Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-temporal Event Detection using Poisson Model and Quad-tree on Geotagged Social Media

Identifying events happening in a specific locality is important as an early warning for accidents, protests, elections or breaking news. However, this location-specific event detection is challenging as the locations and types of events are not known beforehand. To address this problem, we propose an online spatiotemporal event detection system using social media that is able to detect events at different time and space resolutions. First, we exploit a quad-tree method to split the geographical space into multiscale regions based on the density of social media data. Then, we implement a statistical unsupervised approach using Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using Twitter and Flickr for the city of Melbourne based on recall and precision measures. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.
Highly efficient Multi-Junction Solar Cells Performance Improvement for AC Induction Motor Control Using the dsPIC30F Microcontroller

Solar energy is an abundant renewable source which is expected to play an increasing role in terrestrial and space systems future infrastructure for distributed power generation. Improving of solar performance and efficiency is a key goal of research and the prominent factor in photovoltaic systems which make PV technologies cost-competitive with conventional sources of energy. Multi-junction solar cells are the most efficient technology for generation of electricity from solar irradiation. It's estimated that these cells have an incredible efficiency for more than 40%. They have the advantage of improved performance. Highly efficient multi-junction solar cells find their use in many applications. In regard to terrestrial industrial applications, AC electrics motors are being fed from the multi-junction photovoltaic systems when exposed to high light concentrations using a boost converter and three phase inverter for variable speed AC induction motor drives based on full digital control. Another application for multi-junction solar cells is the use in space vehicles and satellites under low light concentrations. The aim of this research work was modeled and simulated the multi-junction solar cells to improve their performance in terms of efficiency for further exploration in future photovoltaic power generation systems and implemented a variable speed control of an AC Induction Motor using the dsPIC30F Microcontroller for high performance industrial applications.
Sensing Users’ Emotional Intelligence in Social Networks

Social networks have integrated into the daily lives of most people in the way of interactions and of lifestyles. The users’ identity, relationships, or other characteristics can be explored from the social networking data, in order to provide personalized services to the users. In this article, we focus on predicting the user’s emotional intelligence (EI) based on social networking data. As an essential facet of users’ psychological characteristics, EI plays an important role on well-being, interpersonal relationships, and overall success in people’s life. Perception of EI contributes to predicting one’s behavior or group behavior. Most existing work on predicting people’s EI is based on questionnaires that may collect dishonest answers or unconscientious responses, thus leading in potentially inaccurate prediction results. In this article, we are motivated to propose EI prediction models based on the sentiment analysis of social networking data. The models are represented by four dimensions, including self-awareness, self-regulation, self-motivation, and social relationships. The EI of a user is then measured by four numerical values or the sum of them. In the experiments, we predict the EIs of over a hundred thousand users based on one of the largest social networks of China, Weibo. The predicting results demonstrate the effectiveness of our models. The results show that the distribution of the four EI’s dimensions of users is roughly normal. The results also indicate that EI scores of females are generally higher than males’ EI scores. This is consistent with previous findings. In addition, the four dimensions of EI are correlated. We finally analyze the advantages and the disadvantages of our models in predicting users’ EI with social networking data.
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance

The insurance industry has been creating innovative products around the emerging online shopping activities. Such ecommerce insurance is designed to protect buyers from potential risks such as impulse purchases and counterfeits. Fraudulent claims towards online insurance typically involve multiple parties such as buyers, sellers, and express companies, and they could lead to heavy financial losses. In order to uncover the relations behind organized fraudsters and detect fraudulent claims, we developed a large-scale insurance fraud detection system, i.e., InfDetect, which provides interfaces for commonly used graphs, standard data processing procedures, and a uniform graph learning platform. InfDetect is able to process big graphs containing up to 100 millions of nodes and billions of edges.In this paper, we investigate different graphs to facilitate fraudster mining, such as a device-sharing graph, a transaction graph, a friendship graph, and a buyer-seller graph. These graphs are fed to a uniform graph learning platform containing supervised and unsupervised graph learning algorithms. Cases on widely applied e-commerce insurance are described to demonstrate the usage and capability of our system. InfDetect has successfully detected thousands of fraudulent claims and saved over tens of thousands of dollars daily.
Cluster-size optimization within a cloud-based ETL framework for Big Data

The ability to analyze the available data is a valuable asset for any successful business, especially when the analysis yields meaningful knowledge. One of the key processes for acquiring such ability is the Extract-Transform-Load (ETL) process. For Big Data, ETL requires a significant effort and it is a very challenging task to be performed in a cost-effective way. There are quite a few examples in the literature that describe an architecture for cost-effective ETL but none of the available examples are complete enough and they are usually evaluated in narrow problem domains. The ones that are more general, require specific implementation details. In this paper we propose a cloud-based ETL framework where we use a general cluster-size optimization algorithm, while providing implementation details, and is able to perform the required job within a predefined, and thus known, time. We evaluated the algorithm by executing three scenarios regarding data aggregation during ETL: (i) ETL with no aggregation; (ii) aggregation based on predefined columns or time intervals; and (iii) aggregation within single user sessions spanning over arbitrary time intervals. The execution of the three ETL scenarios in a production setting showed that the cluster size could be optimized so it can process the required data volume within a predefined and thus, expected, latency. The scalability was evaluated on Amazon AWS Hadoop clusters by processing user logs collected with Kinesis streams with datasets ranging from 30 GB to 2.6 TB.
News Recommender System Considering Temporal Dynamics and News Taxonomy

In the past, news recommender systems have been built to recommend list of news items similar to those that a user has accessed before (content-based); or similar to those that have been read by similar users (collaborative filtering). However, the highly volatile nature of the news content and the dynamic and evolving user preferences are either ignored or not taken into full consideration in these systems. In a news recommender system, it is very likely that a user’s short-term interest or preference may have a sudden change due to an emerging social or personal event or breaking news while their long-term interests may change gradually or remain. For these long-term interests of the readers, it is often more appropriate to associate them with news categories than with individual news items. In this paper, we propose a biased matrix factorization model with consideration of both temporal dynamics of user preferences and news taxonomy to build a news recommender system. By conducting an extensive experiment on a collection of news data, we demonstrate the effectiveness of our proposed model against traditional matrix factorization models as well as other neural recommender baselines. The findings from our experiments show that news category is an important factor when readers choose news articles to read, and temporal factors with consideration of different temporal resolution also play a role in this process.
Origin-destination Flow Prediction with Vehicle Trajectory Data and Semi-supervised Recurrent Neural Network

Origin-Destination (OD) flow data is an important instrument for traffic study and management. So far traditional ways like surveys or detectors are costly and only give limited availability of OD flows. Various statistical and stochastic models for OD flow estimation and prediction based on limited link volume data or automatic vehicle identification (AVI) data have been developed. However, smartphone-generated trajectory data has not been as much leveraged in this field, though the usage of smartphones in traveling is emerging in recent years. In this paper, we propose a semi-supervised deep learning based model that appropriately combines both AVI and smartphone trajectory data during training and is able to generate predictions of OD flows in an urban network solely based on the smartphone trajectory data at inference time. Our model can provide OD estimation and prediction services on larger spatial areas beyond the limited spatial coverage of AVI data. Tests of our model using real data have shown promising results, compared with an AVI input-dependent Kalman filter model. Potentially, our model can easily be embedded to a trajectory collecting platform and generate continuous real-time OD flow predictions online.
Reliable Knowledge Graph Path Representation Learning

Knowledge graphs, which have been widely utilized in various intelligent applications, are highly incomplete. Many valid facts can be inferred from existing facts in knowledge graphs. A promising approach for this task is a knowledge graph representation learning, which aims to represent entities and relations into low-dimensional vector spaces. Most of the existing methods mainly focus on direct relationships between entities and do not reflect the semantics of multi-hop relation paths. Although a few methods have studied the problem of multi-hop path-based representation learning, they fail to distinguish reliable relation paths among a majority of meaningless relation paths. In this paper, we propose a reliable path-based knowledge graph representation learning method, called RKRL. Specifically, we combine the representations of intermediate entities and relations on relation paths to learn more meaningful knowledge representations. Also, we present a reliable knowledge graph path ranking method to avoid the unnecessary computation of unreliable paths and find semantically valid relation paths. Experimental results on benchmark datasets show that our method achieves consistent improvement on typical evaluation tasks for knowledge representations, compared with the classical and state-of-the-art representation learning baselines.
PBEM: A Pattern-Based Embedding Model for User Location Category Prediction

With the rapid popularity of mobile devices, a vast amount of trajectory-based check-in data are shared in many social network applications, which is an important data source for user location prediction. The location category prediction, a branch of location prediction, is a vital task in a wide range of areas, including urban planning, advertising and recommendation systems. In this paper, we propose a novel two-step Pattern-Based Embedding Model (PBEM) for predicting the next location category that user will go to. Based on the observation that some users behave frequently in a similarity pattern, a new feature termed as user cluster label is defined. In order to mine user's behavior patterns and extract the cluster label, a Category-Importance-Decay learning strategy is proposed and implemented, which provides a quantitative standard for evaluating the importance of each category. Thus, a comprehensive feature set is obtained including user, time, historical location category, text content, and user cluster label, which greatly enhances the robustness of data representation and contains more knowledge. Then the extracted feature set is fed into Recurrent Neural Network (RNN) in a unified framework, which improves the prediction accuracy. We evaluate the performance of PBEM on two real-life trajectory-based check-in datasets. Experimental results demonstrate that the proposed model can outperform the state-of-the-art methods.
Accounting-Based Risk and the Valuation of R&D Intensive Firms

We demonstrate that the uncertainty communicated by US R&D accounting is also risk relevant. First, we quantify the information on the uncertainty of future benefits through revisions of expectations regarding future cash flows. R&D significantly influences the market’s revisions of expectations regarding future cash flows (cash flow news) and, slightly, discount rates (discount rate news). This emphasizes that the uncertainty of future R&D-related benefits drives stock return variance. We then examine the risk relevance of this information through its influence on the sensitivities of firm-level cash flow and discount rate news to market return news. Specifically, we gauge whether the stock market beta of R&D active firms is determined by fundamental information or investor sentiment. R&D drives the variation in the sensitivity of firm-level cash flow news to market return news, which supports the view that R&D accounting is risk relevant. There is no pattern on the sensitivity of firm-level discount rate news to market return news, which addresses concerns about the role of R&D-induced sentiment in the stock market beta of R&D active firms.
Supply chain disruptions: the influence of industry and geography on firm reaction speed

PurposeResponding in a timely manner to product recalls emanating from the supply chain presents tremendous challenges for most firms. The source might be a supplier from the same industry located next door, or one from a completely different sector of the economy situated thousands of miles away. Yet the speed of the firm’s response is crucial to mitigating the consequences of the recall both for the firm, and consumer health and well-being. The purpose of this paper is to investigate the effects of geographic distance, industry relatedness and clustering on firm response time to a supplier-initiated product recall.Design/methodology/approachThe authors test the theoretical framework via an examination of food recall announcements registered with the US Food and Drug Administration over a ten-year period. The authors develop a data set comprising 407 pairs of supplier and affected downstream manufacturing firms, and utilize cross-classified hierarchical linear modeling to understand the drivers of organizational responsiveness.FindingsThe results suggest that firm response time is lengthened by geographic distance but reduced when the supplier and affected firm operate in related industry sectors. The authors further find that as more firms in a given industry are affected by the same recall, response time deteriorates.Originality/valueProduct recalls in the agri-food industry are significant events initiated to protect consumer health and ensure the safety of the farm-to-fork food chain. The findings highlight how both geographic- and industry-related factors determine the speed of firm responsiveness to these events.
African American Women in New York State Who Overcame Barriers to Become Superintendents of Schools

African American women educators who possess the leadership characteristics to become superintendents of schools in New York State are not rising to top positions at the same rate as women of other racial and ethnic groups. There is limited research on the barriers and challenges experienced by African American women as well as the strategies that aided in their maintenance of the position. This phenomenological qualitative research study examined the perceptions of African American women who successfully obtained the position of superintendents of schools in New York State at suburban public schools, the barriers and challenges they overcame, and the strategies that made them successful. The findings aimed to highlight the barriers, the challenges, and the strategies as well as to offer recommendations to school boards and educational institutions to aid in increasing the numbers of African American women superintendents of schools in order to ensure a more diverse workforce. Document Type Dissertation Degree Name Doctor of Education (EdD) Department Executive Leadership First Supervisor Frances Wills Second Supervisor Cassandra Hyacinthe Subject Categories Education This dissertation is available at Fisher Digital Publications: https://fisherpub.sjfc. edu/education_etd/394 African American Women in New York State Who Overcame Barriers to Become Superintendents of Schools
The role played by the University of the North student activism in the struggle against apartheid from 1968 to 1994

Student activism is a global phenomenon which mostly refers to work by students to cause political, environmental, economic, or social change. Most countries have benefited tremendously from student activism. For example, the students have played a central role in the independence and anti-colonial struggles in most African countries. The dissertation focuses on an exploration of the role played by University of the North student activism in the struggle against apartheid from 1968 to 1994. This was a period which was characterised by an upsurge of the nationalist struggle in South Africa led by political organisations such as the African National Congress (ANC), the Pan Africanist Congress (PAC), Azanian People’s Organization (AZAPO) the South African Communist Party (SACP) and United Democratic Front (UDF). Student organisations such as South African Student Organisation (SASO), University Christian Movement (UCM), South African National Students’ Congress (SANSCO), Azanian Student Organisation (AZASO) and many others played a significant role. The dissertation deployed both primary and secondary sources. Secondary data was derived from published and unpublished dissertations, journal articles, newsletters, books and autobiographies. Primary information was obtained through archival materials, official university documents, speeches and, unstructured and interactive interviews in order to provide evidence for the nature and character of student activism in the university. Periodisation theory as articulated by Hollander, Rassuli, Jones and Farlow (2005) was utilised to interpret and illuminate the political struggle activities of the student activists. This theory was the most appropriate frame to tackle student activism because it divides the chronological narrative into separately labelled sequential time periods with distinct beginning and ending points. The investigation reveals that the dominant ideology at the beginning of the period under investigation was Black Consciousness inspired by Steve Biko. However with the lapse of time this ideology was watered down by the liberal ideology which underpinned the Freedom Charter. The student activists operated within organisations such as SASO, UCM, AZASO, SANSCO and many others. The
On the Optimality of Nominal GDP Targeting Under Distortionary Taxation

In this thesis the welfare properties of three different monetary policy rules are considered, (i) inflation targeting, (ii) nominal GDP targeting, and (iii) a standard Taylor rule. This is done in the hope of determining whether or not the frequently discussed nominal GDP targeting rule for monetary policy possesses any welfare improving properties over the other two policy rules considered. To perform this analysis a New-Keynesian dynamic stochastic general equilibrium model featuring imperfect competition, nominal rigidities, investment, and distortionary taxation is derived and then solved using perturbation methods, with various parameters of the model being estimated for Icelandic data using Bayesian estimation techniques. The main findings of the thesis are that nominal GDP targeting seems to possess significant welfare improving properties over the other two monetary policy rules considered, managing to minimise the welfare loss resulting from the nominal rigidities as well as the significant welfare loss resulting from the presence of distortionary taxes. This however does not necessarily imply that nominal GPD targeting is an optimal monetary policy rule, owing to the fact that there are significant practical problems associated with the adoption of such a rule.
Exploring the Variability in Institutional Characteristics Related to Meeting Various National STEM Baccalaureate Agendas

Science, technology, engineering, and mathematics (STEM) professions are said to drive the American economy, provide access to the middle class for underrepresented minorities, and bolster national security. Since the launch of Sputnik in 1957, American STEM educators have been under pressure to improve STEM educational outcomes. In order to respond to these pressures, education policy makers must understand the relationship between institutional characteristics and STEM outcomes. In this study, I articulate three specific national STEM agendas, and then I explore the relationship between these agendas and the institutional characteristics of America’s four-year colleges and universities. Utilizing data from the U.S. Department of Education Integrated Postsecondary Education Data System (IPEDS), I operationalized three dependent variables and 108 independent variables, and studied the relationship between each combination of independent and dependent variables. The purpose of my exploratory research was to determine which types of four-year colleges and universities are most likely to produce vi higher proportions of: (1) STEM graduates, (2) traditionally underrepresented STEM graduates, and (3) high-demand STEM graduates. Through my research, I concluded that there are indeed distinct differences in institutional characteristics relative to the three national agendas, and that these differences appear to be related to institutional size, socioeconomic status of students, institutional wealth, ACT/SAT math scores, student academic achievement, institutional STEM mission, institutional research mission, sector, and diversity of students and