Stop List Research Articles

A Novel Control and Monitoring Interface Design for ROS Based Mobile Robots

In this study, an interface design was carried out in order to provide convenience to the user in the control and monitoring of the Robot Operating System (ROS) based autonomous mobile robot (AMR). Qt Designer and Python were used in the interface design. Thanks to the designed interface, autonomous and manual control of AMR was provided. Using the gmapping algorithm, the environment in the virtual world was mapped and transformed into a picture in .png format and visualized in the interface. The location information from the ROS was transferred to the said picture and the instant tracking of the AMR was done via the interface. It was shown which algorithm is used locally and globally at that moment. While in autonomous mode, the vehicle was provided to move to the previously recorded point. The total distance and time spent by the AMR while moving between two points were also calculated by the interface. The location (x, y, z) and orientations (x, y, z, w) of the previously recorded station were monitored from the stop list. On the other hand, the position (x, y, z) and orientation (x, y, z, w) information of the AMR could be followed as real time via the interface. In this way, when the AMR reaches the goal station, the time elapsed between two points, the transportation distance, settlement information such as the location and orientation of the vehicle can be tracked and be compared via the interface.

Classification of Events in Selected Industrial Processes Using Weighted Key Words and K-Nearest Neighbors Algorithm

The problem of classifying events in the industry is related to a large amount of accumulated text data including, among others, communication between the company and the client, whose expectations regarding the quality of its service are constantly growing. The currently used solutions for handling incoming requests have numerous disadvantages; they imply additional costs for the company and often a high level of customer dissatisfaction. A partial solution to this problem may be the automation of event classification; for example, by means of an expert IT system. The presented work proposes the solution to the problem of classifying text events. For this purpose, textual descriptions of events were used, which were collected for many years by companies from many different industries. A large part of text events are various types of problems reported by company customers. As part of this work, a complex text-classification process was constructed by using the K-Nearest Neighbors algorithm. The demonstrated classification process uses two novel proposed mechanisms: the dynamic extension of stop list and weighted keywords. Both of the mechanisms aim to improve the classification performance by solving typical problems that occur when using a fixed stop list and a classical keyword extraction approach by using TF or TF-IDF methods. Finally, the Text Events Categorizer system that implements the proposed classification process was described.

Obligation to Conclude a Contract with the Consumer

Special regulation of relations with the participation of citizens-consumers is typical for almost all modern developed legal orders. The current domestic legislation contains a sufficient number of guarantees for the protection of consumer rights and one of them is the obligation of a business entity to conclude an agreement with any consumer who has applied. Such a measure is a significant restriction on the freedom of contract, which raises the question of the purpose of establishing such a restriction, the interests to protect which it is aimed at, as well as the possibility of its mitigation or complete exclusion. The purpose of the article is to determine the essence and feasibility of establishing the obligation of a business entity to conclude an agreement with any consumer who has applied. The achievement of this goal is carried out by solving the following tasks: determining the prerequisites for establishing the obligation to conclude an agreement with a consumer, identifying a situation where this measure allows to protect consumer rights, establishing the reasons and appropriateness of exceptions to the rule under consideration, as well as the prospects for expanding the list of exceptions. The article was prepared using general scientific methods (systemic, logical) and special legal methods (comparative legal, formal legal). As a result of the study, the author comes to the conclusion that the consolidation of the obligation for the entrepreneur to conclude an agreement with the consumer is aimed at preserving the consumer’s access to the good, which he may be deprived of. In some cases, such a measure really allows the consumer to protect his rights, for example, concluding insurance contracts, but in most cases such a measure is excessive, because in most cases it does not lead to the protection of consumer rights, but to infringement of the interests of third parties. The author comes to the conclusion that it is necessary to provide business entities with the opportunity to establish “stop lists” in relation to those consumers who previously violated the contract.

DYNAMIC STOP LIST FOR THE GUJARATI LANGUAGE USING RULE BASED APPROACH

Text classification, document cluster, and similar endeavors area unit important analysis spaces underpinning a large vary of problems and applications within the area of internet intelligence. A typical approach, once cluster or otherwise classifying documents algorithmically, is to represent an online document as a vector of numbers derived from the frequencies of words therein document, wherever the words area unit taken from the complete assortment of documents into consideration. A tried approach is to use an inventory of “stop” words (also known as a ‘stop list’), that establish frequent words like ‘and’ and ‘or’ which might be unlikely to Words within the stop list area unit thus not enclosed within the vector that represents a document. Current stop lists area unit arguably noncurrent within the lightweight of fluctuations in word usage, and conjointly arguably innocent of sure candidate generic stop lists, thus delivery into question that we tend to explore this by developing new stop lists in an exceedingly rule based method.

Public libraries on Facebook: content analysis of Turkish public libraries' posts

PurposeThe study aims to analyze the posts shared by public libraries in Turkey on their official Facebook pages. The targets of the study are to detect content categories shared by the libraries and to describe user interaction of libraries on the basis of their posted content.Design/methodology/approachThe study examined data relating to 23,027 posts shared by 116 public libraries through descriptive and content analyses. The content analysis on the posts identified general topic and sub-topics through both machine-tagging and checks by the authors. Turkish word parsing applications and stop lists were employed in the designation of sub-categories. In this process, word clusters were identified to allow for the designation of sub-topic headings through the use of the Latent Dirichlet Allocation (LDA) technique.FindingsThe present study identified that a significant portion of the 116 public libraries in Turkey with official Facebook pages had started to use this platform in the year 2017. According to the findings, the user interaction of libraries generally occurs in the form of likes. Analyses on the topic categories of the posts established that a remarkable number of public libraries in Turkey targeted users from pre-school children to young adults.Originality/valueThe study contributes to the existing body of literature as one of the first research studies describing the current situation concerning public libraries in Turkey through the analysis of the content they generate on Facebook pages.

Algorithm for Persian Text Sentiment Analysis in Correspondences on an E- Learning Social Website

By 2000, sentiment analysis had been only studied based on speech and changes in facial expressions. Since then, studies have been focused on text. Concerning Persian text mining, studies have been conducted on the methods for extracting properties for classification and examination of opinions on social websites with an aim to determine text polarity. The present research was aimed to prepare and implement an algorithm for Persian text sentiment analysis based on the following six basic emotional states: happiness, sadness, fear, anger, surprise, and disgust. In this research, sentiment analysis was carried out using the unsupervised lexical method. Lexicons are divided into four categories, namely the emotional, boosters, negation, and stop lists. The algorithm was written in six different ways using different properties. In the first method, the algorithm was capable of identifying an emotional word in a sentence. The sentiment of the sentence was determined based on the given emotional word. However, it should be noted that the text itself is also important for sentiment analysis because in addition to the emotional words, other factors (such as boosters and negating factors) are also present in the sentence and affect the text sentiment. Hence, the algorithm was enhanced in the subsequent methods to detect the boosters and negating words. Results of running the algorithm using different methods indicated that the algorithm accuracy increased with an increase in the number properties involved. In the sixth method, an algorithm capable of identifying emotional, boosters and negative words was applied to two data samples including sentences written by typical users and sentences written by university students on an electronic learning social website. The accuracy of the algorithm with 100 data samples from typical users and 100 data samples from university students was 80% and 84%, respectively.

Acting in the Public Interest: Learnings and Commentary on the Occupational Licensure Literature

There is increased interest in reforming the way occupations are licensed. However, most of the current dialogue focuses on the rapid growth in the number of people who need a license to pursue their chosen occupation. Consequently, significant questions are being raised as to the appropriateness of such requirements for groups that do not appear to present any tangible threat to public safety. Recent research has demonstrated that health professions have failed to articulate the need and value for licensure and how health regulators are addressing the many concerns being raised by the current debate. This study identifies dimensions or aspects of nurse licensure congruent with acting in the public interest and starts to curate (ie, to select, organize, and present) evidence related to each dimension, thereby identifying the current impact and highlighting opportunities for further research. This study utilizes a mixed-methods approach that combines documentary analysis, focus group discussion, and related literature associated with the various dimensions of acting in the public interest to highlight how nurse licensure addresses the current drivers for regulatory reform. A total of 99 documentary sources (ie, papers, articles, reports, and opinion editorials) were uploaded to NVivo 11 Plus for analysis. The analysis generated four overarching themes: (a) licensure reform, (b) impact of occupational associations, (c) occupational licensure issues, and (d) contemporary issues. Associated with these themes, 22 subthemes were also identified. The findings demonstrate there is clear evidence to support the fact that nurse regulators are acting in the public interest. However, further research is needed to provide definitive evidence to inform best practices in regulation and to offer direction for improved nurse regulatory reform. Furthermore, it is suggested that nurse regulators create and effectively maintain a global community capable of learning from one another and driving continuous improvement to better serve the public.

Toward an anatomy of IR system component performances

Information retrieval (IR) systems are the prominent means for searching and accessing huge amounts of unstructured information on the web and elsewhere. They are complex systems, constituted by many different components interacting together, and evaluation is crucial to both tune and improve them. Nevertheless, in the current evaluation methodology, there is still no way to determine how much each component contributes to the overall performances and how the components interact together. This hampers the possibility of a deep understanding of IR system behavior and, in turn, prevents us from designing ahead which components are best suited to work together for a specific search task. In this paper, we move the evaluation methodology one step forward by overcoming these barriers and beginning to devise an “anatomy” of IR systems and their internals. In particular, we propose a methodology based on the General Linear Mixed Model (GLMM) and analysis of variance (ANOVA) to develop statistical models able to isolate system variance and component effects as well as their interaction, by relying on a grid of points (GoP) containing all the combinations of the analyzed components. We apply the proposed methodology to the analysis of two relevant search tasks—news search and web search—by using standard TREC collections. We analyze the basic set of components typically part of an IR system, namely, stop lists, stemmers, and n‐grams, and IR models. In this way, we derive insights about English text retrieval.

<em>Science in the Archives: Pasts, Presents, Futures</em>. Lorraine Daston, ed.

It is a bit unfortunate that, in current parlance, the word science now embraces less than it once did, while the word archive has come to encompass so much. As a result, those who encounter Science in the Archives: Pasts, Presents, Futures, a new volume edited by Lorraine Daston, may make unreasonable assumptions about its contents, when in fact the topics covered by the twelve historically framed essays are unexpectedly broad—from the history of the planets and earth’s stratigraphy, to the archiving of human DNA samples, to the use of “stop lists” in the thirteenth century and by Google. There is something here for all serious students of the rare book and manuscript field, not just science librarians or curators of data created by or for scientists.

Public Transportation Automated Fare Collection Systems Design

Objectives: The article discusses the development of automated payment systems of public transport fare and accounting of passengers. The main objective of the study is a creation of the concept, which allows us to take into account the practical difficulties of collecting fare, electronic monitoring of passenger traffic and control of public transport routes. Methods/ Statistical Analysis: Design of the automated systems is based on analysis of business process chains and their gradual decomposition. The basis of the study approach is developed from the hypothesis about a universal technical and economic solution that allows us to integrate the autonomous elements of the system. The authors summarize the international experience in the development of similar systems in order to create the best practices. Findings: The study worked out the general principles of the automated fare systems in transport. The paper presents the form of deployment, describes typical solutions for the design and gives an example of its practical implementation. The authors specified a composition of business processes in the system operation at various stages. To assess the implementation prospects, the SWOTanalysis was carried out for the electronic accounting systems of the passenger traffic by using state and multiple vendors’ fare cards. Application/Improvement: The results of the comparative study of the automated systems made it possible to complement the existing concept with internal control components for autonomous elements. The authors suggested the introduction of “stop list” units between synchronization intervals in the implementation of fare transactions, as well as differential risk transfer for the fare fixing by the system users.

Automatically generation and evaluation of Stop words list for Chinese Patents

As an important preprocessing step of information retrieval and information processing, the accuracy of stop words’ elimination directly influences the ultimate result of retrieval and mining. In information retrieval, stop words’ elimination can compress the storage space of index, and in text mining, it can reduce the dimension of vector space enormously, save the storage space of vector space and speed up the calculation. However, Chinese patents are a kind of legal documents containing technical information, and the general Chinese stop words list is not applicable for them. This paper advances two methodologies for Chinese patents. One is based on word frequency and the other on statistics. Through experiments on real patents data, these two methodologies’ accuracy are compared under several corpuses with different scale, and also compared with general stop list. The experiment result indicates that both of these two methodologies can extract the stop words suitable for Chinese patents and the accuracy of Methodology based on statistics is a little higher than the one based on word frequency.

A Methodology for Investigating Public Opinion Using Multilevel Text Analysis

Recently, many users have begun to frequently share their opinions on diverse issues using various social media. Therefore, numerous governments have attempted to establish or improve national policies according to the public opinions captured from various social media. In this paper, we indicate several limitations of the traditional approaches to analyze public opinion on science and technology and provide an alternative methodology to overcome these limitations. First, we distinguish between the science and technology analysis phase and the social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we successively apply a start list and a stop list to acquire clarified and interesting results. Finally, to identify the most appropriate documents that fit with a given subject, we develop a new logical filter concept that consists of not only mere keywords but also a logical relationship among the keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discover core issues and public opinions from 1,700,886 documents comprising SNS, blogs, news, and discussions. Keywords—Big data, social network analysis, text mining, topic modeling.

텍스트 분석을 활용한 과학기술이슈 여론 분석 방법론

Recently, many users frequently share their opinions on diverse issues using various social media. Therefore, many governments have attempted to establish or improve national policies according to the public opinions captured from the various social media. In this paper, we indicate several limitations of traditional approaches for analyzing public opinions about science and technology and provide an alternative methodology to overcome the limitations. First of all, we distinguish science and technology analysis phase and social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we apply a start list and a stop list successively to acquire clarified and interesting results. Finally, to identify most appropriate documents fitting to a given subject, we develop a new concept of logical filter that consists of not only mere keywords but also a logical relationship among keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discovering core issues and public opinions from 1,700,886 documents comprising SNS, blog, news, and discussion.

Performance impact of stop lists and morphological decomposition on word-word corpus-based semantic space models.

Corpus-based semantic space models, which primarily rely on lexical co-occurrence statistics, have proven effective in modeling and predicting human behavior in a number of experimental paradigms that explore semantic memory representation. The most widely studied extant models, however, are strongly influenced by orthographic word frequency (e.g., Shaoul & Westbury, Behavior Research Methods, 38, 190-195, 2006). This has the implication that high-frequency closed-class words can potentially bias co-occurrence statistics. Because these closed-class words are purported to carry primarily syntactic, rather than semantic, information, the performance of corpus-based semantic space models may be improved by excluding closed-class words (using stop lists) from co-occurrence statistics, while retaining their syntactic information through other means (e.g., part-of-speech tagging and/or affixes from inflected word forms). Additionally, very little work has been done to explore the effect of employing morphological decomposition on the inflected forms of words in corpora prior to compiling co-occurrence statistics, despite (controversial) evidence that humans perform early morphological decomposition in semantic processing. In this study, we explored the impact of these factors on corpus-based semantic space models. From this study, morphological decomposition appears to significantly improve performance in word-word co-occurrence semantic space models, providing some support for the claim that sublexical information-specifically, word morphology-plays a role in lexical semantic processing. An overall decrease in performance was observed in models employing stop lists (e.g., excluding closed-class words). Furthermore, we found some evidence that weakens the claim that closed-class words supply primarily syntactic information in word-word co-occurrence semantic space models.

Automatic facial attribute analysis via adaptive sparse representation of random patches

It is well known that some facial attributes –like soft biometric traits– can increase the performance of traditional biometric systems and help recognition based on human descriptions. In addition, other facial attributes, such as facial expressions, can be used in human–computer interfaces, image retrieval, talking heads and human emotion analysis. This paper addresses the problem of automated recognition of facial attributes by proposing a new general approach called Adaptive Sparse Representation of Random Patches (ASR+). The proposed method consists of two stages: in the learning stage, random patches are extracted from representative face images of each class (e.g., in gender recognition –a two-class problem–, images of females/males) in order to construct representative dictionaries. A stop list is used to remove very common words of the dictionaries. In the testing stage, random test patches of the query image are extracted, and for each non–stopped test patch a dictionary is built concatenating the ‘best’ representative dictionary of each class. Using this adapted dictionary, each non–stopped test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch voting. Thus, our approach is able to learn a model for each recognition task dealing with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experiments were carried out on eight face databases in order to recognize facial expression, gender, race, disguise and beard. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.

Visualization Techniques for Topic Model Checking

Topic models remain a black box both for modelers and for end users in many respects. From the modelers' perspective, many decisions must be made which lack clear rationales and whose interactions are unclear — for example, how many topics the algorithms should find (K), which words to ignore (aka the "stop list"), and whether it is adequate to run the modeling process once or multiple times, producing different results due to the algorithms that approximate the Bayesian priors. Furthermore, the results of different parameter settings are hard to analyze, summarize, and visualize, making model comparison difficult. From the end users' perspective, it is hard to understand why the models perform as they do, and information-theoretic similarity measures do not fully align with humanistic interpretation of the topics. We present the Topic Explorer, which advances the state-of-the-art in topic model visualization for document-document and topic-document relations. It brings topic models to life in a way that fosters deep understanding of both corpus and models, allowing users to generate interpretive hypotheses and to suggest further experiments. Such tools are an essential step toward assessing whether topic modeling is a suitable technique for AI and cognitive modeling applications.

Stop, Words

The stop list, a list of words excluded from computer processing, is a characteristic artifact of recent times, and its logic is one of the implicit structures of the everyday epistemology of the late twentieth century.

Matching Word-Order Variations and Sorting Results for the iEPG Data Search

This paper describes using a finite-state automaton (FSA) to retrieve Japanese TV guide text. The proposed FSA application can be considered novel due to lack of research on the subject. The automaton has been implemented for matching and extracting all possible combinations of search query words in all possible word orders that may be present in the TV guide text. This implementation also sorts the extraction results by analyzing word semantic features (such as “being an object” or “being a property of an object”). The present paper also proposes a search system using the above implementation and compares it with a baseline system that matches query words (of multi-word queries) in exactly the same and exactly the opposite word orders only. Both systems use morphological parsing and apply a stop list to the query. A multi-parameter evaluation has shown advantages of the proposed system over the baseline one.

Using statistical text mining to supplement the development of an ontology

Statistical text mining was used to supplement efforts to develop a clinical vocabulary for post-traumatic stress disorder (PTSD) in the VA. A set of outpatient progress notes was collected for a cohort of 405 unique veterans with PTSD and a comparison group of 392 with other psychological conditions at one VA hospital. Two methods were employed: (1) “multi-model term scoring” used stepwise logistic regression to develop 21 separate models by varying three frequency weight and seven term weight options and (2) “iterative term refinement” which used a standard stop list followed by clinical review to eliminate non-clinical terms and terms not related to PTSD. Combined results of the two methods were reviewed by two clinicians resulting in 226 unique PTSD related terms. Results of the statistical text mining methods were compared with ongoing efforts to identify terms based on literature review, focus groups with clinicians treating PTSD and review of an existing vocabulary, lending support to the contributions of the STM analyses.

Evaluation Measures in Information Retrieval: A Theoretical Framework for the Unranked Retrieval Sets

It is known that many alternatives in designing an IR system. How do we know which of these techniques are effective in which applications? Should we use stop lists? Should we stem? Should we use in- verse document frequency weighting? Information retrieval has developed as a highly empirical discipline, requiring careful and thorough evaluation to demonstrate the superior performance of novel techniques on representative document collections. In this research tries to present common (although many) evaluation of measuring the effectiveness of IR systems that widely used. and the test collections that are most often used for this purpose. Then presenst the straightforward notion of relevant and nonrelevant documents and the formal evaluation methodol-ogy that has been developed for evaluating unranked retrieval results. This includes explaining the kinds of evaluation measures that are standardly used for document retrieval and related tasks like text clas-sification and why they are appropriate. This research can valuable for those want to do research in the field of IR. . Keyword: Information Retrieval, evaluation & measurement, Precion & Recall,