Mining trading partners’ preferences for efficient multi-issue bargaining in E‑business

Classical negotiation models are weak in supporting real-world business negotiations because these models often assume that the preference information of each negotiator is made public. Although parametric learning methods have been proposed for acquiring the preference information of negotiation opponents, these methods suffer from the strong assumptions about the specific utility function and negotiation mechanism employed by the opponents. Consequently, it is difficult to apply these learning methods to the heterogeneous negotiation agents participating in e‑marketplaces. This paper illustrates the design, development, and evaluation of a nonparametric negotiation knowledge discovery method which is underpinned by the well-known Bayesian learning paradigm. According to our empirical testing, the novel knowledge discovery method can speed up the negotiation processes while maintaining negotiation effectiveness. To the best of our knowledge, this is the first nonparametric negotiation knowledge discovery method developed and evaluated in the context of multi-issue bargaining over e‑marketplaces.

CloudNeg: An autonomous multi issue negotiation system, with preference elicitation component, for trading cloud services

Cloud services provide its users with flexible resource provisioning. But in the current market, a user has to choose from a limited set of configurations at a fixed price. This paper presents an autonomous negotiation system termed CloudNeg for negotiating cloud services. CloudNeg provides buyers and sellers of cloud services with autonomous agents to negotiate on the specifications of a cloud instance, including price, on their behalf. These agents elicit their buyers’ time preferences and use them in negotiations. Further, this paper presents two artifacts: a negotiation algorithm and a prototype which together form CloudNeg.

Book Review: Regulating Code : Good Governance and Better Regulation in the Information Age

Digital technologies have profoundly changed not only the ways we create, distribute, access, use and re-use information but also many of the governance structures we had in place. Overall, "older" institutions at all governance levels have grappled and often failed to master the multi-faceted and multi-directional issues of the Internet. Regulatory entrepreneurs have yet to discover and fully mobilize the potential of digital technologies as an influential factor impacting upon the regulability of the environment and as a potential regulatory tool in themselves. At the same time, we have seen a deterioration of some public spaces and lower prioritization of public objectives, when strong private commercial interests are at play, such as most tellingly in the field of copyright. Less tangibly, private ordering has taken hold and captured through contracts spaces, previously regulated by public law. Code embedded in technology often replaces law. Non-state action has in general proliferated and put serious pressure upon conventional state-centered, command-and-control models. Under the conditions of this "messy" governance, the provision of key public goods, such as freedom of information, has been made difficult or is indeed jeopardized.The grand question is how can we navigate this complex multi-actor, multi-issue space and secure the attainment of fundamental public interest objectives. This is also the question that Ian Brown and Chris Marsden seek to answer with their book, Regulating Code, as recently published under the "Information Revolution and Global Politics" series of MIT Press. This book review critically assesses the bold effort by Brown and Marsden.

Multi-Party Negotiation When Agents Have Subjective Estimates of Bargaining Powers

This paper presents a method for generating Pareto-optimal solutions in multi-party negotiations. In this iterative method, decision makers (DMs) formulate proposals that yield a minimum payoff to their opponents. Each proposal belongs to the efficient frontier, DMs try to adjust to a common one. In this setting, each DM is supposed to have a given bargaining power. More precisely each DM is supposed to have a subjective estimate of the power of the different parties. We study the convergence of the method, and provide examples where there is no possible agreement resulting from it.

Analyse d’information tridimensionnelle issue de systèmes multi-caméras pour la détection de la chute et l’analyse de la marche

Réalisé en cotutelle avec le laboratoire M2S de Rennes 2

An Italian job: the need for collective wage bargaining reform. Bruegel Policy Brief ISSUE 2016/11 - JULY 2016

Highlights: Since the mid-1990s, Italy has been characterised by a lack of labour productivity growth, combinedwith a 60 percent growth in labour costs, 20 percentage points above euro-area average consumer price growth. As a consequence, Italy has become less competitive compared to its euro-area partners, the profitability of its firms has dropped and real GDP-per-capita has flatlined. • At the root of the substantial discrepancy between wages and productivity is Italy’s system of centralised wage bargaining which, in many ways, is designed without regard for the underlying industrial structure and geographical heterogeneity of the Italian economy.This has fostered perverse incentives and imbalances within Italy. • Collective wage bargaining, and in particular the determination of base salaries, should be moved from the national to the regional level for all contracts, in the public and private sectors.The Mezzogiorno,which might superficially be seen as losing out from this policy, would actually gain the most in competitiveness terms. • Furthermore, measures should be taken so that, in the long run, the Italian industrial structure evolves into a less fragmented small-company-based economy. This firm consolidation would likely expand the use of firm-level agreements and performance payments, and would improve Italy’s productivity and competitiveness overall.

Genetically Altered Foods: A Policy Issue for Multi-unit Food Service Operators

Although it is a substantial issue, the technology behind genetically altered foods and the concerns being raised about them are not well understood by most people. The authors discuss how genetically altered foods might fit into the business strategies of multi-unit food service operators as well as current policies and predispositions of multi-unit food service companies toward the use of genetically altered foods. They also outline the issues surrounding genetically altered food as they relate to the food service industry and provide a picture of where multi-unit food service operators currently stand on the technology

Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia

This paper reports the application of multicriteria decision making techniques, PROMETHEE and GAIA, and receptor models, PCA/APCS and PMF, to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. While PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulphate, sea salt and road dust/soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.

Multi-factor password-authenticated key exchange

We consider a new form of authenticated key exchange which we call multi-factor password-authenticated key exchange, where session establishment depends on successful authentication of multiple short secrets that are complementary in nature, such as a long-term password and a one-time response, allowing the client and server to be mutually assured of each other's identity without directly disclosing private information to the other party. Multi-factor authentication can provide an enhanced level of assurance in higher-security scenarios such as online banking, virtual private network access, and physical access because a multi-factor protocol is designed to remain secure even if all but one of the factors has been compromised. We introduce a security model for multi-factor password-authenticated key exchange protocols, propose an efficient and secure protocol called MFPAK, and provide a security argument to show that our protocol is secure in this model. Our security model is an extension of the Bellare-Pointcheval-Rogaway security model for password-authenticated key exchange and accommodates an arbitrary number of symmetric and asymmetric authentication factors.

Multi-criteria ranking and receptor modelling of airborne fine particles at three sites in the Pearl River Delta region of China

The multi-criteria decision making methods, Preference METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site > urban site > roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8 ± 8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

Vibrational spectroscopy of the multi anion mineral zykaite Fe4(AsO4)(SO4)(OH)•15H2O – implications for arsenate removal

Some minerals are colloidal and are poorly diffracting . Vibrational spectroscopy offers one of the few methods for the assessment of the structure of these types of minerals. Among this group of minerals is zykaite with formula Fe4(AsO4)(SO4)(OH)•15H2O. The objective of this research is to determine the molecular structure of the mineral zykaite using vibrational spectroscopy. Raman and infrared bands are attributed to the AsO43-, SO42- and water stretching vibrations. The sharp band at 3515 cm-1 is assigned to the stretching vibration of the OH units. This mineral offers a mechanism for the formation of more crystalline minerals such as scorodite and bukovskyite. Arsenate ions can be removed from aqueous systems through the addition of ferric compounds such as ferric chloride. This results in the formation of minerals such as zykaite and pitticite (Fe3+,AsO4,SO4,H2O).

National Environmental Law Review Issue 2011: 3, Editorial: Federal Developments

Clean Energy Agreement of the MPCCC On 10 July 2011, details of the Multi-Party Climate Change Committee’s Clean Energy Agreement for implementing a carbon price were released. This included an agreed package of measures that the Committee considered would enable Australia to meet its emissions reduction targets in an environmentally and economically efficient way. A copy of the agreement can be found on the website of the Department of Climate Change and Energy Efficiency...

The reliability, accuracy and minimal detectable difference of a multi-segment kinematic model of the foot-shoe complex

Kinematic models are commonly used to quantify foot and ankle kinematics, yet no marker sets or models have been proven reliable or accurate when wearing shoes. Further, the minimal detectable difference of a developed model is often not reported. We present a kinematic model that is reliable, accurate and sensitive to describe the kinematics of the foot–shoe complex and lower leg during walking gait. In order to achieve this, a new marker set was established, consisting of 25 markers applied on the shoe and skin surface, which informed a four segment kinematic model of the foot–shoe complex and lower leg. Three independent experiments were conducted to determine the reliability, accuracy and minimal detectable difference of the marker set and model. Inter-rater reliability of marker placement on the shoe was proven to be good to excellent (ICC = 0.75–0.98) indicating that markers could be applied reliably between raters. Intra-rater reliability was better for the experienced rater (ICC = 0.68–0.99) than the inexperienced rater (ICC = 0.38–0.97). The accuracy of marker placement along each axis was <6.7 mm for all markers studied. Minimal detectable difference (MDD90) thresholds were defined for each joint; tibiocalcaneal joint – MDD90 = 2.17–9.36°, tarsometatarsal joint – MDD90 = 1.03–9.29° and the metatarsophalangeal joint – MDD90 = 1.75–9.12°. These thresholds proposed are specific for the description of shod motion, and can be used in future research designed at comparing between different footwear.