Irrelevant Results Research Articles

Business cycle fluctuations in Mirrlees economies: The case of i.i.d. shocks

I consider a real business cycle model in which agents have private information about the i.i.d. realizations of their value of leisure. For the case of logarithmic preferences I provide an analytical characterization of the solution to the associated mechanism design problem. Moreover, I show a striking irrelevance result: that the stationary behavior of all aggregate variables are exactly the same in the private information economy as in the full information case. Numerical simulations indicate that the irrelevance result approximately holds for more general preferences.

The Impact of Customer Satisfaction with EWOM and Brand Equity on E-Commerce Purchase Intention in Indonesia Moderated by Culture

The digital era, which increasingly becomes massive in people's lives, impacts the traditional sales methods. E-commerce companies compete in winning the competition to create superior customer value. An effective and efficient marketing strategy is needed that can influence customer purchase intention. The research focused on the effect of customer satisfaction, which created eWOM and increased brand equity on purchase intention by adding culture (collectivist culture) as the moderator variables. The research used a quantitative approach. The data were obtained from 280 respondents selected by non-probability sampling. Respondents were customers who had already bought products through e-commerce in Indonesia. The questionnaire instrument was adopted based on theories from the previous researchers. The data used the Likert scale and were analyzed using the SmartPLS 3.0. The results state a significant relationship between the independent and dependent variables. All independent variables have a significant influence on the dependent variable. However, culture as the moderator variable cannot get significant results in the relationship between all the variables. It gets irrelevant results. Hence, the results show that the moderator variable is more appropriate as the independent variable. It can directly affect purchase intention. The research contributes to testing culture as the moderator variable that makes the independent variables generalize the findings.

A hybrid approach towards content-based image retrieval for colored images using enhanced first type of pessimistic covering based lower approximation multi-granular rough sets

Content-based image retrieval (CBIR) is used to follow a strategy which allows getting the desired images as identified by means of given input to the maximum extent possible, but in most of the occasions the result is bounded nearest to the expected but not exactly. In other words the result is nearest as along with the nearest output the user has maximum chances of getting other irrelevant results too. To overcome or to reduce this kind of situation we have decided to go with a method which accepts the input from the user and accordingly the images are classified into two covers based on the interest of the input. Further the first cover is resulted after applying a procedure which performs the task of averaging the cardinality of the given input. The second cover is resulted with some valuable values in the form of content which fulfills the nature of expected second input with the help of applying a moments oriented technique. The input from these two covers are passed to the next phase where the proposed algorithm is applied and which results into much filtered form of output and which further ease the task of proposed matching algorithm. The experiments are tested and validated using Corel dataset and the results proves that the proposed approach is looking better and sometimes equal with the other compared methods.

Using the Google™ Search Engine for Health Information: Is There a Problem? Case Study: Supplements for Cancer.

ABSTRACTWe assessed the quality of online health and nutrition information using a Google™ search on “supplements for cancer”. Search results were scored using the Health Information Quality Index (HIQI), a quality-rating tool consisting of 12 objective criteria related to website domain, lack of commercial aspects, and authoritative nature of the health and nutrition information provided. Possible scores ranged from 0 (lowest) to 12 (“perfect” or highest quality). After eliminating irrelevant results, the remaining 160 search results had median and mean scores of 8. One-quarter of the results were of high quality (score of 10–12). There was no correlation between high-quality scores and early appearance in the sequence of search results, where results are presumably more visible. Also, 496 advertisements, over twice the number of search results, appeared. We conclude that the Google™ search engine may have shortcomings when used to obtain information on dietary supplements and cancer.

Ontology based Semantic Query Expansion for Searching Queries in Programming Domain

Information on the web is growing rapidly; learning programming languages has become one of the most widely searched topics. Programmers and people of several backgrounds are now using Web search engines to acquire programming information, including information about a specific language or topic. Nonetheless, due to a lack of programming knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, so they used to write short queries, which leads to vocabulary problems and contextless query. Besides, semantics is almost neglected in traditional search query since it is just keyword-based searches, which deem their search to be imprecise with irrelevant results, due to the use of unclear keywords. A Semantic query expansion method is proposed for disambiguating queries in computer programming domain using ontology. The integration of Cosine similarity method into the proposed model improved the expanded query, and accordingly, the searching results. The proposed system has been tested with several ambiguous and misspelled queries and the generated extensions proved to retrieve more relevant results, when applied to a search engine. The quality of the retrieved results for the expanded queries, are much higher than that for the crude queries. The proposed technique was implemented, and then tested with external independent testers. They confirmed the mechanical test results and displayed its improvement with an average of precision @10 82.2% and 91.1% respectively. The results were promising and therefore open further research directions.

Knowing Where to Leverage: Context-Aware Graph Convolutional Network With an Adaptive Fusion Layer for Contextual Spoken Language Understanding

Spoken language understanding (SLU) systems aim to understand users’ utterance, which is a key component of task-oriented dialogue systems. In this paper, we focus on improving the contextual SLU. The contextual SLU systems mainly focus on how to effectively incorporate dialog context information (contextual information). The existing approaches all use the same contextual information to guide slot filling at all tokens, which may inject the irrelevant information and result in ambiguity. To tackle this problem, we propose a context-aware graph convolutional network (GCN) with an adaptive fusion layer for contextual SLU. The context-aware GCN is proposed to automatically aggregate the contextual information, which frees our model from the manually designed heuristic aggregation function. Meanwhile, an adaptive fusion layer is applied at each token to dynamically incorporate relevant contextual information, which achieves a fine-grained contextual information transfer to guide the token-level slot filling. Experiments on the Simulated Dialog Dataset show that our model achieves state-of-the-art performance and outperforms other previous methods by a large margin (+3.67% on Sim-R, +4.18% on Sim-M and +3.75% on Overall dataset). In addition, we explore and analyze the pre-trained model (i.e., BERT) in our framework. We show that incorporating BERT brings a large improvement in low-resource setting.

Correction Notice

In “The Professionalization of Academic Advising: A Structured Literature Review”, published in the NACADA Journal, 39(1), there was an error in reporting the data collection processes. The sentence in question (on page 91) reads, “The related terms profession, professionalism, and profession (which captures every permutation of the key term) were tested but produced irrelevant results.” The second instance of “profession” should have an asterisk attached to it (profession*). The asterisk at the end of a keyword in a search is called an asterisk wildcard. The root word is searched with different word endings; in this case, the root word is profession. Thus, the sentence should read, “The related terms profession, professionalism, and profession* (which captures every permutation of the key term) were tested but produced irrelevant results.”

PolInSAR Coherence and Entropy‐Based Hybrid Decomposition Model

AbstractTarget characterization is an essential aspect of polarimetric decomposition. This technique is capable of categorizing polarimetric signatures for different types of targets based on the scattering mechanisms they follow, enabling straightforward physical interpretation of the targets. The geometric anomalies associated with human‐made targets escalate the degree of randomness in the scattering process, which causes scattering ambiguity for such targets. The second‐order model descriptors do not relate to the actual physical structure and yield predominant volume scattering power. Such urban targets are decomposed as natural targets leading to irrelevant decomposition results. The methods developed to curb the problem are unable to maintain the consistency in the decomposition modeling as they underestimate volume scattering powers for natural landcover. A hybrid decomposition model is proposed herein to solve the problem of predominant volume scattering observed from urban targets by preserving volume scattering powers for natural targets. The model uses eigenvalue‐based decomposition parameters and polarimetric interferometric synthetic aperture radar (PolInSAR) coherence to decompose ambiguous targets. The proposed model has been tested on NISAR UAVSAR PolInSAR data acquired over the Greenville region, MS, USA. The proposed model has increased the double‐bounce scattering from the urban targets and enhanced the volume scattering from natural landcover as well. By comparing the results with existing decomposition models, it is observed that the proposed model gives a more robust representation of the landcover than the compared decomposition models.

MONEY AND CREDIT REMIX

Money and credit are ubiquitous in actual economies, but there is an active theoretical debate on whether they are both necessary if they can both be used in all transactions. Recently, Gu et al. (2016) have shown that money and credit cannot be simultaneously essential and debt limits do not matter for the determination of real allocations in a class of monetary economies. In this paper, we revisit their irrelevance result in a monetary economy based on Lagos and Wright (2005), which exhibits a misallocation of liquidity that is common in search models of money. We show that monetary loans, which naturally require the use of both money and credit, implement Pareto superior allocations in which the size of debt limits matters.

Topic modeling in short-text using non-negative matrix factorization based on deep reinforcement learning

Topic modeling for short texts is a challenging and interesting problem in the machine learning and knowledge discovery domains. Nowadays, millions of documents published on the internet from various sources. Internet websites are full of various topics and information, but there is a lot of simila rity between topics, contents, and total quality of sources, which causes data repetition and gives the user the same information. Another issue is data sparsity and ambiguity because the length of the short text is limited, which causes unsatisfactory results and give irrelevant results to end-users. All these mentioned issues in short texts made an interesting topic for researchers to use machine learning and knowledge discovery techniques to discover underlying topics from a massive amount of data. In this paper, we propose a combination of deep reinforcement learning (RL) and semantics-assisted non-negative matrix factorization model to extract meaningful and underlying topics from short document contents. The main objective of this work is to reduce the problem of repetitive information and data sparsity in short texts to help the users to get meaningful and relevant contents. Furthermore, our propose model reviews an issue of the Seq2Seq approach based on the reinforcement learning perspective and provides a combination of reinforcement learning and SeaNMF formulation using the block coordinate descent algorithm. Moreover, we compare different real-world datasets by using numerical calculation and present a couple of state-of-art models to get better performance on short text document topic modeling. Based on experimental results and comparative analysis, our propose model outperforms the state of art techniques in terms of short document topic modeling.

Barriers and facilitators to use of a clinical evidence technology in the management of skin problems in primary care: insights from mixed methods.

Objective:Few studies have examined the impact of a single clinical evidence technology (CET) on provider practice or patient outcomes from the provider's perspective. A previous cluster-randomized controlled trial with patient-reported data tested the effectiveness of a CET (i.e., VisualDx) in improving skin problem outcomes but found no significant effect. The objectives of this follow-up study were to identify barriers and facilitators to the use of the CET from the perspective of primary care providers (PCPs) and to identify reasons why the CET did not affect outcomes in the trial.Methods:Using a convergent mixed methods design, the authors had PCPs complete a post-trial survey and participate in interviews about using the CET for managing patients' skin problems. Data from both methods were integrated.Results:PCPs found the CET somewhat easy to use but only occasionally useful. Less experienced PCPs used the CET more frequently. Data from interviews revealed barriers and facilitators at four steps of evidence-based practice: clinical question recognition, information acquisition, appraisal of relevance, and application with patients. Facilitators included uncertainty in dermatology, intention for use, convenience of access, diagnosis and treatment support, and patient communication. Barriers included confidence in dermatology, preference for other sources, interface difficulties, presence of irrelevant information, and lack of decision impact.Conclusion:PCPs found the CET useful for diagnosis, treatment support, and patient communication. However, the barriers of interface difficulties, irrelevant search results, and preferred use of other sources limited its positive impact on patient skin problem management.

Representative Sets and Irrelevant Vertices

We continue the development of matroid-based techniques for kernelization, initiated by the present authors [47]. We significantly extend the usefulness of matroid theory in kernelization by showing applications of a result on representative sets due to Lovász [51] and Marx [53]. As a first result, we show how representative sets can be used to derive a polynomial kernel for the elusive ALMOST 2- SAT problem (where the task is to remove at most k clauses to make a 2- CNF formula satisfiable), solving a major open problem in kernelization. This result also yields a new O(√log OPT)-approximation for the problem, improving on the O(√log n)-approximation of Agarwal et al. [3] and an implicit O(log OPT)-approximation due to Even et al. [24]. We further apply the representative sets tool to the problem of finding irrelevant vertices in graph cut problems, that is, vertices that can be made undeletable without affecting the answer to the problem. This gives the first significant progress towards a polynomial kernel for the MULTIWAY CUT problem; in particular, we get a kernel of O( k s+1 ) vertices for MULTIWAY CUT instances with at most s terminals. Both these kernelization results have significant spin-off effects, producing the first polynomial kernels for a range of related problems. More generally, the irrelevant vertex results have implications for covering min cuts in graphs. For a directed graph G=(V,E) and sets S, T ⊆ V , let r be the size of a minimum ( S,T )-vertex cut (which may intersect S and T ). We can find a set Z ⊆ V of size O(|S| . |T| . r) that contains a minimum ( A,B )-vertex cut for every A ⊆ S , B ⊆ T . Similarly, for an undirected graph G=(V,E) , a set of terminals X ⊆ V , and a constant s , we can find a set Z ⊆ V of size O(|X| s+1 ) that contains a minimum multiway cut for every partition of X into at most s pairwise disjoint subsets. Both results are polynomial time. We expect this to have further applications; in particular, we get direct, reduction rule-based kernelizations for all problems above, in contrast to the indirect compression-based kernel previously given for ODD CYCLE TRANSVERSAL [47]. All our results are randomized, with failure probabilities that can be made exponentially small in n , due to needing a representation of a matroid to apply the representative sets tool.

Query reformulation approach using domain specific ontology for semantic information retrieval

Conventional search engines provide a lot of irrelevant results to the user’s search query, which consumes a lot of time and effort, as the context and semantics of the request made by the user are not analyzed to the optimum extent. This highlights for the need of a methodology embedded in the search machinery, enabling semantic web search. Ontologies have proven to be an effective technology for such semantic knowledge representation and information retrieval. In this research paper, the researcher has constructed a string ontology in a new domain (the music domain) from scratch using Protege 5.0 for semantic information retrieval for a query in the search engine. This ontology is further used in the proposed ontology based semantic information retrieval method (OBSIRM), which has been built to refine the web search in the music domain. The researcher has proposed a novel approach to refine the web search in which the query is first replaced with abbreviations along with the use of the multilingual concept to search a query. The results of the proposed method have been compared with Google. The accuracy and efficiency of the proposed method has been measured in terms of precision and recall. The average precision and recall of OBSIRM is 1.43 and 0.7 as compared to generic Google search engine i.e. 1.04 and 0.51. Hence it can safely be concluded that in the domain of research the OBSIRM performs better in terms of users’ expectations than Google (for string instrument domain considered by the researcher) as far as relevant information retrieval is concerned.

Development of Research Proposal Selection Based on Domain Ontology using K-Means Categorical Clustering

With the prompt improvement in research progress of various zones, selection of research proposals became a remarkable methodology in many research funding agencies and organizations. When a less number of research proposals are received, then it is ease to cluster the research proposals and the selection process became as non-problematic way. If a number of research proposals elevated, then the clustering and selecting the proposals became complicated. In current system, proposals grouping is done in manual-based or along with their similarities in subject disciplinaries which yield irrelevant results in some cases. The main goal of this research work is to develop an enhanced system in selection of research proposals based on Domain ontology, where the ontology acts as a searching criteria for the topics of research proposals. This proposed system will help to select the topics of research proposals in well-systematic way without the interference of manual progression. In this paper, an algorithm is proposed as Scikit-learn K-means Multiclass Document Clustering(SKMDC) to group each subject discipline according to their sub-topics and sub-domains. Here, the k-means clustering technique is implemented on categorical data to implement the clustering process. As, the categorical data are not able to applied directly in K-means clustering algorithm, the LabelEncoder method is implemented to encode the text data to numerical values and the dimensions of a dataset are reduced using Principal Component Analysis. This paper also overwhelms the weaknesses of k-means technique in specification of cluster number in initial stage. It is done through the determination of optimal number of clusters by using Elbow Curve method and it is cross-validated through Silhouette Score analysis.

Concealed Penis after Circumcision: Is It Beneficial in Lowering Uropathogenic Colonization in Penile Skin and Preventing Recurrence of Febrile Urinary Tract Infections?

To discuss whether concealed penis after circumcision lowers perimeatal urethral and glanular sulcus uropathogenic bacterial colonization in healthy boys with no urinary tract problems and prevents attacks of febrile urinary tract infections in non-healthy boys with defined urinary tract abnormalities. Materials and Methods: This case-control study was conducted in Ibn-i Sina Hospital and retrospectively collected data of 471 boys were analyzed. All patients were scanned for any urinary tract abnormality and those with any defined abnormalities were classified as non-healthy group. (123 patients) Non-healthy patients were divided into two subgroups as concealed (n:31) and non-concealed (n:92) penis after circumcision. Healthy patients with no urinary problems were divided into three groups as circumcised without concealed penis (n:144), with concealed penis after circumcision (n:104) and uncircumcised control group (n:100). Bacterial cultures were obtained from both periurethral meatal and glanular sulcus areas by adhering strictly to the rules of obtaining bacterial culture to avoid false-positive or negative culture results. Also only uropathogenic bacterias were evaluated, irrelevant results were excluded. Results: Mean age was similar in healthy population. Comparison of three groups showed that there was a significant difference in both cultures.(P = .026 for periurethral meatal region, P = .039 for glanular sulcus region) In post hoc analysis, non-concealed group had a lower rate of culture positivity in both areas compared to other groups. Mean age was also similar in non-healthy population. Mean follow-up period was 18.2 months. Patients with concealed penis after circumcision had a significantly higher number of febrile UTI attacks (20 attacks in 8 patients vs 7 attacks in 5 patients) compared to non-concealed group. (P = .019) All febrile UTI attacks except one in this group occurred below the age of 12 months. A total of 10 patients in both healthy and non-healthy groups had postoperative hemorrhage after circumcision and only 1 patient had a wound infection. Conclusion: Concealed penis after circumcision does not lower perimeatal urethral and glanular sulcus uropathogenic bacterial colonization in healthy patients and does not protect unhealthy patients from febrile urinary tract infection attacks. If circumcision is planned, concealed penis should be avoided and also parents should be informed about the possible risks due to concealed penis before the procedure, particularly in patients with urinary tract abnormalities.

An Improved Framework for Content-based Spamdexing Detection

To the modern Search Engines (SEs), one of the biggest threats to be considered is spamdexing. Nowadays spammers are using a wide range of techniques for content generation, they are using content spam to fill the Search Engine Result Pages (SERPs) with low-quality web pages. Generally, spam web pages are insufficient, irrelevant and improper results for users. Many researchers from academia and industry are working on spamdexing to identify the spam web pages. However, so far not even a single universally efficient method is developed for identification of all spam web pages. We believe that for tackling the content spam there must be improved methods. This article is an attempt in that direction, where a framework has been proposed for spam web pages identification. The framework uses Stop words, Keywords Density, Spam Keywords Database, Part of Speech (POS) ratio, and Copied Content algorithms. For conducting the experiments and obtaining threshold values WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets have been used. An excellent and promising F-measure of 77.38% illustrates the effectiveness and applicability of proposed method.

Gender Bias and Intergenerational Educational Mobility: Theory and Evidence from China and India

This paper incorporates gender bias against girls in the family, school and labor market in a model of intergenerational persistence in schooling where parents self-finance children's education because of credit market imperfections. Parents may underestimate a girl's ability, expect lower returns, and assign lower weights to their welfare (pure son preference). The model delivers the widely used linear conditional expectation function under constant returns and separability but generates an irrelevance result: parental bias does not affect relative mobility. With diminishing returns and complementarity, the conditional expectation function can be concave or convex, and parental bias affects both relative and absolute mobility. This paper tests these predictions in India and China using data not subject to coresidency bias. The evidence rejects the linear conditional expectation function in rural and urban India in favor of a concave relation. Girls in India face lower mobility irrespective of location when born to fathers with low schooling, but the gender gap closes when the father is college educated. In China, the conditional expectation function is convex for sons in urban areas, but linear in all other cases. The convexity supports the complementarity hypothesis of Becker et al. (2018) for the urban sons and leads to gender divergence in relative mobility for the children of highly educated fathers. In urban China, and urban and rural India, the mechanisms are underestimation of the ability of girls and unfavorable school environment. There is some evidence of pure son preference in rural India. The girls in rural China do not face bias in financial investment by parents, but they still face lower mobility when born to uneducated parents. Gender barriers in rural schools seem to be the primary mechanism.

Schistosomiasis and Bladder Cancer in Egypt: Truths and Myths.

BACKGROUND:The gathered archeopathological evidence has confirmed that Schistosomiasis has been endemic in Ancient Egypt for over 500 decades. The association between Schistosoma hematobium and increase bladder cancer risk is also well acknowledged. However, over the years, there is a proved changing pattern of bladder cancer that needs to be investigated.AIM:We aim to discuss the truths and myths about bladder cancer and its association with Schistosomiasis in Egypt.METHODS:A cross-sectional, case-control study was performed to collect recent data on the topic.RESULTS:Of the reported cancer cases, 79.3% were transitional cell carcinoma (TCC), an additional 6% showed associated squamous features. Squamous cell carcinoma (SCC) constituted only 13.8% of cancer cases. Schistosomiasis was histologically confirmed in 19 cancer cases, only one was SCC. The relative frequency of TCC is increasing, while SCC is decreasing. There is no evidence that this pattern is related to smoking or environmental factors, as the incidence of lung cancer, is not proportionately increasing.CONCLUSION:The old concept that Schistosomiasis is associated with SCC should be revaluated as most cases are associated with TCC. Relying on the histopathology for confirmation of Schistosomiasis in our research studies appears to be non-accurate and leads to irrelevant results.

Mixed oligopoly, cost-reducing research and development, and privatisation

We develop a mixed oligopoly model to examine the role of R&D subsidies and evaluate the welfare effects of privatisation. In solving the oligopoly model we propose a novel use of aggregative games techniques. Our analysis reveals that privatisation reduces the optimal R&D subsidy. Furthermore, privatisation improves social welfare but only when the number of firms is sufficiently large. Implementing solely a subsidy to R&D does not lead to a ‘privatisation neutrality theorem’ or ‘irrelevance result’.

An Automatic Image Registration Methods for Multi-Domain Images by using Geometric Relationships

In the internet technology world, all the information is considered as an image format only. Because one image is equal to a thousand words. Automatically increasing the image database size, high resolution and including multi-domain concepts. The human perception of getting the required image is very poor during the image retrieval process. In this case, the image retrieval system is consists of many problems like more time consuming, irrelevant results of the user. Registration is necessary to be able to compare or integrate the data obtained from different measurements of the query image. Image registration is that the method of reworking totally different sets of knowledge into one organization and orienting 2 or additional pictures of constant scene. This method involves designating one image because the reference image, conjointly known as the mounted image, and applying geometric transformations or native displacements to the opposite pictures so they align with the reference. To solve the above problems by using an automatic image registration methods. In this paper, we are mainly focusing the automatic image registration methods for multi-domain images for better human understating of required results. Finally, evaluate the performance of image registration methods with length, counts of matched points and uniformly distributed points of multi-domain images for better understanding.