Machine Learning Methods for functional Near Infrared Spectroscopy

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain. We propose machine learning methods that capture this spatial nature of the human brain activity using a novel preprocessing method that uses `Region of Interest\u27 based feature extraction. Experiments show that this method outperforms the F1 score achieved previously in classifying `low\u27 vs `high\u27 valence state of a user. We further our analysis by applying a Convolutional Neural Network (CNN) to the fNIRS data, thus preserving the spatial structure of the data and treating the data similar to a series of images to be classified. Going further, we use a combination of CNN and Long Short-Term Memory (LSTM) to capture the spatial and temporal behavior of the fNIRS data, thus treating it similar to a video classification problem. We show that this method improves upon the accuracy previously obtained by valence classification methods using EEG or fNIRS devices. Finally, we apply the above model to a problem in classifying combined task-load and performance in an across-subject, across-task scenario of a Human Machine Teaming environment in order to achieve optimal productivity of the system

Gain vs. Loss and Near vs. Far Spatial Distance Message Framing and Support for Aquaculture Among U.S. Seafood Consumers

In the U.S., the aquaculture industry receives differential support from various publics due to the health and environmental concerns of seafood consumers. Since consumer communication plays a significant role in policy support, understanding how messages about aquaculture should be framed is important. This study investigated the influence of gain vs. loss and near vs. far spatial distance framing on support for aquaculture among seafood consumers in the U.S. The study used a 2*2 experimental design to vary gain/loss and near/far framing among 1052 U.S. residents from all 50 states. An online questionnaire, distributed by the survey firm GfK, was employed to collect quantitative data. Gain frames highlight advantages of adhering to an expected behavioral outcome whereas loss frames highlight disadvantages of nonconforming to a given expectancy. In contrast, a near frame specifies spatial closeness to an event and the far frame is focused on spatially distal events. The framing literature reveals that message framing behaves in contradictory ways depending on the context. For instance, gain frames are more effective in influencing cautious behaviors but loss frames are more effective in inducing risky behaviors. Similarly, near vs. far spatial distance framing shows converging influences depending on research contexts. This study investigated three main research questions to identify what message frames may engender more support for aquacultures and tested for their interaction effect. Results suggest that age, gender, education, political orientation, region of the U.S., seafood consumption frequency, and message relevancy cause extra variation above the effect of the framing variables. Therefore, these variables were treated as covariates in the ANCOVA. Findings further indicated that the loss frame was more effective in increasing support for aquaculture than the gain frame. In addition, near and far spatial distance frames had no significant impact on the support for aquaculture at 5% probability levels. However, loss/near and loss/ far messages show a significant increase in support for aquaculture at the 10% probability level. Finally, gain vs. loss and near vs. far spatial distance frames do not have a significant interaction effect. The above findings indicate that emphasizing the losses of non-adoption of aquaculture in the U.S. (i.e., near) and China (i.e., far) for U.S. audiences may influence support for aquaculture policies, as compared to gain-framed messages. This study also poses implications for the seafood industry as it suggests that presenting a loss frame (as opposed to a gain frame) may lead to more support for aquaculture among U.S. consumers, when controlling for various individual characteristics. Loss frames highlight the disadvantages of not adopting aquaculture in a given location. In so doing, these messages may provoke thoughts about loss of employment opportunities, adverse economic effects of less adoption, and nutritional disadvantages of not consuming seafood, and thus lead to support for the aquaculture industry. Analyzing the mediation and moderation roles of message relevance and emotions, seafood consumption, aquaculture knowledge, perceived aquaculture benefits, source credibility, and political orientation is suggested as future research to this study

Nitrogen and harvest impact on warm-season grass biomass yield and feedstock quality

Perennial warm-season grasses including switchgrass (Panicum virgatum L.), big bluestem (Andropogon geradii Vitman), and Indiangrass (Sorghastrum nutans L.) have drawn interest as bioenergy feedstocks due to their high yielding capacity with minimal amounts of inputs under a wide range of environments, and their capability to produce multiple environmental benefits. Nitrogen (N) fertility and harvest timing are considered as critical management practices when optimizing biomass yield and the feedstock quality of these grasses. The objective of this investigation was to quantify the impact of N fertilizer rate, N timing and harvest date on warm season biomass dry matter yield. Research was conducted in 2014 and 2015 on a total of four field-plot locations situated in central and west-central Missouri. Nitrogen fertilizer was applied using dry ammonium nitrate at the rates of 0, 34, 67, and 101 kg ha-1 at two application times, all N early spring and split N (early spring and following 1st harvest). Harvest treatments were as follows: 1) one cut in September; 2) one cut in November; 3) one cut in June and a second in September; and 4) one cut in June and a second in November. Treatments were arranged in a split-plot design with N rate as the main plot and harvest as the sub-plot in arandomized complete block design. Both N and harvest date and their interactions impacted biomass yield at all four locations. Delaying harvesting until late fall or killing frost increased yield. November harvest in combination with N rates grater than or equal to 67 kg ha-1 year-1 produced higher yields compared to the control and 34 kg ha-1N treatments and other harvest timing strategies. Although N was needed to optimize yield, partial factor 24 productivity (PFP) of applied N was flat when N applied was greater than 34 kg ha-1. Nitrogen fertilization at 67 kg ha-1 per growing season provided an opportunity to maintain a balance between both yield and efficiency of N inputs. Results of this research highlight the interactions of N fertilization and harvest management have when optimizing yield of warm-season grasses grown as bioenergy feedstocks. List of acronyms: N, Nitrogen; PFP, partial factor productivity.Dr. Newell R. Kitchen, Thesis Supervisor.Includes bibliographical references

Socioeconomic and Environmental Factors Influencing Access to Resources in a Fishing Community: A case Study in Rekawa, Sri Lanka

Mode of production of a society is an essential element of the social system, and it can be influenced by different socio-economic and environmental factors. Hierarchical arrangements and contextspecific rules also can affect the way in which a particular community is organized. This study explores how social structure influences access to resources as claimed by different social segments in a fishing community in Sri Lanka. Because fishery in rural fisher communities requires a high level of communal labour, the role played by the community alongside government and private organizations in different activities of fishing is predominant. Based on a case study in Rekawa – a fishingcommunity in Sri Lanka, data were collected using participatory rapid appraisal and interviewer guided questionnaire. The study shows that different socio-economic and environmental factors influence different strata in the fishing-community differently when natural resources are accessed. In addition, the social hierarchy through which culture is imposed on anglers would then influence the way in which the access is granted to fishers. Consequently, people of Rekawa fishing-community are likely to reflect their social position in line with the identity they have perceived being a member of a particular fisher-group in the same community.DOI: http://doi.org/10.31357/fhss/vjhss.v05i02.0

Secure multi-party based cloud computing framework for statistical data analysis of encrypted data

Secure Multi-party Computation (SMC) is a paradigm used to accomplish a common computation among multiple users while keeping the data of each party secret from others. In recent years there has been a keen interest among the research community to look for techniques that can be adopted for the evolvement of SMC based solutions for improving its e ciency and performance. Cloud computing is a next generation computing solution in the eld of Information and Communication Technology (ICT) which allows its users to use high speed infrastructure and services provided by Cloud Service Providers (CSP) in a cost e ective manner with a higher availability. There- fore, deployment of cloud based architecture for SMCs would aid in improving its performance and e ciency. However, cloud based solutions raises concerns over secu- rity of users' private data, since data is handled by an external party that cannot be trusted. Hence, it is necessary to incorporate necessary security measures to ensure the security of users' private data. In this master's thesis we have addressed this issue by proposing a Secure Multi- party based Cloud Computing Framework which can ensure security, privacy and anonymity of users private data. In order to achieve this, we have formulated a case involving sales data analysis of a certain organization through computing statistical parameters of sales persons private sales data on a cloud environment. Furthermore, we have implemented a prototype of the proposed security framework which aids us to evaluate its performance. Moreover, considering the results that we have obtained, it is conclusive that cloud platforms can be successfully deployed to improve e ciency of SMCs while ensuring the security of users' private data; which in turn provides evidence for the practicability of multi-party based cloud computing solutions

Delay Distribution Based Remote Data Fetch Scheme for Hadoop Clusters in Public Cloud

Apache Hadoop and its ecosystem have become the de facto platform for processing large-scale data, or Big Data, because it hides the complexity of distributed computing, scheduling, and communication while providing fault-tolerance. Cloud-based environments are becoming a popular platform for hosting Hadoop clusters due to their low initial cost and limitless capacity. However, cloud-based Hadoop clusters bring their own challenges due to contradictory design principles. Hadoop is designed on the shared-nothing principle while cloud is based on the concepts of consolidation and resource sharing. Most of Hadoop\u27s features are designed for on-premises data centers where the cluster topology is known. Hadoop depends on the rack assignment of servers (configured by the cluster administrator) to calculate the distance between servers. Hadoop calculates the distance between servers to find the best remote server from which to fetch data from when fetching non-local data. However, public cloud environment providers do not share rack information of virtual servers with their tenants. Lack of rack information of servers may allow Hadoop to fetch data from a remote server that is on the other side of the data center. To overcome this problem, we propose a delay distribution based scheme to find the closest server to fetch non-local data for public cloud-based Hadoop clusters. The proposed scheme bases server selection on the delay distributions between server pairs. Delay distribution is calculated measuring the round-trip time between servers periodically. Our experiments observe that the proposed scheme outperforms conventional Hadoop nearly by 12% in terms of non-local data fetch time. This reduction in data fetch time will lead to a reduction in job run time, especially in real-world multi-user clusters where non-local data fetching can happen frequently

不確実なネットワーク条件におけるロバストな経路選択方式

電気通信大学201

Revisão geral dos minérios de titânio em exploração: estado atual e previsão

ABSTRACT: Titanium ore minerals have a unique spectrum of properties useful for modern-day industrial applications. This study focuses on the global distribution, genesis, processing, and economics of titanium ore minerals. Titanium ore deposits are distributed in 20 countries. Ilmenite (FeOTiO2), leucoxene (Fe2O3.nTiO2), and rutile (TiO2) are the major Ti ores. Titanium ore minerals in rocks (i.e., primary deposits) are products of magmatic, hydrothermal, metasomatic, and metamorphic processes. Titanium ore minerals are also concentrated as unconsolidated/placer deposits (i.e., secondary deposits) due to weathering (chemical, physical and biological), erosion, and transportation of sediments. About 60% of global Ti ore production comes from unconsolidated mineral sand deposits. China is the leading producer of ilmenite accounting for 31% of global production, primarily from hard-rock deposits. Australia and South Africa are also leading producers of ilmenite. In addition, Australia leads rutile production with a global share of 52%. Titanium ore minerals are used to extract TiO2 and Ti metal, using three major processes pyrometallurgy, hydrometallurgy, and electrometallurgy. Therefore, processed TiO2 and Ti metal are used in advanced applications such as the production of paints, aircraft, photovoltaic cells, medicines, and biomedical engineering. Substitutions are virtually impossible in most applications of TiO2 due to its unique physical and chemical properties. Time series analysis and forecast (using the R studio software) of global production and price variations of ilmenite and rutile indicate satisfactory growth rates, based on the United States Geological Survey (USGS) database and mineral yearbooks over 65 years from 1950 to 2015.info:eu-repo/semantics/publishedVersio

Preventive Start-Time Network Optimization for Energy Saving against Link Failure with Unpredictable Traffic Demand

電気通信大学201

Perception Enhanced Virtual Environment for Maritime Applications

This paper presents the development of a realtimeperception enhanced virtual environment for maritimeapplications which simulates real-time six degrees of freedomship motions (pitch, heave, roll, surge, sway, and yaw) underuser interactions, environmental conditions and various threatscenarios. This simulation system consists of reliable shipmotion prediction system and perception enhanced immersivevirtual environment with greater ecological validity. Thisvirtual environment supports multiple-display viewing that cangreatly enhance user perception and we developed the ecologicalenvironment for strong sensation of immersion. In this virtualenvironment it is possible to incorporate real world ships,geographical sceneries, several environmental conditions andwide range of visibility and illumination effects. This system canbe used for both entertainment and educational applications suchas consol level computer games, teaching & learning applicationsand various virtual reality applications. Especially this framework can be used to create immersive multi user environments