Natural resources conservation management and strategies in agriculture

This paper suggests a holistic framework for assessment and improvement of management strategies for conservation of natural resources in agriculture. First, it incorporates an interdisciplinary approach (combining Economics, Organization, Law, Sociology, Ecology, Technology, Behavioral and Political Sciences) and presents a modern framework for assessing environmental management and strategies in agriculture including: specification of specific “managerial needs” and spectrum of feasible governance modes (institutional environment; private, collective, market, and public modes) of natural resources conservation at different level of decision-making (individual, farm, eco-system, local, regional, national, transnational, and global); specification of critical socio-economic, natural, technological, behavioral etc. factors of managerial choice, and feasible spectrum of (private, collective, public, international) managerial strategies; assessment of efficiency of diverse management strategies in terms of their potential to protect diverse eco-rights and investments, assure socially desirable level of environmental protection and improvement, minimize overall (implementing, third-party, transaction etc.) costs, coordinate and stimulate eco-activities, meet preferences and reconcile conflicts of individuals etc. Second, it presents evolution and assesses the efficiency of diverse management forms and strategies for conservation of natural resources in Bulgarian agriculture during post-communist transformation and EU integration (institutional, market, private, and public), and evaluates the impacts of EU CAP on environmental sustainability of farms of different juridical type, size, specialization and location. Finally, it suggests recommendations for improvement of public policies, strategies and modes of intervention, and private and collective strategies and actions for effective environmental protection

ANCHOR: logically-centralized security for Software-Defined Networks

While the centralization of SDN brought advantages such as a faster pace of innovation, it also disrupted some of the natural defenses of traditional architectures against different threats. The literature on SDN has mostly been concerned with the functional side, despite some specific works concerning non-functional properties like 'security' or 'dependability'. Though addressing the latter in an ad-hoc, piecemeal way, may work, it will most likely lead to efficiency and effectiveness problems. We claim that the enforcement of non-functional properties as a pillar of SDN robustness calls for a systemic approach. As a general concept, we propose ANCHOR, a subsystem architecture that promotes the logical centralization of non-functional properties. To show the effectiveness of the concept, we focus on 'security' in this paper: we identify the current security gaps in SDNs and we populate the architecture middleware with the appropriate security mechanisms, in a global and consistent manner. Essential security mechanisms provided by anchor include reliable entropy and resilient pseudo-random generators, and protocols for secure registration and association of SDN devices. We claim and justify in the paper that centralizing such mechanisms is key for their effectiveness, by allowing us to: define and enforce global policies for those properties; reduce the complexity of controllers and forwarding devices; ensure higher levels of robustness for critical services; foster interoperability of the non-functional property enforcement mechanisms; and promote the security and resilience of the architecture itself. We discuss design and implementation aspects, and we prove and evaluate our algorithms and mechanisms, including the formalisation of the main protocols and the verification of their core security properties using the Tamarin prover.Comment: 42 pages, 4 figures, 3 tables, 5 algorithms, 139 reference

Mining data quality rules based on T-dependence

Since their introduction in 1976, edit rules have been a standard tool in statistical analysis. Basically, edit rules are a compact representation of non-permitted combinations of values in a dataset. In this paper, we propose a technique to automatically find edit rules by use of the concept of T-dependence. We first generalize the traditional notion of lift, to that of T-lift, where stochastic independence is generalized to T-dependence. A combination of values is declared as an edit rule under a t-norm T if there is a strong negative correlation under T-dependence. We show several interesting properties of this approach. In particular, we show that under the minimum t-norm, edit rules can be computed efficiently by use of frequent pattern trees. Experimental results show that there is a weak to medium correlation in the rank order of edit rules obtained under T_M and T_P, indicating that the semantics of these kinds of dependencies are different

Super Logic Programs

The Autoepistemic Logic of Knowledge and Belief (AELB) is a powerful nonmonotic formalism introduced by Teodor Przymusinski in 1994. In this paper, we specialize it to a class of theories called `super logic programs'. We argue that these programs form a natural generalization of standard logic programs. In particular, they allow disjunctions and default negation of arbibrary positive objective formulas. Our main results are two new and powerful characterizations of the static semant ics of these programs, one syntactic, and one model-theoretic. The syntactic fixed point characterization is much simpler than the fixed point construction of the static semantics for arbitrary AELB theories. The model-theoretic characterization via Kripke models allows one to construct finite representations of the inherently infinite static expansions. Both characterizations can be used as the basis of algorithms for query answering under the static semantics. We describe a query-answering interpreter for super programs which we developed based on the model-theoretic characterization and which is available on the web.Comment: 47 pages, revised version of the paper submitted 10/200

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of FILT in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find FILT to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of FILT to be consistent with that of the highly efficient E-learning Chronotron, but with the distinct advantage that FILT is also implementable as an online method for increased biological realism.Comment: 26 pages, 10 figures, this version is published in PLoS ONE and incorporates reviewer comment