An Approach to Argumentation Context Mining from Dialogue History in an E-Market Scenario

Argumentation allows agents to exchange additional information to argue about their beliefs and other mental attitudes during the negotiation process. Utterances and subsequent observations may differ during argumentation due to the gap in internal and external information with other agent. Contextual information is one reason of deviation between utterance and subsequent observations. Historic dialogues are a key source for extracting contextual information regarding illocutions, ontological category or semantically similar category. How historical dialogues contribute to contextual information during argument generation, selection and evaluation process is crucial to modeling the commonsense that human being apply in managing dialogues. Identifying, managing and augmenting contextual information and use that information in agent dialogue requires attention to several dimensions, e.g., illocution, interaction protocol, ontology, context, contract etc. which is an important problem in electronic market research area. This paper presents an approach for extraction of argumentation context from historical dialogues between intelligent agents in e-market. We are developing an argumentation system to extract context from historical dialogue and exploit context for dialogue moves between agents. An agent architecture using context monitor, context network, context miner is presented for argumentation context minin

An argumentation system that builds trusted trading partnerships

University of Technology, Sydney. Faculty of Engineering and Information Technology.In e-Commerce, a buying process typically begins with browsing the available products or services, and then selecting the ones that satisfy a given need. The next phase is negotiation to reach an agreement. If an agreement is signed between two parties, they enter into the enactment phase including payment and delivery. After that, they evaluate how well the products or services satisfy their needs. One of the reasons for dissatisfaction is that a trading agent does not know its opponent agent's needs, contract acceptance criteria, or behaviour during their interactions. This dissertation is concerned with the problems and challenges of repeatedly conducted trading activities in e-Commerce applications. Argumentation is a mode of interaction between agents that enables them to exchange information within messages in the form of arguments to explain their current position and future plans with the intention of increasing the chance of success in the negotiation. How an agent conducts all phases of a buying process through argumentation is an important research query. It becomes difficult to solve this query if an agent has to repeatedly conduct trading activities with its opponent agents. This work describes a novel solution to how an agent builds trusted trading partnerships with its opponent agents. The requirements of all phases of a buying process are specified by five models: the needs model, the opponent agent selection model, the communication model, the agreement model, and the relationship model. The relationship aware argumentation framework is then proposed. It integrates how the trading agents analyze their interaction history, exchanged information, and any promises made. An agent architecture is then developed that extends the idea of information based agency. It measures the strength of business relationships and predicts behavioural parameters from the history of interactions. This dissertation establishes the thesis statement, "Modelling the strength of relationships between agents and predicting the behaviour of trading partner agents in a multi agent argumentation system enables agents to build trusted trading partnerships". A prototype simulation environment has been developed to conduct the experiments and to validate the thesis statement. The simulated arrival rate obtained by the proposed model is lower than that of an existing model, e.g., the Trust and Honour model. The prototype argumentation system demonstrated a proof of concept. The prototype will be further developed before applying the proposed argumentation system in commercial applications

Higher Fertilizer Inputs Increase Fitness Traits of Brown Planthopper in Rice.

Rice (Oryza sativa L.) is the primary staple food source for more than half of the world's population. In many developing countries, increased use of fertilizers is a response to increase demand for rice. In this study, we investigated the effects of three principal fertilizer components (nitrogen, phosphorus and potassium) on the development of potted rice plants and their effects on fitness traits of the brown planthopper (BPH) [Nilaparvata lugens (Stål) (Homoptera: Delphacidae)], which is a major pest of rice in Bangladesh and elsewhere. Compared to low fertilizer inputs, high fertilizer treatments induced plant growth but also favored BPH development. The BPH had higher survival, developed faster, and the intrinsic rate of natural increase (r m ) was higher on well-fertilized than under-fertilized plants. Among the fertilizer inputs, nitrogen had the strongest effect on the fitness traits of BPH. Furthermore, both the "Plant vigor hypothesis" and the "Plant stress hypothesis" were supported by the results, the former hypothesis more so than the latter. These hypotheses suggest that the most suitable/attractive hosts for insect herbivores are the most vigorous plants. Our findings emphasized that an exclusive focus on yield increases through only enhanced crop fertilization may have unforeseen, indirect, effects on crop susceptibility to pests, such as BPH

Spatial modelling of bacterial diversity over the selected regions in Bangladesh by next-generation sequencing: role of water temperature

In this study, a spatial model has been developed to investigate the role of water temperature to the distribution of bacteria over the selected regions in the Bay of Bengal, located in the southern region of Bangladesh using next-generation sequencing. Bacterial concentration, quantitative polymerase chain reactions, and sequencing were performed on water samples and identified Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. The spatial model tessellated the parts of the Bay of Bengal with hexagons and analyzed the relationship between the distribution of bacteria and water temperature. A geographically weighted regression was used to observe whether water temperature contributed strongly or weakly to the distribution of bacteria. The residuals were examined to assess the model’s fitness. The spatial model has the potential to predict the bacterial diversity in the selected regions of Bangladesh

Data-Driven Threat Analysis for Ensuring Security in Cloud Enabled Systems

Cloud computing offers many benefits including business flexibility, scalability and cost savings but despite these benefits, there exist threats that require adequate attention for secure service delivery. Threats in a cloud-based system need to be considered from a holistic perspective that accounts for data, application, infrastructure and service, which can pose potential risks. Data certainly plays a critical role within the whole ecosystem and organisations should take account of and protect data from any potential threats. Due to the variation of data types, status, and location, understanding the potential security concerns in cloud-based infrastructures is more complex than in a traditional system. The existing threat modeling approaches lack the ability to analyse and prioritise data-related threats. The main contribution of the paper is a novel data-driven threat analysis (d-TM) approach for the cloud-based systems. The main motivation of d-TM is the integration of data from three levels of abstractions, i.e., management, control, and business and three phases, i.e., storage, process and transmittance, within each level. The d-TM provides a systematic flow of attack surface analysis from the user agent to the cloud service provider based on the threat layers in cloud computing. Finally, a cloud-based use case scenario was used to demonstrate the applicability of the proposed approach. The result shows that d-TM revealed four critical threats out of the seven threats based on the identified assets. The threats targeted management and business data in general, while targeting data in process and transit more specifically

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Glutamate Induces Mitochondrial Dynamic Imbalance and Autophagy Activation: Preventive Effects of Selenium

Glutamate-induced cytotoxicity is partially mediated by enhanced oxidative stress. The objectives of the present study are to determine the effects of glutamate on mitochondrial membrane potential, oxygen consumption, mitochondrial dynamics and autophagy regulating factors and to explore the protective effects of selenium against glutamate cytotoxicity in murine neuronal HT22 cells. Our results demonstrated that glutamate resulted in cell death in a dose-dependent manner and supplementation of 100 nM sodium selenite prevented the detrimental effects of glutamate on cell survival. The glutamate induced cytotoxicity was associated with mitochondrial hyperpolarization, increased ROS production and enhanced oxygen consumption. Selenium reversed these alterations. Furthermore, glutamate increased the levels of mitochondrial fission protein markers pDrp1 and Fis1 and caused increase in mitochondrial fragmentation. Selenium corrected the glutamate-caused mitochondrial dynamic imbalance and reduced the number of cells with fragmented mitochondria. Finally, glutamate activated autophagy markers Beclin 1 and LC3-II, while selenium prevented the activation. These results suggest that glutamate targets the mitochondria and selenium supplementation within physiological concentration is capable of preventing the detrimental effects of glutamate on the mitochondria. Therefore, adequate selenium supplementation may be an efficient strategy to prevent the detrimental glutamate toxicity and further studies are warranted to define the therapeutic potentials of selenium in animal disease models and in human

Plasma irisin is elevated in type 2 diabetes and is associated with increased E-selectin levels

BACKGROUND: Irisin is a hormone released mainly from skeletal muscle after exercise which increases adipose tissue energy expenditure. Adipocytes can also release irisin after exercise, acting as a local adipokine to induce white adipose tissue to take on a brown adipose tissue-like phenotype, suggesting that irisin and its receptor may represent a novel molecular target for the treatment of obesity and obesity-related diabetes. Previous reports provide conflicting evidence regarding circulating irisin levels in patients with type 2 diabetes (T2DM). METHODS: This study investigated plasma irisin concentrations in 79 T2DM individuals, assessing potential associations with measures of segmental body composition, markers of endothelial dysfunction and peripheral blood mononuclear cell telomere length (TL). RESULTS: Resting, overnight-fasted plasma irisin levels were significantly higher in this group of T2DM patients compared with levels we previously reported in healthy volunteers (p < 0.001). Moreover, plasma irisin displayed a positive correlation with body mass index (p = 0.04), body fat percentage (p = 0.03), HbA1c (p = 0.03) and soluble E-selectin (p < 0.001). A significant negative association was observed between plasma irisin and visceral adiposity (p = 0.006) in T2DM patients. Multiple regression analysis revealed that circulating soluble E-selectin levels could be predicted by plasma irisin (p = 0.004). Additionally, cultured human umbilical vein endothelial cells (HUVEC) exposed to 200 ng/ml irisin for 4 h showed a significant fourfold increase in E-selectin and 2.5-fold increase in ICAM-1 gene expression (p = 0.001 and p = 0.015 respectively), and there was a 1.8-fold increase in soluble E-selectin in conditioned media (p < 0.05). CONCLUSION: These data suggest that elevated plasma irisin in T2DM is associated with indices of adiposity, and that irisin may be involved in pro-atherogenic endothelial disturbances that accompany obesity and T2DM. Accordingly, irisin may constitute a potentially novel therapeutic opportunity in the field of obesity and cardiovascular diabetology