Feature selection for incomplete set-valued data

Set-valued data is a significant kind of data, such as data obtained from different search engines, market data, patients’ symptoms and behaviours. An information system (IS) based on incomplete set-valued data is called an incomplete set-valued information system (ISVIS), which generalized model of a single-valued incomplete information system. This paper gives feature selection for an ISVIS by means of uncertainty measurement. Firstly, the similarity degree between two information values on a given feature of an ISVIS is proposed. Then, the tolerance relation on the object set with respect to a given feature subset in an ISVIS is obtained. Next, λ-reduction in an ISVIS is presented. What’s more, connections between the proposed feature selection and uncertainty measurement are exhibited. Lastly, feature selection algorithms based on λ-discernibility matrix, λ-information granulation, λ-information entropy and λ-significance in an ISVIS are provided. In order to better prove the practical significance of the provided algorithms, a numerical experiment is carried out, and experiment results show the number of features and average size of features by each feature selection algorithm.

Attribute selection approaches for incomplete interval-value data

Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, θ-reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction algorithms base on θ-discernibility matrix, θ-information entropy and θ-significance in an IIVIS are given.

Information structures and uncertainty in an image information system

An information system as a database that stands for relationships between objects and attributes is an important mathematical model. An image information system is an information system where each of its information values is an image and its information structures embody internal features of this type of information system. Uncertainty measurement is an effective tool for evaluation. This paper explores measures of uncertainty for an information system by using the proposed information structures. The distance between two objects in an image information system is first given. After that, the fuzzy Tcos-equivalence relation, induced by this system by using Gaussian kernel method, is obtained, where Gaussian kernel is based on this distance. Next, information structures of this system are described by set vectors, dependence between information structures is studied and properties of information structures are given by using inclusion degree, and application for information structures and uncertainty measures of an image information system are investigated by the information structures. Moreover, effectiveness analysis is done to show the feasibility of the proposed measures from the angle of statistics. Finally, an application of the proposed measurement for attribute reduction is given. These results will be helpful for understanding the essence of uncertainty in an image information system.

Temporal-spatial variation of surface water affected by apatite mining activity in Lao Cai, Viet Nam

Apatite ore mining and processing is one of the main mineral activities of Lao Cai province. According to the annual environmental monitoring results, most of the rivers and streams flowing through apatite mining and processing areas such as: O, Ngoi Dum, Ngoi Duong, Dong Ho and Coc streams are all polluted by content of COD, BOD5, TSS, N?2−, N?4+, N?3−. The concentration of parameters as COD, BOD5, N?3−, N?2−, N?4+, tends to increase over the years. Streams near the mining areas such as Ngoi Duong, Dong Ho, Chu O, Coc streams were mainly polluted by COD, BOD5, TSS, N?3−. Streams in the ore processing area at Tang Loong such as Trat, Cam Duong, Khe Chom streams,... were mainly polluted by N?4+, NO2- and some heavy metals like as Cu, Fe. The results of cluster analysis showed that basically the quality of stream water in the ore mining and processing area is divided into 4 groups: heavy pollution, medium pollution, light pollution and no pollution. In particular, heavy polluted streams flow through apatite mining areas such as Ngoi Duong, Ngoi Dum and Chu O. The results of PCA show changes in the distribution of environmental quality parameters in major components by years.In particular, the parameters that contain high information values include COD, BOD5¸ N?3−, N?2− , Cu and Fe. The source of pollution is related to domestic wastewater at apatite mining and processing areas.

Social Distance Into Factual Information Distance about COVID-19 in Indonesia Whatsapp Groups

WhatsApp has become a forum for communication and information related to the COVID-19 pandemic, so there are messages that ignore the identification and validation of news facts. This creates an information gap on WhatsApp. This study aims to analyze the form of information gaps related to information on the COVID-19 pandemic in Indonesia shared by WhatsApp group users. This research uses quantitative research methods, with data collection techniques through documentation of text messages and pictures, as well as the results of a survey conducted on WhatsApp group users. The results of this research indicate that the information gap in reporting the COVID-19 pandemic occurs due to information gaps in the form of uncertainty, strong beliefs and opportunities to choose information and decisions to choose information values. The substance of this research contributes in the form of new policy recommendations in assessing information gaps in social media by validating the truth of the facts in each message received and shared to another users.

Transnational Advocacy Networks of Migrants and Asylum Seekers’ Human Rights: The San Diego—Tijuana Border in the Trump Era

How do advocacy organizations from the San Diego—Tijuana area contest and resist Trump’s immigration policies? What resources and tactics do they use to externalize their demands at the local and international levels? Based on semi-structured interviews with eight advocacy organizations—with a local, binational, and international presence—that have mobilized to externalize their demands on different local and international arenas, this research aims to answer these questions by applying the transnational advocacy networks literature to the mentioned case study. Our main findings show that transnational relations between advocacy organizations represent a counterbalance to Trump’s immigration policies. Some organizations have adapted to react to a set of new policies implemented by the administration, and, at the same time, they have diffused information, values, and ideas as part of their resistance tactics. Through transnational advocacy networks, local organizations have accomplished international relevance, turning into key players of advocacy in the region. These networks demonstrate that, despite Trump’s restricting immigration policies, transnational channels between these actors remain open for collective action.

Information theoretic approaches to deciphering the neural code with functional fluorescence imaging

Information theoretic metrics have proven highly useful to quantify the relationship between behaviorally relevant parameters and neuronal activity with relatively few assumptions. However, such metrics are typically applied to action potential recordings and were not designed for the slow timescales and variable amplitudes typical of functional fluorescence recordings (e.g. calcium imaging). Therefore, the power of information theoretic metrics has yet to be fully exploited by the neuroscience community due to a lack of understanding for how to apply and interpret the metrics with such fluorescence traces. Here, we used computational methods to create mock action potential traces with known amounts of information and from them generated fluorescence traces to examine the ability of different information metrics to recover the known information values. We provide guidelines for the use of information metrics when applied to functional fluorescence and demonstrate their appropriate application to GCaMP6f population recordings from hippocampal neurons imaged during virtual navigation.

Toward a Better “Person–Environment Fit” through Items Calibration of the SIS-C

The Supports Intensity Scale–Children’s Version (SIS-C) is the only available tool to assess extraordinary support needs for children and adolescents with intellectual disability. In past years, several works have proclaimed the need for its ongoing improvement as a measurement instrument. To contribute to this line of research, the goal of this work is to analyze the reliability of the SIS-C and its usefulness to distinguish between different levels of intensity of support needs. To address this, 814 children and adolescents with intellectual disability (M = 11.13 years; SD = 3.41) were assessed using the SIS-C Spanish version. Item response theory analyses were conducted to estimate latent scores and assess measurement quality along the support needs continuum. The SIS-C items showed good overall discrimination and information values, and none showed problems that required their removal or modification. However, all the scales composing the SIS-C showed problems in discerning high levels of intensity of support needs, especially for children and adolescents with severe/profound intellectual disability. This ceiling effect may be an obstacle for both research and practice involving the SIS-C. Implications for research and practice are discussed, and future lines of research for improving the SIS-C are provided.