Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of Twitter Posts About news

The thesis set out to solve a practical problem of sentiment analysis of Twitter posts about news. The thesis has made contributions is data collection of tweets about news, empirical study of the role of context in sentiment analysis of tweets about news, and best feature selection. Test data was collected by a bootstrapping approach where some tweets that contain some number of words from the news headline were used to extract links and to obtain more tweets about news from Twitter. The test data was manually inspected and annotated for its sentiment to see if context plays role in determining sentiment of a tweet. Uni-gram+bi-gram was selected as a feature that captures two important features of the data: uni-grams provides better coverage of the data, and bi-grams capture sentiment expression patterns. The thesis has shown that tweets about news can be automatically collected and successfully analyzed for their sentiment. Multinomial Naive Bayes classifier using uni-gram+bi-gram presence was found to give the highest accuracy, an accuracy of 87.78% for a three-classed classification and an accuracy of 90.79% for the two-classed (subjective, objective) classifier derived from the three-classed classifier. The accuracies of the classifier on both three-classed and two-classed classification is impressive and can be applied for practical applications dealing with sentiment analysis of tweets.
A methodology for analyzing whole tree logging in Bulgaria

The process of whole tree logging in Bulgaria is regulated by The Executive Forest Agency. This Agency provides information about the planned area and the planned quantity of tree logging for each municipality and each plot of land. It also keeps track of the real area and real quantity of tree logging by each plot of land. The purpose of this paper is to present a methodology for analyzing the whole tree logging process using public available data. Statistical methods such as correlation analysis, testing of hypotheses, analyzing growth rates and calculating structural change indexes are applied. Visual grouping of locations reveals three groups of places. It is proved that there is a great correlation between the planned area, planned quantity, the real area and the real quantity of tree logging for each year. Growth rate analysis gives signals for possible problems with tree logging. The structural change indexes show great variations in the structure of living places providing wood.
RFID data cleansing with Bayesian Filters

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through extensive simulations with real-world parameters. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently.
Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed: Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference Sentic Computings shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses This volume is the first in the Series Socio-Affective Computing edited byDr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems.
Automatic Identification of Online Predators in Chat Logs by Anomaly Detection and Deep Learning

Providing a safe environment for juveniles and children in online social networks is considered as a major factor in improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of juvenile abuse in cyberspace has become inevitable. Using automatic ways to address this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and binary classification in machine learning. This thesis proposes two machine learning approaches to deal with the following two issues in the domain of online predator identification: 1) The first problem is gathering a comprehensive set of negative training samples which is unrealistic due to the nature of the problem. This problem is addressed by applying an existing method for semi-supervised anomaly detection that allows the training process based on only one class label. The method was tested on two datasets; 2) The second issue is improving the performance of current binary classification methods in terms of classification accuracy and F1-score. In this regard, we have customized a deep learning approach called Convolutional Neural Network to be used in this domain. Using this approach, we show that the classification performance (F1-score) is improved by almost 1.7% compared to the classification method (Support Vector Machine). Two different datasets were used in the empirical experiments: PAN-2012 and SQ (Surete du Quebec). The former is a large public dataset that has been used extensively in the literature and the latter is a small dataset collected from the Surete du Quebec.
Integrated Cluster-based Rule Induction Mining of Temporal Data for Time-Series Analysis

Objectives: To deliver excellent performance when compared to existing method Mining Comprehensible Classification Rules for Time-Series (MCCR-TS) using zoo dataset collected from UCI repository comprising of more than 1000 records. Methods/Analysis: Research works on Time-series data has been evolving as a new trend in current scenarios due to the wide range of applications involved in it. One of the widely researched topics is the web usage mining and effective evaluation of time-series movements and transaction associated with them. Most of the studies conducted earlier, focused on identifying the time-series data from the entire logs. However, this type of patterns may not be accurate enough for evaluation due to differentiated behaviors of user patterns are not taken into consideration. Findings: In this paper, we examine the time series for temporal data using integrated cluster-based rule induction mining, by presenting a novel algorithm, namely, Integrated Cluster-based Rule Induction Mining for Time-Series analysis (ICRIM-TS). The algorithm discovers temporal data and the similarities between users are evaluated by the proposed measure, Time Series. To our best knowledge, this is the first work on mining and identification of user behaviors based on the time series data with preferences being given for both user relations and temporal data at the same time. Improvement: Through experimental evaluation, under various settings, the performance of web usage mining is evaluated in terms of precision of users, error threshold value and number of rules extracted.
Increasing Pedal Duration on a Stationary Recumbent Bicycle in Children with Autism Spectrum Disorder by Comparing the Effectiveness of Immediate Versus Delayed Reinforcement

This study examined the effects of immediate vs. delayed reinforcement on time spent pedaling a stationary recumbent bicycle within the target heart rate zone (THRZ) of children with Autism Spectrum Disorder (ASD). The immediate reinforcement group would receive and immediate reward as the television would be turned on for viewing during the exercise session when they pedaled and reached their THRZ. Furthermore, the television would remain on as long as each participant pedaled in his or her THRZ range. The delayed group was rewarded by watching television after each exercise session. A single-subject design with a split-middle plot technique was used to analyze and compare individual trends within and between both groups. There were differences between the contingent and the delayed reinforcement phase (p≤0.03) for seven of the nine participants. Subscribe to Palaestra
A Reflective Analysis of the Selection and Production of Instructional Material for Curriculum Delivery at the Primary Level in Postcolonial Guyana

This study gazed upon curriculum delivery in postcolonial Guyana. The key focus was on the impact of effective curricula instructional materials on curriculum delivery. A descriptive survey method was employed to gather data and a sequential transformative strategy which embraced a mixed method approach was utilized to analyze and interpret the data. One hundred mainstream teachers were asked to respond to five questions on a questionnaire. The sample represents 13% of the entire teacher population at the University of Guyana and 25% of the primary level teacher population of the Faculty of Education and Humanities. These teachers were randomly stopped and asked the questions as they made their way to class on the Turkeyen Campus. These teachers have all acquired initial qualified teachers’ status and at least 2 years of mainstream teaching experience. The teachers indicated that they are limited in their use of technology in mainstream schools, as many schools do not have Information and Communication Technology (ICT) support and physical space is limited. Teachers stated that they produced at least 90% of the curricula instructional material they use in mainstream classrooms. They indicated that they sometimes have to self-fund the raw materials needed to produce the instructional materials necessary for effective curriculum delivery in mainstream schools. Teachers are expected to do this without either allotted planning or preparation time. The production of instructional materials, done independently with little support from the senior leadership of the mainstream schools, is sometimes neither adequate nor cater to the diverse needs of all learners especially learners with Special Education Needs and Disabilities.
Mennonite Country: The Role of School Parent Liaisons and School Administrators Connecting with Immigrant Latino Families in North Central Indiana

This study examined the role of school personnel on the education of immigrant Latino families and their children in North Central Indiana. An ethnographic research design was used to examine the relationship between a Midwestern U.S. school district and immigrant families and its impact on the education of Latino children. Specifically, over the course of two years, 14 school personnel participated in the ethnography. Findings demonstrated that school parent liaisons were critical to developing a bridge between the schools and the immigrant Latino families and to the success of the children. Another important finding was that school administrators’ proactive involvement in the education and lives of the immigrant Latino families and their children had resulted in a positive learning environment for the Latino children. Implications are provided for practice and policy.
Case Studies on Optimizing Algorithms for GPU Architectures

Data type: An abstract data type (ADT) is a model for data structures or data types that can be described by their behavior or semantics. An abstract data type is defined only by the operations that may be performed on it; pre-conditions on the inputs to those operations; post-conditions on the outputs from those operations; and possible descriptions of the required time or space complexity of those operations using asymptotic notation. For example: A stack can be defined by 3 operations – push, pop, and top, each taking constant O(1) time. See also object-oriented programming. Abstraction: In computer science, abstraction manages complexity. A certain level of complexity ision: In computer science, abstraction manages complexity. A certain level of complexity is established on how a person interacts with the computing system, while more complex details below the current level are suppressed. Thus a programmer works with an idealized well-defined interface and can add more levels of functionality later that would otherwise be too complex to handle. There are two main types of abstraction – control and data. Control abstraction involves using sub-programs or sub-routines to manage the control flow through a program. Data abstraction allows the grouping of data bits into meaningful chunks, i.e. data types, records, structures, etc. Object-oriented classes combine abstractions of both functionality and data. Algebraic Simplification: A data-flow optimization technique that uses algebraic laws to simplify or re-order instructions for better performance. See also constant folding, code fusion, and strength reduction. Algorithm: An algorithm is a step-by-step recipe to perform a task that solves a problem – an unambiguous set of instructions that performs the task by operating on input and producing output. An algorithm is typically expressed using language-agnostic pseudo-code, which can be implemented in any programming language. Algorithmic Skeleton: Synonym for pattern, specifically the subclass of patterns having to do with
Mental institutions, habits of mind, and an extended approach to autism

We argue that the notion of “mental institutions” – discussed in recent debates about extended cognition – can help better understand the origin and character of social impairments in autism, and also help illuminate the extent to which some mechanisms of autistic dysfunction extend across both internal and external factors (i.e., they do not just reside within an individual’s head). After providing some conceptual background, we discuss the connection between mental institutions and embodied habits of mind. We then discuss the significance of our view for understanding autistic habits of mind and consider why these embodied habits are sometimes a poor fit with neurotypical mental institutions. We conclude by considering how these insights highlight the two-way, extended nature of social impairments in autism, and how this extended picture might assist in constructing more inclusive mental institutions and intervention strategies.
A Phenomenological Study on the Ego-subjectivity Recovery Experience of the Wives of Alcoholics

The objective of this study was to evaluate how wives of alcoholics could escape from the co-dependency relationship and recover their ego-subjectivity. This study conducted 1:1 in-depth interviews with five wives of alcoholics in order to achieve the study objective. Data collected from the interviews were analyzed by the phenomenological method proposed by Giorgi[1]. First, researchers extracted five subjects, including ‘Co-dependency (Loss of ego-identity)’, ‘Impoverished life’, Ego-prop’, Ego-awareness’, and ‘Ego-subjectivity recovery’ in the course of the data analysis. Secondly, researchers identified “Ego-subjectivity recovery through Ego-awareness of a wife of an alcoholic”, an essential theme, by integrating all subjects. Researchers, based on the analysis results, proposed practical alternatives for various psychological treatment system and ego-prop like as counselling, family member of alcoholic wives and power of faith. ■ keyword :∣The Wives of Alcoholics∣Ego-subjectivity Recovery∣Phenomenological Study∣ 접수일자 : 2019년 02월 17일 수정일자 : 2019년 03월 11일 심사완료일 : 2019년 03월 20일 교신저자 : 조희선, e-mail : hscho2@skku.edu 알코올중독자 남편을 둔 여성배우자의 자아주체성 회복경험에 대한 현상학적 연구 577
Recent Developments In Modeling And Applications In Statistics

Thank you for reading recent developments in modeling and applications in statistics. Maybe you have knowledge that, people have look hundreds times for their favorite books like this recent developments in modeling and applications in statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their desktop computer.
What is Watson

CONclusions of the American Society for Quality Futures Study was globalization stimulated by shrinking boundaries among businesses due to electronic commerce. In the coming millennium, the effect of technology on the success of all businesses, not just the large multinationals, will be significant. Even a small business can benefit from advanced technology. A friend of mine, Ian Prosser, owns a small floral shop in Tampa, FL, named Botanica International Florist. Europeans who are coming to Florida to get married call him. Technology, specif-
Proposed Novel Hybrid Approach For Improvement in Bug Severity prediction

: In this research study an approach of creating dictionary of critical terms is proposed to assess the bug severity as severe and non severe. It is found that using different approaches of feature selection and classifier the pattern of accuracy and precision is approximately same. However Chi square test and KNN classifier give the maximum performance of precision and accuracy for the all four components. The proposed work will help Triager in classifying bugs based on severity and assigning these bugs to relevant developer
Adaptive Strip Compression for Panorama Video Streaming

Traditional live video streaming systems support the limited field of view (FOV) of image to the remote users. A server system based on the pan/tilt camera provides a user with wide view by changing the view direction of the camera mechanically. But, when many clients try to access to the server, this system can not offer their own view to every user simultaneously, and moreover it has the delay by camera motion. In order to offer wide views to several users, we propose new streaming system using the panorama image that has wide view. Our system is a kind of implementation of software pan/tilt camera. The server acquires panorama video and sends a part of the video to clients. Then, each client can control their own view. We need the effective way to reduce the average transmission data size and server burden to the compression because generally the full size of panorama video is too big to be served by the real-time streaming. To solve this problem, we propose an strip-based video compression and adaptive transmission of the compressed multiple strip videos. Experimental results show that our system can be adapted quickly to the change of view and the number of clients. Furthermore, proposed method effectively reduce the transmission data.
An Adaptive Framework for Power Components Dynamic Loadability

Adaptive modelling is generally used in order to make the system to model less sensitive to unmodeled system dynamics and to changes in its parameters. Even if many non-linear systems can be approximated by reduced-order models, possibly linear, these models may be suitable only within certain specific operating ranges and, because of changed operating conditions or changed system model parameters, abStract
Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. The proposed approach is identified as two procedures. First, the weight is determined based on the distance of evidence. Then, the obtained weight value in the first step is modified by making advantage of Deng entropy function. Our proposed method can efficiently cope with high conflicting evidences with better performance of convergence. A numerical example is provided to demonstrate that the proposed method is reasonable and efficient in the end.
Looking for neurophysiological correlates of brain-computer interface learning

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Looking for neurophysiological correlates of brain-computer interface learning Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie George, Laurent Hugueville, Ari Kahn, Sophie Dupont, Danielle S. Bassett, Fabrizio de Vico Fallani
High-Quality Winners Take More: Modeling Non-Scale-Free Bulletin Forums with Content Variations

Modeling the Internet has been an active research in the past ten years. From the ”rich get richer” behavior to the ”winners don't take all” property, the models depend on the explicit attributes described in the net-work. This paper discusses the modeling of non-scale-free network subsets like bulletin forums. A new evolution mechanism, driven by some implicit attributes ”hidden” in the network, leads to a slightly increase in the page sizes of front rank forum. Due to the complication of quantifying these implicit attributes, two potential models are suggested. The first model introduces a content ratio and it is patched to the lognormal model, while the second model truncates the data into groups according to their regional specialties and data within groups are fitted by power-law models. A Taiwan-based bulletin forum is used for illustration and data are fitted via four models. Statistical Diagnostics show that two suggested models perform better than the traditional models in data fitting and predictions. In particular, the second model performs better than the first model in general.
Automated policing: the case of body-worn video

Abstract This paper examines the impact of body-worn video (BWV) on the police craft skills of close observation, note-taking, investigative analysis, report-writing and preparation of evidence for the courts. It explains how the technology functions and explores its surveillant, investigative, probative and regulatory applications. The evidence shows that policing tasks are being transformed by BWV cameras and analytics such as facial recognition. The paper argues that BWV exemplifies the automation of policing – the replacement of police labour with mechanical devices – and explores the implications of this for transparency, accountability, fairness and police discretion.