr/smi-o-boguchanskoy-ges/publikatsii-v-presse/); publications of the website “Dam. No!” (http://www.plotina.net/); transcripts of interviews and reminiscences of displaced people from the flood zone of Boguchanskaya HPP as well as people permanently residing in Boguchansky and Kezhemsky districts of the Krasnoyarsk region. As a result of a sample listing of written and oral texts about Boguchanskaya HPP, more than 400 contexts of conceptual, figurative and axiological attributes of this concept were written out. The research methodology is as follows: in order to achieve the goal the method of conceptual analysis developed by representatives of the linguocultural direction, as well as the interpretive analysis and the method of linguistic description of the results are used. Research results. The “Boguchanskaya HPP” concept refers to dynamic, actively translated concepts of national linguoculture. It is distinguished by high modifying potential, discursive variability of its structure, and value and semantic content. Variability of the content of this concept is manifested, first of all, in different degrees of relevance of those or other conceptual features at different stages of its life cycle, as well as ambivalence of evaluations which determine the content of axiological layer of the “Boguchanskaya HPP” concept in conversational discourse, ecological Internet discourse and official mass-media discourse. The reason for the variability of the value and semantic content of the concept are socio-economic, socio-cultural and civilizational factors.

Context-aware item attraction model for session-based recommendation

Session-based recommendation uses existing items in users’ interaction sessions to predict the next items with which users will interact. The existing items in sessions usually have different degrees of relevance with each other, and this item relevance also reflects users’ interests. Moreover, when sessions are represented in different structural forms, there will be different types of relevance between items, an aspect typically neglected by previous work. In this paper, we propose a novel Context-aware Item Attraction Model (CIAM) for session-based recommendation, which is capable of capturing different types of relevance between items in order to obtain users’ general and temporal interests and predict the next items in sessions. First, we convert sessions into local and global undirected graphs to mine the item adjacency relevance within and across sessions in order to better determine users’ general interests. Second, we retain the natural sequence structure of sessions, and model the transition relevance between items in sessions to get users’ temporal interests. Third, we design a context-aware item embedding method to obtain the embedding of each item; this method utilizes superposition and a weighted graph convolutional network to aggregate the context information from both the item’s features and the item’s neighborhood. Finally, based on users’ general and temporal interests, as well as the context-aware embeddings of items, we predict the next items with which users will interact during a session. The proposed model is then extensively evaluated on two real-world datasets. Experimental results show that our model outperforms the state-of-the-art baseline methods. Through the analysis of the experiments, we prove that our model can effectively capture the different types of relevance between items within and across sessions for accurately modeling user interests, therefore improving recommendation performance.

Detail or Disclosure – Towards a Visualisation of Confidentiality Related Spatial Damage to Demographic Grids

Abstract. Statistical data on demography is the basis for many population-related scientific questions, economic questions of health care and questions of planning public services. Population data in equal-area cartographic grid cells appears to be a good basis, especially for use cases in inter-municipal contexts of administration and planning (Specht et al. 2019). Census results have been used, since the 2011 census made available small-scale population data for the entire Federal Republic of Germany on a 100m grid for the first time. Unfortunately, this data is not updated by the statistical offices.This presentation describes a use case of demographic grids implemented in a context of inter-municipal cooperation in the region of Bremen. As the calculation of population forecasts was an objective, small-scale data on migration was required. Similar to the approach in the census, demographic data and data on migration are recorded in the residents' registration offices (EMA) of the cooperating municipalities. However, since outside the census other legal frameworks apply, the process cannot be adopted as is. In the EMAs, individual-related micro-data are available, serving as a base file. Under the respective legal framework, the data is anonymised, geo coded and converted into an aggregated tabular form on site.Aggregated data may still contain individual cases worthy of protection. The higher the number of queried characteristics (region, gender, age, nationality etc.) and their differentiation (100m grid or 1km grid, age years or age groups, etc.), the higher the probability to encounter such cases. A number of procedures for statistical disclosure control are available, of which the SAFE procedure (Höhne 2015) (used in the 2011 census) is currently implemented in the project. As other methods or strategies are up for consideration, how can they be evaluated in a specific regional context? From the perspective of confidentiality, space is at first just one feature dimension among others, although there are approaches that explicitly take spatial interrelation into account (Young, Martin, and Skinner 2009).From a geographical point of view, however, high resolution data, especially in sparsely populated areas, can generally be expected to show high before-and-after deviations as a result of confidentiality procedures. Depending on the subject matter, these spatial errors can have different degrees of relevance and thus be ultimately relevant for the selection of the confidentiality strategy. To support a decidedly spatial comparison of the effects of different classification, aggregation and confidentiality strategies, a set of indicators together with an interactive visualization for the project area under consideration is presented for discussion.

Heterogeneous programmed death-ligand 1 expression in gastric cancer: comparison of tissue microarrays and whole sections

BackgroundProgrammed death-ligand 1 (PD-L1) expression determines the eligibility for anti-PD-1 treatment in patients with advanced gastric cancer, but evidence indicates that PD-L1 staining is heterogeneous. Patients who are ineligible for radical surgery could be tested for PD-L1 expression with biopsy staining, but it is unclear if a small biopsy is representative of the PD-L1 status of the whole tumor. The aim of our study was to determine how many biopsy specimens are needed to accurately reflect the objective status of PD-L1 expression in whole sections.MethodsWe built tissue microarrays (TMAs) as substitutes for core biopsies, collecting 6 cores per case from 152 gastric cancer specimens. All of the slides and TMAs underwent PD-L1 immunohistochemical staining, and PD-L1 expression in at least 1% of tumor cells or immune cells was defined as positive.ResultsIt was necessary to randomly select multiple cores from TMAs to reach a suitable agreement rate (> 90%) and Cohen’s κ value (> 0.8) between TMAs and whole sections. We defined the PD-L1 staining status from the whole section as the standard. The evaluation of five randomly selected cores from TMAs agreed well with the evaluation of whole sections. The sensitivity, specificity and the area under the curve (AUC) of the receiver-operating characteristic (ROC) were 0.93, 0.92, and 0.922 (95% confidence interval (CI) 0.863–0.982), respectively.ConclusionsWe conclude that PD-L1 expression among TMA samples had different degrees of relevance to the corresponding surgical specimens, which indicates that at least five biopsies might be necessary to characterize patients taking anti-PD-1 treatment.

Diffusion as a future perspective of agenda setting

Abstract The changing media environment has led to fragmentation of both media and audiences and thus to new flows of news items across channels and time. It has become challenging to clearly distinguish public and media agendas and their relationships. Our traditional way of counting single news items to determine an issue’s importance does not apply online. Articles are not scheduled into fixed publication cycles, they are moved, updated, or shared. Moreover, the variety of channels through which users encounter news loosens the connection between news items and their original source. We suggest “diffusion” as a concept to identify agenda-setting effects. News items are assigned different degrees of relevance by users clicking, liking, or sharing it. In turn, such relevance can affect others in assigning prominence to news items. The diffusion process results in a shared agenda which is constantly modified and updated by the entire community of online actors.

An efficient page ranking approach based on vector norms using sNorm(p) algorithm

In the whole world, the internet is exercised by millions of people every day for information retrieval. Even for a small to smaller task like fixing a fan, to cook food or even to iron clothes persons opt to search the web. To fulfill the information needs of people, there are billions of web pages, each having a different degree of relevance to the topic of interest (TOI), scattered throughout the web but this huge size makes manual information retrieval impossible. The page ranking algorithm is an integral part of search engines as it arranges web pages associated with a queried TOI in order of their relevance level. It, therefore, plays an important role in regulating the search quality and user experience for information retrieval. PageRank, HITS, and SALSA are well-known page ranking algorithm based on link structure analysis of a seed set, but ranking given by them has not yet been efficient. In this paper, we propose a variant of SALSA to give sNorm(p) for the efficient ranking of web pages. Our approach relies on a p-Norm from Vector Norm family in a novel way for the ranking of web pages as Vector Norms can reduce the impact of low authority weight in hub weight calculation in an efficient way. Our study, then compares the rankings given by PageRank, HITS, SALSA, and sNorm(p) to the same pages in the same query. The effectiveness of the proposed approach over state of the art methods has been shown using performance measurement technique, Mean Reciprocal Rank (MRR), Precision, Mean Average Precision (MAP), Discounted Cumulative Gain (DCG) and Normalized DCG (NDCG). The experimentation is performed on a dataset acquired after pre-processing of the results collected from initial few pages retrieved for a query by the Google search engine. Based on the type and amount of in-hand domain expertise 30 queries are designed. The extensive evaluation and result analysis are performed using MRR, Precision@k, MAP, DCG, and NDCG as the performance measuring statistical metrics. Furthermore, results are statistically verified using a significance test. Findings show that our approach outperforms state of the art methods by attaining 0.8666 as MRR value, 0.7957 as MAP value. Thus contributing to the improvement in the ranking of web pages more efficiently as compared to its counterparts.

Availability of Informal Caregivers for Palliative Care Patients with Cancer: Is there a Difference between Higher- and Lower-Income Settings.

Objective:Family caregivers are the default caring personnel for terminal cancer patients. The characteristics, demographics, distribution, psychological burden, and socioeconomic standards differ between high- and low-income countries. We aimed to assess those factors and their direct reflection on both the patient and the caregiver.Patients and Methods:This is a comparative cross-sectional study for terminal cancer patients in the palliative care unit between the United Kingdom (UK) as a high-income community and Egypt as a low-income community. We assessed the different characteristics, demographics, living place, the degree of relevance, and the availability of caregivers.Results:We have recruited 216 patients from the UK and 117 patients from Egypt. Informal caregivers were available in 74.5% and 92.3% for these patients with a mean age of 71.5 (standard deviation [SD] 16) years and 50.9 (SD 15.18) years, respectively. There has been a statistically significant difference between the two countries’ caregivers in being married, family, and living in the same household (P < 0.0001).Conclusion:Low-income countries are more common to have an informal caregiver who is a family member of different degree of relevance. Caregivers in low-income settings tend to be younger, of the female gender, married, and living in the same household than in high-income ones.

Value-relevance of disclosure: risk factors and risk management in Brazilian firms

ABSTRACT This study investigated whether the disclosure in accounting reports of information on risk and its management affects, at the margin, firm valuations in the Brazilian capital market. It questioned the existence of value relevance in the disclosure of information related to risk factors and risk management structures. The importance of the topic arises from the documentation that disclosure on risk factors plays a statistically significant role in valuing firms in a context marked by economic and political uncertainty, as is the case of the Brazilian market. The findings confirmed that information about risk shows informational relevance for firm valuations. Information on risk management in a firm, in turn, was not shown to be significant in terms of affecting company share valuations. The investigation of detailed reports with different risk factors indicated that individualized information shows different degrees of relevance for company valuations. It is inferred that the disclosure of risk factors affects the perception of investors, who also base their estimates of return on the availability and detail of such information. It is also observed that information on risk management structure was not shown to be value relevant; moreover, few firms published information on the establishment of this type of entity in their management structures. The residual income valuation (RIV) model (Ohlson, 1995) was applied using regressions with panel data estimation related to the three years covering 2012 to 2014. The sample, which was randomly delimited, was formed of 100 companies. The data on disclosure of risk and disclosure of risk management were collected from the companies’ Reference Forms, available from the website of the São Paulo Stock, Commodities, and Futures Exchange (BM&amp;FBOVESPA). As an impact on the area of knowledge, broader discussions are expected regarding the utility of disclosure on the topic related to risk factors and the existence of a specific body responsible for risk management in firms, according to investors’ perceptions. It also contributes by providing evidence about the quality of disclosure related to risk (factors and management) and the perception of the capital market regarding the informativeness and relevance of such elements.

Effect of Sponsored Listings on Online Marketplaces: The Role of Information Asymmetry

Advertising on e-commerce marketplaces, wherein sponsored product listings are interleaved with organic product listings, is a large and growing phenomenon. In this paper, we both theoretically and empirically study whether including sponsored listings improves or hurts the overall quality and relevance of the set of products displayed to consumers. Our theoretical analysis reveals that the relevance of the ads to the consumers' search queries depends on the level of information asymmetry between the marketplace owner and the sellers who sell products with different degrees of relevance. Specifically, when information asymmetry is low (high), i.e., the platform can (cannot) easily distinguish between high- and low-relevance sellers, then low-relevance (high-relevance) sellers have a greater incentive to advertise. However, even when low-relevance products are displayed as ads, consumers end up finding well-matching products as long as search and evaluation costs are reasonable; therefore, the overall impact on sales is relatively small while the marketplace benefits from the additional revenue from selling ads. We obtain data from a large-scale field experiment run at Flipkart, a leading online marketplace in India, and find that various empirical patterns implied by our theoretical results hold in the data. Our study provides several practical implications for managers of online marketplaces.

IR evaluation methods for retrieving highly relevant documents

This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in modem large IR environments. The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) two novel measures computing the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. We then demonstrate the use of these evaluation methods in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system (In- Query I) in a text database consisting of newspaper articles. The results indicate that the tested strong query structures are most effective in retrieving highly relevant documents. The differences between the query types are practically essential and statistically significant. More generally, the novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods.

‘Managing successful change in the public sector’: a view from the consultants’ world

ABSTRACTOur paper presents the results of a survey on the perceptions of 680 Italian public-sector consultants on the drivers of successful organizational change according to Fernandez and Rainey’s model. The results show that the consultants mostly confirm the model, though recognizing that the various drivers have different degrees of relevance. A clear vision and plan for change and the top management’s commitment are seen as central in change processes, whereas interestingly, resource availability and a comprehensive approach to change appear to play a less relevant role.

A Survey on Feature Weighting Based K-Means Algorithms

In a real-world data set, there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analyzing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched. However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub-industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories.

Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

We consider the Weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different weights at different clusters. Thus, it supports the intuitive idea that features may have different degrees of relevance at different clusters. We use the Minkowski metric in a way that feature weights become feature re-scaling factors for any considered exponent. Moreover, the traditional Silhouette clustering validity index was adapted to deal with both numerical and categorical types of features. Finally, we show that our new method usually outperforms traditional K-Means as well as the recently proposed WK-DC clustering algorithm.

Recovering the number of clusters in data sets with noise features using feature rescaling factors

In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features. Our method obtains feature re-scaling factors taking into account the structure of a given data set and the intuitive idea that different features may have different degrees of relevance at different clusters.We experiment with the Silhouette (using squared Euclidean, Manhattan, and the pth power of the Minkowski distance), Dunn’s, Calinski–Harabasz and Hartigan indexes on data sets with spherical Gaussian clusters with and without noise features. We conclude that our methods indeed increase the chances of estimating the true number of clusters in a data set.