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"abstract": "Mining users'= topics of interest is one of the most important tasks for social media services. Given known topic associations for some fraction of the users in an online microblogging platform, our goal is to infer the topics of interest for the remaining users in the same site. Specifically, we proposed a novel bi-relational graph model to capture the interactions among users and their shared topics of interests. The proposed graph model contains two sub-graphs: one corresponds to users and the other corresponds to topics. Such a representation allows for effective exploitation of both user homophily relation and topic correlation simultaneously. This is in contrast with previous work where these two factors are considered in isolation. Subsequently, the user interest discovery problem is formulated as a multi-label learning problem on the bi-relational graph, with the goal to estimate the optimized associations between user nodes and topic nodes across the two sub-graphs. Our experiment is carried out with a complete month-long data collected from Twitter and Tumblr via GNIP Decahose1 and Firehose2 respectively. The large-scale studies shed light on the effectiveness of inferring user interests based on the underlying social connections.",
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"content": "Mining users'= topics of interest is one of the most important tasks for social media services. Given known topic associations for some fraction of the users in an online microblogging platform, our goal is to infer the topics of interest for the remaining users in the same site. Specifically, we proposed a novel bi-relational graph model to capture the interactions among users and their shared topics of interests. The proposed graph model contains two sub-graphs: one corresponds to users and the other corresponds to topics. Such a representation allows for effective exploitation of both user homophily relation and topic correlation simultaneously. This is in contrast with previous work where these two factors are considered in isolation. Subsequently, the user interest discovery problem is formulated as a multi-label learning problem on the bi-relational graph, with the goal to estimate the optimized associations between user nodes and topic nodes across the two sub-graphs. Our experiment is carried out with a complete month-long data collected from Twitter and Tumblr via GNIP Decahose1 and Firehose2 respectively. The large-scale studies shed light on the effectiveness of inferring user interests based on the underlying social connections.",
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"abstract": "Microblog network, with its structural complexity and variety, prevents trivial network visual analytical tools from providing effective insight into the diffusion process of information and the effect of human participation on it. Therefore, we propose an advanced system to help users identify the key players and their various roles in the propagation of microblogs and thus analyze them based on the relations of relevant entities in between, by connecting multiple steps of filtering.",
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"affiliation": "Peking University",
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"affiliation": "Peking University",
"fullName": "Zipeng Liu",
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"affiliation": "Peking University",
"fullName": "Cong Guo",
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"affiliation": "Peking University",
"fullName": "Hongwei Ai",
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"affiliation": "University of California, Santa Barbara",
"fullName": "Donghao Ren",
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"abstract": "Due to the potential applications in social and political science, research on public opinion formation and diffusion have been increasing for a long time. Many researchers have developed numerous dynamics models, and they have used a wide variety of statistical, computational, and mathematical methods to understand the spread of public opinion in online social networks. So far, in some standard public opinion diffusing models, the transition probability from ignorants to spreaders is always treated as a constant. However, from a realistic perspective view, an individual whether or not be infected by the neighbor spreader greatly depends on the trustiness between them. In this paper, we introduced a public opinion diffusion model with variable transition probability parameter, in which the infectious probability was partly defined as an exponent function of the spreaders, and covered probability was also defined as an exponent function of the stiflers. Furthermore, we proposed a public opinion diffusion dynamics model with isolation class for hindering the diffusion of public opinion. Finally, we investigated numerically the behavior of the proposed models. Our findings may offer some useful insights in understanding the underlying dynamics of public opinion in online social networks.",
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"abstract": "Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.",
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"content": "Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.",
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"affiliation": "Worcester Polytechnic Institute,Department of Computer Science,Worcester,USA",
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"abstract": "In this paper, we used Ishii et al.'s new opinion dynamics theory that includes both trust and distrust in human relationships to simulate the transition of opinions of new entrants. When the mass media had a uniform impact on the market, it is shown that people's opinion distribution is biased toward media-led. We have observed that the media affects those in the market first, and then new entrants. It has also been shown that when the connection between people is strong, they are more influenced by others. In other words, in order to make the opinions of consumers who enter the market later positive(adopt), it is shown that we need the consumers with positive opinions that exist in the market in advance, mass media that encourages adopt (stronger is preferable), and dense people network.",
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"abstract": "Social media data bear valuable insights regarding events that occur around the world. Events are inherently temporal and spatial. Existing visual text analysis systems have focused on detecting and analyzing past and ongoing events. Few have leveraged social media information to look for events that may occur in the future. In this paper, we present an interactive visual analytic system, CrystalBall, that automatically identifies and ranks future events from Twitter streams. CrystalBall integrates new methods to discover events with interactive visualizations that permit sensemaking of the identified future events. Our computational methods integrate seven different measures to identify and characterize future events, leveraging information regarding time, location, social networks, and the informativeness of the messages. A visual interface is tightly coupled with the computational methods to present a concise summary of the possible future events. A novel connection graph and glyphs are designed to visualize the characteristics of the future events. To demonstrate the efficacy of CrystalBall in identifying future events and supporting interactive analysis, we present multiple case studies and validation studies on analyzing events derived from Twitter data.",
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"content": "Social media data bear valuable insights regarding events that occur around the world. Events are inherently temporal and spatial. Existing visual text analysis systems have focused on detecting and analyzing past and ongoing events. Few have leveraged social media information to look for events that may occur in the future. In this paper, we present an interactive visual analytic system, CrystalBall, that automatically identifies and ranks future events from Twitter streams. CrystalBall integrates new methods to discover events with interactive visualizations that permit sensemaking of the identified future events. Our computational methods integrate seven different measures to identify and characterize future events, leveraging information regarding time, location, social networks, and the informativeness of the messages. A visual interface is tightly coupled with the computational methods to present a concise summary of the possible future events. A novel connection graph and glyphs are designed to visualize the characteristics of the future events. To demonstrate the efficacy of CrystalBall in identifying future events and supporting interactive analysis, we present multiple case studies and validation studies on analyzing events derived from Twitter data.",
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"abstract": "In true-life, the existence of many events which are occurred in the interval may cause uncertainty in events ordering. The inaccurate event has been introduced for sequential pattern mining to improve accuracy of computing support threshold. In this paper, we store a sequence in the chain table. Sequence with inaccurate event can be expressed expediently. Besides, precise support is introduced to evaluate the probability of a pattern contained in a sequence. And the probabilistic ordering is employed to handle overlapping events. In essence, probabilities of generated candidate pattern contained in an inaccurate sequence are computed. The maximal value in probabilities is selected as precise support of the sequence. The sum of all inaccurate sequence precise supports in the database is computed. If the ratio value between the sum and the length of database is not less than predefined minimum threshold, the generated candidate pattern is called frequent pattern. So some infrequent patterns might be turned to be frequent and some interesting patterns could not be missed. Performance analysis shows the accuracy of discovering frequent patterns is improved.",
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"content": "In true-life, the existence of many events which are occurred in the interval may cause uncertainty in events ordering. The inaccurate event has been introduced for sequential pattern mining to improve accuracy of computing support threshold. In this paper, we store a sequence in the chain table. Sequence with inaccurate event can be expressed expediently. Besides, precise support is introduced to evaluate the probability of a pattern contained in a sequence. And the probabilistic ordering is employed to handle overlapping events. In essence, probabilities of generated candidate pattern contained in an inaccurate sequence are computed. The maximal value in probabilities is selected as precise support of the sequence. The sum of all inaccurate sequence precise supports in the database is computed. If the ratio value between the sum and the length of database is not less than predefined minimum threshold, the generated candidate pattern is called frequent pattern. So some infrequent patterns might be turned to be frequent and some interesting patterns could not be missed. Performance analysis shows the accuracy of discovering frequent patterns is improved.",
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"abstract": "The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close-interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatio-temporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.",
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{
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"content": "The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close-interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatio-temporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.",
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"abstract": "The dynamics between technologies and everyday life is increasingly volatile, pushing for greater convenience and ease of both trivial and complex processes. A consequence of the wide implementation of Internet of Things-based devices in smart city applications clearly reflects the mentioned dynamics by the huge amount of data to be handled. To extract useful information from such data, a clever approach to conduct data analysis is the interdisciplinary one, i.e., engineering and computer scientists as close partners of the problem domain experts. This paper addresses IoT data of a Public transportation System (PTS) aiming at contributing to the data analysis process for new insights about bus based PTS applications. Open Data of Curitiba PTS was explored looking for approaches to handle and analyze this kind of data, aiming to find important information for users, managers and planners of the public transport network in Curitiba.",
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"content": "Public Distribution System is a mechanism to enhance the food security in India. It is basically a food grain supply chain to serve the needy population of the country. PDS currently suffers from problems like ghost beneficiaries, leakage, wastage of resources, non- accountability, non-transparency, etc. One of the major problems present in this system is the existence of duplicate and bogus ration cards. The Unique Identification Authority of India uniquely identifies each individual in the country. This mechanism could help us in the elimination of the problem of duplicate and bogus ration cards. In this paper, there exists an empirical study on the unique identification mechanism to identify the beneficiaries in PDS and Agent-Based social simulation of PDS. Public Distribution System is a non-profit system and the subject considered here is a supply chain that is distributed across all the states of India. The social phenomena of food grain supply chain i.e. Public Distribution System is simulated based on the principles of a multi agent based system. A multi agent based simulation and modeling toolkit namely, Netlogo is used to generate the virtual supply chain network of the Public Distribution System.",
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"title": "A Case Study of CPNS Intelligence: Provenance Reasoning over Tracing Cross Contamination in Food Supply Chain",
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"abstract": "A Cyber-Physical System (CPS) is a system featuring a tight combination and coordination of the system's computational and physical elements. CPS integrates the executive ability of the physical world and the intelligence of the cyber world to add new capabilities to real-world physical systems. Recent years has witnessed the thriving of various applications in Cyber-Physical Networked System (CPNS), one of which is food distribution industry. Food supply chain is a typical case of model of networked systems. As food safety is becoming an increasing concern over the world, assurance in the quality and trace ability in food supply chain is essential. While data collection of food is available with CPNS, intelligent sensing and process in CPNS is insufficient, e.g., though it is easy to trace the origin of food, finding the source of cross contamination is an unsolved critical issue. In this paper, a case study of CPNS intelligence is presented to provide solutions for ceasing outbreaks of food borne disease. As the case of provenance reasoning, a heuristic approach to tracing cross contamination is studied, which is comprised of dynamic partition sampling strategy and heuristic tracing algorithm. With satisfactory performance and accuracy results for our approach in our simulation, we further suggest strategies regarding provenance reasoning to address the challenges of provenance as an open issue in cloud computing and domain specific intelligence (D.S.I) in Internet of Things (IoT) and CPS.",
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"content": "A Cyber-Physical System (CPS) is a system featuring a tight combination and coordination of the system's computational and physical elements. CPS integrates the executive ability of the physical world and the intelligence of the cyber world to add new capabilities to real-world physical systems. Recent years has witnessed the thriving of various applications in Cyber-Physical Networked System (CPNS), one of which is food distribution industry. Food supply chain is a typical case of model of networked systems. As food safety is becoming an increasing concern over the world, assurance in the quality and trace ability in food supply chain is essential. While data collection of food is available with CPNS, intelligent sensing and process in CPNS is insufficient, e.g., though it is easy to trace the origin of food, finding the source of cross contamination is an unsolved critical issue. In this paper, a case study of CPNS intelligence is presented to provide solutions for ceasing outbreaks of food borne disease. As the case of provenance reasoning, a heuristic approach to tracing cross contamination is studied, which is comprised of dynamic partition sampling strategy and heuristic tracing algorithm. With satisfactory performance and accuracy results for our approach in our simulation, we further suggest strategies regarding provenance reasoning to address the challenges of provenance as an open issue in cloud computing and domain specific intelligence (D.S.I) in Internet of Things (IoT) and CPS.",
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"abstract": "Techniques based on discrete-event simulation have been widely used for network analysis and policy optimization in the domain of supply chain management. Previous researchers have developed and implemented architectures for simulation-based control for shop floor. A more detailed and high-fidelity simulation model is used for control purposes as opposed to that used for analytical purposes alone. This paper discusses the issues related to implementing a simulation based control architecture for actively controlling supply chain interactions.",
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"content": "Techniques based on discrete-event simulation have been widely used for network analysis and policy optimization in the domain of supply chain management. Previous researchers have developed and implemented architectures for simulation-based control for shop floor. A more detailed and high-fidelity simulation model is used for control purposes as opposed to that used for analytical purposes alone. This paper discusses the issues related to implementing a simulation based control architecture for actively controlling supply chain interactions.",
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"abstract": "An increased incidence of food mislabeling and handling in recent years has led to consumers demanding transparency in how food items are produced and handled. The current traceability solutions suffer from issues such as scattering of information across multiple silos and susceptibility in recording erroneous data and thus are often unable to produce reliable farm to fork stories of products. Blockchain (BC) is a promising technology that could play an important role in providing data transparency and integrity due to its salient features which include decentralisation, immutability and auditability. In this paper, we propose a permissioned blockchain framework which is governed by a consortium of key Food Supply Chain (FSC) entities including government and regulatory bodies to promote food provenance. We propose to use a sharded, three-tiered architecture which ensures availability of data to consumers, limits access to competitive partners and provides scalability for handling transaction load. We also propose a transaction vocabulary and access rights to manage read and write privileges to BC supported by the consortium. The framework, ProductChain, ensures that trade flows are kept confidential when provenance information is retrieved by consumers and stakeholders. Simulation results show that query time for a product ledger is of the order of a few milliseconds even when the information is collated from multiple shards. ProductChain is generalised and applicable to supply chains in diverse industries.",
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"content": "An increased incidence of food mislabeling and handling in recent years has led to consumers demanding transparency in how food items are produced and handled. The current traceability solutions suffer from issues such as scattering of information across multiple silos and susceptibility in recording erroneous data and thus are often unable to produce reliable farm to fork stories of products. Blockchain (BC) is a promising technology that could play an important role in providing data transparency and integrity due to its salient features which include decentralisation, immutability and auditability. In this paper, we propose a permissioned blockchain framework which is governed by a consortium of key Food Supply Chain (FSC) entities including government and regulatory bodies to promote food provenance. We propose to use a sharded, three-tiered architecture which ensures availability of data to consumers, limits access to competitive partners and provides scalability for handling transaction load. We also propose a transaction vocabulary and access rights to manage read and write privileges to BC supported by the consortium. The framework, ProductChain, ensures that trade flows are kept confidential when provenance information is retrieved by consumers and stakeholders. Simulation results show that query time for a product ledger is of the order of a few milliseconds even when the information is collated from multiple shards. ProductChain is generalised and applicable to supply chains in diverse industries.",
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"title": "Leveraging In Situ Data Analysis to Enable Computational Steering of Brain's Neocortex Simulations with GENESIS",
"normalizedTitle": "Leveraging In Situ Data Analysis to Enable Computational Steering of Brain's Neocortex Simulations with GENESIS",
"abstract": "To investigate the origin and functional role of the brain's electrical activity in specific frequency ranges, scientists have employed biologically accurate computational models of the brain. The onset of extreme-scale computing brings the promise of allowing neuroscientists the ability to examine the brain in silico at unprecedented fidelity and resolution that is not possible using traditional methods such as the electroencephalogram. The large amount of data produced by high fidelity simulations are becoming increasingly difficult to save and transform into scientific insights for runtime simulations. One attractive solution is to modify the data analysis workflow from one that is accomplished post-simulation to one that is completed in situ at the cost of confining the analysis to the level of local data rather than a single global view. The missing global view of data has the potential to introduce inaccuracies in the simulation's findings. In this paper, we present the integration of in situ analysis into a simulation of the electrical activity of a brain's neuronal network to enable computational steering of the simulation itself at runtime (i.e., without stopping, performing post-simulation analysis, and restarting the simulation). We evaluate the accuracy of our in situ analysis versus the traditional post-simulation analysis, showing how we can gain meaningful global insights from local data. We demonstrate the integration's effectiveness in steering the electrical activity of a simulated neuronal network at runtime from electroencephalograph's (EEG) alpha frequency band (8-13Hz) to the beta frequency band (13-40Hz).",
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"content": "To investigate the origin and functional role of the brain's electrical activity in specific frequency ranges, scientists have employed biologically accurate computational models of the brain. The onset of extreme-scale computing brings the promise of allowing neuroscientists the ability to examine the brain in silico at unprecedented fidelity and resolution that is not possible using traditional methods such as the electroencephalogram. The large amount of data produced by high fidelity simulations are becoming increasingly difficult to save and transform into scientific insights for runtime simulations. One attractive solution is to modify the data analysis workflow from one that is accomplished post-simulation to one that is completed in situ at the cost of confining the analysis to the level of local data rather than a single global view. The missing global view of data has the potential to introduce inaccuracies in the simulation's findings. In this paper, we present the integration of in situ analysis into a simulation of the electrical activity of a brain's neuronal network to enable computational steering of the simulation itself at runtime (i.e., without stopping, performing post-simulation analysis, and restarting the simulation). We evaluate the accuracy of our in situ analysis versus the traditional post-simulation analysis, showing how we can gain meaningful global insights from local data. We demonstrate the integration's effectiveness in steering the electrical activity of a simulated neuronal network at runtime from electroencephalograph's (EEG) alpha frequency band (8-13Hz) to the beta frequency band (13-40Hz).",
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"Brain",
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"Unprecedented Fidelity",
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"Enable Computational Steering",
"Traditional Post Simulation Analysis",
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"Functional Role",
"Specific Frequency Ranges",
"Biologically Accurate Computational Models",
"In Situ Data Analysis",
"Frequency 8 0 Hz To 13 0 Hz",
"Frequency 13 0 Hz To 40 0 Hz",
"Brain Modeling",
"Analytical Models",
"Computational Modeling",
"Data Models",
"Biological System Modeling",
"Neurons",
"Big Data",
"In Situ Analysis",
"Model And Simulation",
"High Performance Computing"
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"fullName": "Alfred Yu",
"givenName": "Alfred",
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"title": "Research on the Establishment of Virtual Assembly System for Production Line of PET Hot Filling Beverages Driven by Food Technological Parameters",
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"abstract": "As for the long assembly cycle, high complexity, strongly professional layout design, high cost consumption and other issues of food engineering equipment, with the production line of PET hot filling beverages as the research object, this Paper achieves breakthroughs in the virtual assembly technology of the whole line, covering homogenization, sterilization, filling, packaging, cleaning and disinfection and other critical equipment of food processing, conducts a research on the mapping rules of food production technological parameters and equipment use parameters, and optimizes the equipment assembly model of PET hot filling beverages. At the same time, the virtual assembly system of PET hot filling beverages which has such functions as regional design, compliance verification and cross contamination pre-judgment of human flow and material flow is realized by virtual simulation technology of computer 3D image.",
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"content": "As for the long assembly cycle, high complexity, strongly professional layout design, high cost consumption and other issues of food engineering equipment, with the production line of PET hot filling beverages as the research object, this Paper achieves breakthroughs in the virtual assembly technology of the whole line, covering homogenization, sterilization, filling, packaging, cleaning and disinfection and other critical equipment of food processing, conducts a research on the mapping rules of food production technological parameters and equipment use parameters, and optimizes the equipment assembly model of PET hot filling beverages. At the same time, the virtual assembly system of PET hot filling beverages which has such functions as regional design, compliance verification and cross contamination pre-judgment of human flow and material flow is realized by virtual simulation technology of computer 3D image.",
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"title": "2019 IEEE International Conference on Cluster Computing (CLUSTER)",
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"title": "STASH : Fast Hierarchical Aggregation Queries for Effective Visual Spatiotemporal Explorations",
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"abstract": "The proliferation of sensors and observational instruments enable scientists to explore natural, spatiotemporal phenomena via explorative analysis and advanced modeling. Geospatial visualization, in particular, is an intuitive tool to identify patterns, enhance understanding of the data, and plan for subsequent analysis. However, seamless interactions between end-user devices and the sheer volume of data have been a challenge due to the limited bandwidth and data access latencies.In this paper, we introduce Stash, a distributed, in-memory cache for hierarchical aggregation and query evaluations. Stash is a middleware which can be loaded on top of a distributed file system. Users perform queries from a lightweight visualization interface at the front-end and the evaluations occur over the back-end storage system housing the raw data over which summarization and subsequent visualizations are to be performed. Stash facilitates fast exploratory analytics by caching relevant past query results based on their frequency and freshness to assist similar, future queries and avoid expensive disk I/O and network usage, thus reducing their latency. Additionally, Stash handles any hotspot that might result from a spike in user requests due to the spatial and temporal locality of their access patterns.Our empirical benchmarks show that a Stash-enabled system reduces query latency of a basic system by over 5-folds and brings it down to interactive speed even for large country-sized spatiotemporal queries. We have contrasted Stash with existing cache-enabled analytics engines, such as ElasticSearch, and found that our STASH-enabled system reduced the aggregation query latency up to ~70%. STASH also alleviated skewed workloads through its dynamic replication scheme and improved throughput by ~40% in hotspot scenarios.",
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{
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"content": "The proliferation of sensors and observational instruments enable scientists to explore natural, spatiotemporal phenomena via explorative analysis and advanced modeling. Geospatial visualization, in particular, is an intuitive tool to identify patterns, enhance understanding of the data, and plan for subsequent analysis. However, seamless interactions between end-user devices and the sheer volume of data have been a challenge due to the limited bandwidth and data access latencies.In this paper, we introduce Stash, a distributed, in-memory cache for hierarchical aggregation and query evaluations. Stash is a middleware which can be loaded on top of a distributed file system. Users perform queries from a lightweight visualization interface at the front-end and the evaluations occur over the back-end storage system housing the raw data over which summarization and subsequent visualizations are to be performed. Stash facilitates fast exploratory analytics by caching relevant past query results based on their frequency and freshness to assist similar, future queries and avoid expensive disk I/O and network usage, thus reducing their latency. Additionally, Stash handles any hotspot that might result from a spike in user requests due to the spatial and temporal locality of their access patterns.Our empirical benchmarks show that a Stash-enabled system reduces query latency of a basic system by over 5-folds and brings it down to interactive speed even for large country-sized spatiotemporal queries. We have contrasted Stash with existing cache-enabled analytics engines, such as ElasticSearch, and found that our STASH-enabled system reduced the aggregation query latency up to ~70%. STASH also alleviated skewed workloads through its dynamic replication scheme and improved throughput by ~40% in hotspot scenarios.",
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"normalizedAbstract": "The proliferation of sensors and observational instruments enable scientists to explore natural, spatiotemporal phenomena via explorative analysis and advanced modeling. Geospatial visualization, in particular, is an intuitive tool to identify patterns, enhance understanding of the data, and plan for subsequent analysis. However, seamless interactions between end-user devices and the sheer volume of data have been a challenge due to the limited bandwidth and data access latencies.In this paper, we introduce Stash, a distributed, in-memory cache for hierarchical aggregation and query evaluations. Stash is a middleware which can be loaded on top of a distributed file system. Users perform queries from a lightweight visualization interface at the front-end and the evaluations occur over the back-end storage system housing the raw data over which summarization and subsequent visualizations are to be performed. Stash facilitates fast exploratory analytics by caching relevant past query results based on their frequency and freshness to assist similar, future queries and avoid expensive disk I/O and network usage, thus reducing their latency. Additionally, Stash handles any hotspot that might result from a spike in user requests due to the spatial and temporal locality of their access patterns.Our empirical benchmarks show that a Stash-enabled system reduces query latency of a basic system by over 5-folds and brings it down to interactive speed even for large country-sized spatiotemporal queries. We have contrasted Stash with existing cache-enabled analytics engines, such as ElasticSearch, and found that our STASH-enabled system reduced the aggregation query latency up to ~70%. STASH also alleviated skewed workloads through its dynamic replication scheme and improved throughput by ~40% in hotspot scenarios.",
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"Query Evaluations",
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"affiliation": "Colorado State University,Department of Computer Science,Fort Collins,USA",
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"affiliation": "Colorado State University,Department of Computer Science,Fort Collins,USA",
"fullName": "Sangmi Lee Pallickara",
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"abstract": "Low-altitude unmanned aerial systems (UAS) have a rapidly changing field of view for most sensor payloads, especially downward-looking high-resolution visual imagery. In this research, we explore visible-spectrum derived spatiotemporal awareness applied to companion sensor phenomenologies and computationally derived information, such as computer vision- based road extraction and maneuverability hazard localization. Specifically, short-span visual-temporal analysis is performed to understand the platform motion and therefore geospatial context of measurements, whether from sensors or analytical processes. The mathematical model of frame-to-frame platform motion is extracted as homographies and affine transformations that describe how one image \"fits into\" the next. By coupling these computed motion models with derived data, such as road segmentation and object localization, we show how this technique allows the development of temporal-confidence to aid scene understanding of a complex environment and results in more robust feature matching process. The resulting context-aware geospatial platform facilitates increasing complex spatiotemporal analytics for enhanced maneuverability and route planning for ground units from assistive low-altitude airborne platforms. The tools proposed show promise for increasing user security through spatial awareness in regions that are GPS-denied or unseen. In particular, these capabilities are demonstrated on a collection of sequential aerial images taken of a region with mixed use.",
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"content": "Low-altitude unmanned aerial systems (UAS) have a rapidly changing field of view for most sensor payloads, especially downward-looking high-resolution visual imagery. In this research, we explore visible-spectrum derived spatiotemporal awareness applied to companion sensor phenomenologies and computationally derived information, such as computer vision- based road extraction and maneuverability hazard localization. Specifically, short-span visual-temporal analysis is performed to understand the platform motion and therefore geospatial context of measurements, whether from sensors or analytical processes. The mathematical model of frame-to-frame platform motion is extracted as homographies and affine transformations that describe how one image \"fits into\" the next. By coupling these computed motion models with derived data, such as road segmentation and object localization, we show how this technique allows the development of temporal-confidence to aid scene understanding of a complex environment and results in more robust feature matching process. The resulting context-aware geospatial platform facilitates increasing complex spatiotemporal analytics for enhanced maneuverability and route planning for ground units from assistive low-altitude airborne platforms. The tools proposed show promise for increasing user security through spatial awareness in regions that are GPS-denied or unseen. In particular, these capabilities are demonstrated on a collection of sequential aerial images taken of a region with mixed use.",
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"Autonomous Aerial Vehicles",
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"abstract": "Multi-Agent Systems have been in the focus of research interest for a long time. These systems are used widely in such areas as logistics, management, various simulations, game development and many others. In this paper, we search for a method to increase flexibility and efficiency of Multi-Agent Systems by investigating the usefulness of Decision Support Systems for intelligent agents in multi-agent environment. We also research advantages, such as the ability to add new agents at run-time, and disadvantages, such as increased complexity, of creating and modifying Decision Support Systems dynamically in Multi-Agent System.",
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"abstract": "Numerous clinical trials have revealed that a serious consequence of polypharmacy is that patients are at high risk of adverse side effects. However, designing clinical trials to determine the frequency of side effects from polypharmacy is both time-consuming and costly. Therefore, the computer-aided prediction of drug side effects is becoming an attractive proposition. Existing methods of drug side effects prediction introduce the target protein of a drug without screening. Although this alleviates the sparsity of the original data to some extent, the blind introduction of proteins as auxiliary information allows a large amount of noisy information to be added, which degrades the model efficiency and acheive sub-opitmal predicition results. To this end, we propose a novel method called DEP-GCN (Drug Side Effects Prediction via Heterogeneous Multi-Relational Graph Convolutional Networks). Specifically, we design two protein auxiliary pathways directly related to drugs and combine these two auxiliary pathways with a multi-relational graph of drug side effects, which both alleviate the sparsity of data and filter out noisy data. Then, to produce accurate drug representations, we distinguish the impact from different drug neighbors and introduce a query-aware attention mechanism to fine-grained determine how much messaging is delivered. Finally, in contrast to approaches limited to predicting the existence or associations of drug side effects, we output the exact frequency of drug side effects occurring via a tensor factorization decoder. Extensive experimental results demonstrate that DEP-GCN significantly outperforms all baseline methods. The further examination provides literature evidence for highly ranked predictions.",
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"content": "The design of ocean-going fishing vessels usually has to consider their main parameters. In this paper, based on the data of more than 2,000 fishing vessels provided by the Fisheries and Fisheries Administration of the Ministry of Agriculture and Rural Affairs, the main parameters of fishing tackle vessels, fishing trawler vessels and seine fishing vessels are studies by regression analysis, and the relationship between the length and other parameters are obtained. These formulas may be used in the design of ocean-going fishing vessels in the future.",
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"abstract": "In recent years, constructing regulatory networks using gene expression data has received extensive attentions. From Boolean network, Bayesian network to Module network, a number of models have been applied in order to learn the regulatory networks more accurately. The statistical power of network modeling is directly affected by sample size of available expression data used as training data. However, training data are not always abundantly available, except a few well-studied model organisms. It is also infeasible to perform a large number of experiments which require a lot of resources and labor. How to learn a reliable network using minimal training data making use of well-characterized model organisms becomes an important problem with pressing needs. In this paper, we developed a method that infers regulatory sub-networks for a species with limited expression data by learning from a known reference network through orthologous gene mapping. Inspection of three predicted sub-networks confirms biological relevance of our predictions and demonstrates the ability of the method in extracting core regulatory relationships.",
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"title": "Integrative Gene Regulatory Network inference using multi-omics data",
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"abstract": "Biological network inference is of importance to understand underlying biological mechanisms. Gene regulatory networks describe molecular interactions of complex biological processes. Graph models are mainly used for gene regulatory networks, where nodes and edges represent genes and their regulations respectively. In the most research, the molecular interactions (edges) of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data. However, gene expression is a product of sequential interactions of DNA sequence variations, single nucleotide polymorphism, copy number variation, histone modifications, transcription factor, DNA methylation, and many other factors. There are high-throughput genomic data that measure the various biological processes. We call the multiple types of genomics data as ‘multi-omics data’. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that can incorporate multi-omics data and their interactions in the graph model of gene regulatory network. Copy number variation and DNA methylation were considered for multi-omics data in this paper. The proposed method, iGRN, was applied to the human brain data of psychiatric disorder. Through the experiments, iGRN showed its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference.",
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"content": "Biological network inference is of importance to understand underlying biological mechanisms. Gene regulatory networks describe molecular interactions of complex biological processes. Graph models are mainly used for gene regulatory networks, where nodes and edges represent genes and their regulations respectively. In the most research, the molecular interactions (edges) of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data. However, gene expression is a product of sequential interactions of DNA sequence variations, single nucleotide polymorphism, copy number variation, histone modifications, transcription factor, DNA methylation, and many other factors. There are high-throughput genomic data that measure the various biological processes. We call the multiple types of genomics data as ‘multi-omics data’. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that can incorporate multi-omics data and their interactions in the graph model of gene regulatory network. Copy number variation and DNA methylation were considered for multi-omics data in this paper. The proposed method, iGRN, was applied to the human brain data of psychiatric disorder. Through the experiments, iGRN showed its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference.",
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"affiliation": "Department of Computer Science, Kennesaw State University, Marietta, GA, USA",
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"affiliation": "Department of Computer Science, Kennesaw State University, Marietta, GA, USA",
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"abstract": "Gene regulatory network expansion is a task of the foremost importance in computational biology that aims at finding new genes to expand a given known gene regulatory network. To this end we present OneGenE, a novel framework for gene regulatory network expansion that relies on the BOINC platform. OneGenE is an evolution of the NES2RA algorithm, with the aim to overcome its main criticality, i.e. long response time for the final user. To achieve this goal, candidate expansion lists are pre-computed for each gene in the organism and then aggregated at runtime to produce the the final expansion list for a given known gene regulatory network. We validated OneGenE on the expression data of Pseudomonas aeruginosa, comparing its results with the one obtained by NES2RA and through a biological literature review.",
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"title": "Computing Minimal Boolean Models of Gene Regulatory Networks",
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"abstract": "Models of Gene Regulatory Networks (GRNs) capture the dynamics of the regulatory processes that occur within the cell as a means to understand the variability observed in gene expression between different conditions. Arguably the simplest mathematical construct used for modeling is the Boolean network, which dictates a set of logical rules for transition between states described as Boolean vectors. Due to the complexity of gene regulation and the limitations of experimental technologies, in most cases knowledge about regulatory interactions and Boolean states is partial. In addition, the logical rules themselves are not known a-priori. Our goal in this work is to create an algorithm that finds the network that fits the data optimally, and identify the network states that correspond to the noise-free data. We present a novel methodology for integrating experimental data and performing a search for the optimal consistent structure via optimization of a linear objective function under a set of linear constraints. In addition, we extend our methodology into a heuristic that alleviates the computational complexity of the problem for datasets that are generated by single-cell RNA-Sequencing(scRNA-Seq). We demonstrate the effectiveness of these tools using a public scRNA-Seq dataset and the GRN that is associated with it. Our methodology wm enable researchers to obtain a better understanding of the dynamics of gene regulatory networks and their biological role.",
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"content": "Models of Gene Regulatory Networks (GRNs) capture the dynamics of the regulatory processes that occur within the cell as a means to understand the variability observed in gene expression between different conditions. Arguably the simplest mathematical construct used for modeling is the Boolean network, which dictates a set of logical rules for transition between states described as Boolean vectors. Due to the complexity of gene regulation and the limitations of experimental technologies, in most cases knowledge about regulatory interactions and Boolean states is partial. In addition, the logical rules themselves are not known a-priori. Our goal in this work is to create an algorithm that finds the network that fits the data optimally, and identify the network states that correspond to the noise-free data. We present a novel methodology for integrating experimental data and performing a search for the optimal consistent structure via optimization of a linear objective function under a set of linear constraints. In addition, we extend our methodology into a heuristic that alleviates the computational complexity of the problem for datasets that are generated by single-cell RNA-Sequencing(scRNA-Seq). We demonstrate the effectiveness of these tools using a public scRNA-Seq dataset and the GRN that is associated with it. Our methodology wm enable researchers to obtain a better understanding of the dynamics of gene regulatory networks and their biological role.",
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"abstract": "In this study, students behavioral patterns in an educational game were investigated by conducting a digital game-based learning activity in an elementary school science course. Moreover, the behavioral patterns of the highachievement and low-achievement students were compared to explore the possible educational gaming behavioral patterns affecting the students learning achievements. It was found that the high-achievement students showed more deep thinking and reflective behavioral patterns than the low-achievement ones, implying the necessity of incorporating learning strategies to guide students to have deep thinking and reflective behaviors during the digital game-based learning process. The finding of this study provides a good reference for researchers and school teachers who intend to employ the digital game-based learning approach.",
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"title": "2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)",
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"title": "An Efficient Method for Porosity Properties Extraction of Carbonate Rocks",
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"abstract": "Porous media characterization presents substantial importance for the oil industry. The X-ray micro-computed tomography (μCT) is often used to generate digital models of reservoir rocks. This paper presents an automatic histogram-based method for the segmentation of μ CT images, which allows the fast extraction of some petrophysical properties. The processing is based on the analysis of the typical 2D images used to produce 3D volumes. The method was applied to analyze seven samples of carbonate rocks, to define the porosity values, pores size distribution, and the orientation of the pores. Calculated porosity values were compared to the porosity results obtained with a helium porosimeter. The comparison of porosity calculated by this method against the experimental values showed an average error of 3.43%. The computational time spent for each sample processing was of about 9 minutes on a regular PC. The method can be used to extract porosity parameters in a large number of samples with a substantial gain of time and computational power.",
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"content": "Porous media characterization presents substantial importance for the oil industry. The X-ray micro-computed tomography (μCT) is often used to generate digital models of reservoir rocks. This paper presents an automatic histogram-based method for the segmentation of μ CT images, which allows the fast extraction of some petrophysical properties. The processing is based on the analysis of the typical 2D images used to produce 3D volumes. The method was applied to analyze seven samples of carbonate rocks, to define the porosity values, pores size distribution, and the orientation of the pores. Calculated porosity values were compared to the porosity results obtained with a helium porosimeter. The comparison of porosity calculated by this method against the experimental values showed an average error of 3.43%. The computational time spent for each sample processing was of about 9 minutes on a regular PC. The method can be used to extract porosity parameters in a large number of samples with a substantial gain of time and computational power.",
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"keywords": [
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"affiliation": "Federal University of Pernambuco,Center of Informatics,Recife,Pernambuco,Brazil",
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"abstract": "The performance of DF-based beamformers is seriously degraded in situations where the array is imprecisely calibrated, or when the spatial coherence of the signal wavefronts is perturbed. When the calibration errors or perturbation may be characterized by a set of parameters drawn from a known Gaussian distribution, a maximum a posteriori (MAP) estimator may be used to separately estimate the directions of arrival and the perturbation parameters, resulting in essentially an on-line auto-calibration. This paper examines the improvement that results from using the MAP auto-calibrated steering vectors in standard DF-based beamformers to estimate the received signal waveforms and suppress unwanted interference. For the special case of additive unstructured calibration errors and uncorrelated signals, it is shown that the MAP beamformer is similar in form to so-called \"subspace corrected\" approaches.",
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"content": "The performance of DF-based beamformers is seriously degraded in situations where the array is imprecisely calibrated, or when the spatial coherence of the signal wavefronts is perturbed. When the calibration errors or perturbation may be characterized by a set of parameters drawn from a known Gaussian distribution, a maximum a posteriori (MAP) estimator may be used to separately estimate the directions of arrival and the perturbation parameters, resulting in essentially an on-line auto-calibration. This paper examines the improvement that results from using the MAP auto-calibrated steering vectors in standard DF-based beamformers to estimate the received signal waveforms and suppress unwanted interference. For the special case of additive unstructured calibration errors and uncorrelated signals, it is shown that the MAP beamformer is similar in form to so-called \"subspace corrected\" approaches.",
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"affiliation": "Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT, USA",
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"title": "Analyzing Multivariate Calibration Techniques for Glucose Level Prediction in Non-invasive Human Tongue Spectra",
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"abstract": "Non-Invasive Glucose Monitoring devices use Multivariate Calibration (MC) methods to estimate glucose concentration in blood. The accuracy of methods depends on spectral data obtained from tongue-to-spectrometer interface. In this work we examine four widely used MC methods, they are: Classical Least Square (CLS), Inverse Least Square (ILS), Principal Component (PC) and Partial Least Square (PLS). We discuss the impact of factor selection on the prediction of response for first overtone transmission spectra collected across human tongues. Results are exposed for various Signal-to-Noise Ratio (SNR) and different factor values. We explicitly tackle the issue of low SNR in tongue-to-spectrometer interface. We show that CLS outperforms factor based regression techniques where SNR is as low as 30 dB. Our results are useful in calibration models that are used to predict vivo glycemia from human tongue spectra.",
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{
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"content": "Non-Invasive Glucose Monitoring devices use Multivariate Calibration (MC) methods to estimate glucose concentration in blood. The accuracy of methods depends on spectral data obtained from tongue-to-spectrometer interface. In this work we examine four widely used MC methods, they are: Classical Least Square (CLS), Inverse Least Square (ILS), Principal Component (PC) and Partial Least Square (PLS). We discuss the impact of factor selection on the prediction of response for first overtone transmission spectra collected across human tongues. Results are exposed for various Signal-to-Noise Ratio (SNR) and different factor values. We explicitly tackle the issue of low SNR in tongue-to-spectrometer interface. We show that CLS outperforms factor based regression techniques where SNR is as low as 30 dB. Our results are useful in calibration models that are used to predict vivo glycemia from human tongue spectra.",
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