Modeling and Analyzing Collective Behavior Captured by Many-to-Many Networks

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Heterogeneous participation of developing countries in global value chains:A mixed methods analysis with focus on the Philippines

This thesis entitled Heterogeneous Participation of Developing Countries in Global Value Chains is an empirical investigation of the diverse modes of global value chain participation by developing countries. The organizing principle of the research is a mixed methods strategy that includes meta-regression analysis. The quantitative methodologies are applied to the latest gross and value added trade databases 2001 - 2018 (OECD-WTO Trade in Value Added indicators, 2021 edition) to achieve a more current assessment of global value chain participation after the Great Recession. The qualitative method of Global Value Chain framework structures the case study on the Philippine game development industry which contributes to the still nascent research on digital services value chains. The mixed methods approach further facilitates a methodological and data triangulation beyond the conclusions reached by the investigation of four dimensions of global value chain participation. The four dimension are as follows. First, the impact on firm productivity of global value chain participation is differentiated between developing and advanced countries through a meta-regression analysis performed on 1,083 firm productivity estimates. Second, participation in manufacturing global value chains is surveyed on the East Asian regional level – specifically, Indonesia, Malaysia, the Philippines, Thailand, and Vietnam - using intra-industry trade indices, value added trade indicators, and revealed comparative advantage. Third, the participation of the Philippines in global manufacturing and service value chains is examined using Constant Value Added Share analysis, a novel application of the constant market share decomposition method. Finally, the participation of the Philippine game development industry in the game production global value chain is analysed as a case study using the Global Value Chain framework. The overall findings of the thesis present a nuanced discussion of global value chain participation: there is a heterogeneity of participation between developing and advanced countries, among developing countries in a region, between sectors in a developing country, and within the digital service value chain of game production. Important factors that influence heterogeneity include firm productivity, competitiveness, power relations and upgrading, and confirm the complexity of developing country participation. The results of the meta-regression analysis suggest that the reported estimates are affected by severe publication bias and show evidence of a positive, but slight, genuine productivity effect for advanced countries. Global value chain participation in the five East Asian countries differed between countries that had joined global value chains in the 1990s and 2000s and gave mixed results for strong manufacturing sectors. The Constant Value Added Share decomposition showed slow growth in Philippine manufacturing but an improvement in the Value Added share effect through the information and communication service sector. The case study on the Philippine video game development industry revealed power asymmetry between lead firms in the pre-production and post-production segments and supplier firms in the production segment, and posed a hindrance to upgrading.<br/

Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction

Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity. Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results. In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied

Mapping Scholarly Communication Infrastructure: A Bibliographic Scan of Digital Scholarly Communication Infrastructure

This bibliography scan covers a lot of ground. In it, I have attempted to capture relevant recent literature across the whole of the digital scholarly communications infrastructure. I have used that literature to identify significant projects and then document them with descriptions and basic information. Structurally, this review has three parts. In the first, I begin with a diagram showing the way the projects reviewed fit into the research workflow; then I cover a number of topics and functional areas related to digital scholarly communication. I make no attempt to be comprehensive, especially regarding the technical literature; rather, I have tried to identify major articles and reports, particularly those addressing the library community. The second part of this review is a list of projects or programs arranged by broad functional categories. The third part lists individual projects and the organizations—both commercial and nonprofit—that support them. I have identified 206 projects. Of these, 139 are nonprofit and 67 are commercial. There are 17 organizations that support multiple projects, and six of these—Artefactual Systems, Atypon/Wiley, Clarivate Analytics, Digital Science, Elsevier, and MDPI—are commercial. The remaining 11—Center for Open Science, Collaborative Knowledge Foundation (Coko), LYRASIS/DuraSpace, Educopia Institute, Internet Archive, JISC, OCLC, OpenAIRE, Open Access Button, Our Research (formerly Impactstory), and the Public Knowledge Project—are nonprofit.Andrew W. Mellon Foundatio

Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis

Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.Comment: To appear at NAACL 202

Social media analytics with applications in disaster management and COVID-19 events

Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled methods that can provide appropriate tags or labels for further analysis for timely situation-awareness. In that direction, this work proposes machine learning models to identify the people who are seeking assistance using social media during a disaster and further demonstrates a prototype application that can collect and process Twitter data in real-time, identify the stranded people, and create rescue scheduling. In addition, to understand the people’s reactions to different trending topics, this work proposes a unique auxiliary feature-based deep learning model with adversarial sample generation for emotion detection using tweets related to COVID-19. This work also presents a custom Q&A-based RoBERTa model for extracting related phrases for emotions. Finally, with the aim of polarization detection, this research work proposes a deep learning pipeline for political ideology detection leveraging the tweet texts and the expressed emotions in the text. This work also studies and conducts the historical emotion and polarization analysis of the COVID-19 pandemic in the USA and several individual states using tweeter data --Abstract, page iv

Regional economic activity report 2014

Provides consistent information for each of New&nbsp;Zealand’s 16 regions. This allows us to compare the regions’ economic performance, distinguish their attributes and specialisations, and understand the different roles they play in the New&nbsp;Zealand economy. Summary The 2014 regional economic activity report report shows that nearly all of New Zealand\u27s 16 regions have made good economic progress over the last 12 months, reflecting New Zealand’s recovery after the global financial crisis. Each region provides a different contribution to the New&nbsp;Zealand economy and, while there is diversity, all regions have the potential to attract further investment, improve their living standards and generate high-value economic growth. Most regions experienced job growth over the last year. This is despite the 2013 drought which particularly affected primary production in the North Island. Canterbury has been the fastest growing region over the last two years, driven by the Christchurch rebuild and supported by its primary sector. Actions to enhance regional economic activity and outcomes need to be underpinned by a sound knowledge of each region’s historical trends and its strengths and weaknesses.This report provides comprehensive and comparative information about economic outcomes and the drivers of those outcomes across all regions. In addition, the government, in partnership with local decision-makers, is this year undertaking in-depth economic growth studies of regions such as East Coast, Northland, Bay of Plenty, and Manawatū-Wanganui. Those studies will help the regions prioritise opportunities for growth and identify how to overcome any barriers to that growth. This report highlights several key findings. First, each region has industry specialisations which have developed historically due to natural resource and infrastructure endowments, geographic location and skills. Those specialisations are the chief contributors to the different economic outcomes seen across the regions. Some sectors, such as dairy farming and milk processing, are benefiting from high commodity prices and market growth while others, such as horticulture, have lower returns. Secondly, the report identifies a regional dimension to the economic disparity between Māori and non-Māori. Some of the regions with poorer outcomes are also regions that have a higher proportion of Māori in their populations. The Crown and Māori have entered into an economic growth partnership to improve economic outcomes for Māori and to build economic growth from Māori assets and Māori Inc. This partnership will be delivered regionally and will include Business Growth Agenda actions such as the Māori and Pasifika Trades Training programme. Thirdly, the report shows there is significant diversity in demographic trends across regions, partly in response to relative economic opportunities. New&nbsp;Zealand, like all developed countries, has an ageing population but in some regions and sub-regions the population is ageing at a significantly faster rate than others. There is also disparity in regional shares of international migrants. Local decision-makers face the need to anticipate today how their projected population profiles will impact infrastructure and services demand

Identifying functionally and topologically cohesive modules in protein interaction networks

Abstract unavailable please refer to PD

Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

Markovian-based clustering of internet addiction trajectories

A hidden Markov clustering procedure is applied to a sample of n=185 longitudinal Internet Addiction Test trajectories collected in Switzerland. The best solution has 4 groups. This solution is related to the level of emotional wellbeing of the subjects, but no relation is observed with age, gender and BMI