Probabilistic coherence spaces as a model of higher-order probabilistic computation

We study a probabilistic version of coherence spaces and show that these objects provide a model of linear logic. We build a model of the pure lambda-calculus in this setting and show how to interpret a probabilistic version of the functional language PCF. We give a probabilistic interpretation of the semantics of probabilistic PCF closed terms of ground type. Last we suggest a generalization of this approach, using Banach spaces.     

EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network

This paper presents a Web 2.0 Learning Environment, for a systematic creation of adaptive and intelligent tutoring systems. Authoring contents is made by a community of users including teachers and students. The tutoring systems adapt the contents according to the best learning style using self-organizing maps (SOMs). The SOM was trained for classifying Felder–Silverman learning styles. The most important advantage of these unsupervised neural networks is that they do not require an external teacher for presenting a training set. The approach was implemented under an authoring tool that allows the production of personalized learning material to be used under collaborative and mobile learning environments. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the authoring tool.     

Reconfigurable distributed storage for dynamic networks

This paper presents a new algorithm for implementing a reconfigurable distributed shared memory in an asynchronous dynamic network. The algorithm guarantees atomic consistency (linearizability) in all executions in the presence of arbitrary crash failures of the processing nodes, message delays, and message loss. The algorithm incorporates a classic quorum-based algorithm for read/write operations, and an optimized consensus protocol, based on Fast Paxos for reconfiguration, and achieves the design goals of: (i) allowing read and write operations to complete rapidly and (ii) providing long-term fault-tolerance through reconfiguration, a process that evolves the quorum configurations used by the read and write operations. The resulting algorithm tolerates dynamism. We formally prove our algorithm to be correct, we present its performance and compare it to existing reconfigurable memories, and we evaluate experimentally the cost of its reconfiguration mechanism.     

Comprehensive Automation for Specialty Crops: Year 1 results and lessons learned

Comprehensive Automation for Specialty Crops is a project focused on the needs of the specialty crops sector, with a focus on apples and nursery trees. The project’s main thrusts are the integration of robotics technology and plant science; understanding and overcoming socio-economic barriers to technology adoption; and making the results available to growers and stakeholders through a nationwide outreach program. In this article, we present the results obtained and lessons learned in the first year of the project with a reconfigurable mobility infrastructure for autonomous farm driving. We then present sensor systems developed to enable three real-world agricultural applications—insect monitoring, crop load scouting, and caliper measurement—and discuss how they can be deployed autonomously to yield increased production efficiency and reduced labor costs.     

Computational aspects of the expected differential probability of 4-round AES and AES-like ciphers

In this paper we study the security of the Advanced Encryption Standard (AES) and AES-like block ciphers against differential cryptanalysis. Differential cryptanalysis is one of the most powerful methods for analyzing the security of block ciphers. Even though no formal proofs for the security of AES against differential cryptanalysis have been provided to date, some attempts to compute the maximum expected differential probability (MEDP) for two and four rounds of AES have been presented recently. In this paper, we will improve upon existing approaches in order to derive better bounds on the EDP for two and four rounds of AES based on a slightly simplified S-box. More precisely, we are able to provide the complete distribution of the EDP for two rounds of this AES variant with five active S-boxes and methods to improve the estimates for the EDP in the case of six active S-boxes.     

Information in the nonstationary case

Information estimates such as the direct method of Strong, Koberle, de Ruyter van Steveninck, and Bialek (1998) sidestep the difficult problem of estimating the joint distribution of response and stimulus by instead estimating the difference between the marginal and conditional entropies of the response. While this is an effective estimation strategy, it tempts the practitioner to ignore the role of the stimulus and the meaning of mutual information. We show here that as the number of trials increases indefinitely, the direct (or plug-in) estimate of marginal entropy converges (with probability 1) to the entropy of the time-averaged conditional distribution of the response, and the direct estimate of the conditional entropy converges to the time-averaged entropy of the conditional distribution of the response. Under joint stationarity and ergodicity of the response and stimulus, the difference of these quantities converges to the mutual information. When the stimulus is deterministic or nonstationary the direct estimate of information no longer estimates mutual information, which is no longer meaningful, but it remains a measure of variability of the response distribution across time.     

Symbol Grounding in Computational Systems: A Paradox of Intentions

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism.     

Would you mind being watched by machines? Privacy concerns in data mining

Data mining is not an invasion of privacy because access to data is only by machines, not by people: this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people.

---