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Individuals who have autism, tend to have trouble fully communicating what it is that they truly need. Furthermore, there are many different varieties of communication technology out there today that are extremely beneficial; however, for some families, not affordable at all. Thus, we have researched the different ways to help a student with autism communicate but at an affordable rate. In the end we have decided on a low-tech picture exchange communication system as our form of intervention in helping an individual clearly express their needs and to better communicate in general. Our hypothesis for the study using the picture exchange communication system is that individuals who have autism that are non-verbal will be able to increase their excessive language skills with using this intervention. Upon research, we have found that the picture exchange communication system has helped increase the expressive language skills for individuals who have autism spectrum disorder. Our recommendation would be to use a low-tech form of this intervention, therefore, many people have the capability to work with this intervention, as it would be the cheapest option

Distributed Relation Logic

We extend the relational algebra of Chin and Tarski so that it is multisorted or, as we prefer, typed. Each type supports a local Boolean algebra outfitted with a converse operator. From Lyndon, we know that relation algebras cannot be represented as proper relation algebras where a proper relation algebra has binary relations as elements and the algebra is singly-typed. Here, the intensional conjunction, which was to represent relational composition in Chin and Tarski, spans three different local algebras, thus the term distributed in the title. Since we do not rely on proper relation algebras, we are free to re-express the algebras as typed. In doing so, we allow many different intensional conjunction operators.We construct a typed logic over these algebras, also known as heterogeneous algebras of Birkhoff and Lipson. The logic can be seen as a form of relevance logic with a classical negation connective where the Routley-Meyer star operator is reified as a converse connective in the logic. Relevance logic itself is not typed but our work shows how it can be made so. Some of the properties of classical relevance logic are weakened from Routley-Meyer’s version which is too strong for a logic over relation algebras

IT Supported Open Innovation in a Chinese Context

The authors examine digital innovation and how it is supported by portfolios of digital services within a digital innovation platform

The role of digital infrastructures in performances of organizational agility

Organizational agility has received much attention from practitioners and researchers in Information Systems. Existing research on agility, however, often conceptualizes information systems in a traditional way, while not reflecting sufficiently on how, as a consequence of digitalization, they are turning into open systems defined by characteristics like modularity and generativity. The concept of digital infrastructures captures this shift and stresses the evolving, socio-technical nature of such systems. This thesis sees IT in large companies as digital infrastructures and organizational agility as a performance within them. In order to explain how such infrastructures can support performances of agility, a focus on the interactions between IT, information and the people using and designing them is proposed. A case study was conducted within Telco, a large telecommunications firm in the United Kingdom. It presents three projects employees regarded as agile. A critical realist ontology is applied in order to identify generative mechanisms for agility. The thesis develops a theory of agility as a performance within digital infrastructures. This contains the central generative mechanism of agilization – making an organization more agile by cultivating digital infrastructures and minding flows of information to attain an appropriate level of agility. This is supported by the related mechanisms of informatization and infrastructuralization. Moreover, the concept of bounded agility illustrates how people in large organizations do not strive for agility unreservedly, instead aiming for agility in well-defined areas that does not put the business at risk. This theory of agility and the concept of bounded agility constitute the main theoretical contributions of this thesis. It also contributes clear definitions of the terms ‘information’ and ‘data’ and aligns them to the ontology of critical realism. Finally, the proposed mechanisms contribute to an emerging middle range theory of organizational agility that will be useful for practitioners

Comparison of the CPU and memory performance of StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA)

High Energy Physics data sets are often characterized by a huge number of events. Therefore, it is extremely important to use statistical packages able to efficiently analyze these unprecedented amounts of data. We compare the performance of the statistical packages StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA). We focus on how CPU time and memory usage of the learning process scale versus data set size. As classifiers, we consider Random Forests, Boosted Decision Trees and Neural Networks. For our tests, we employ a data set widely used in the machine learning community, "Threenorm" data set, as well as data tailored for testing various edge cases. For each data set, we constantly increase its size and check CPU time and memory needed to build the classifiers implemented in SPR and TMVA. We show that SPR is often significantly faster and consumes significantly less memory. For example, the SPR implementation of Random Forest is by an order of magnitude faster and consumes an order of magnitude less memory than TMVA on Threenorm data

Error-Correcting Tournaments

We present a family of pairwise tournaments reducing $k$-class classification to binary classification. These reductions are provably robust against a constant fraction of binary errors. The results improve on the PECOC construction \cite{SECOC} with an exponential improvement in computation, from $O(k)$ to $O(\log_2 k)$, and the removal of a square root in the regret dependence, matching the best possible computation and regret up to a constant.Comment: Minor wording improvement

Algebraic information theory for binary channels

AbstractWe study the algebraic structure of the monoid of binary channels and show that it is dually isomorphic to the interval domain over the unit interval with the operation from Martin (2006) [4]. We show that the capacity of a binary channel is Scott continuous as a map on the interval domain and that its restriction to any maximally commutative submonoid of binary channels is an order isomorphism onto the unit interval. These results allows us to solve an important open problem in the analysis of covert channels: a provably correct method for injecting noise into a covert channel which will reduce its capacity to any level desired in such a way that the practitioner is free to insert the noise at any point in the system

Efficient Feature Selection and Multiclass Classification with Integrated Instance and Model Based Learning

Multiclass classification and feature (variable) selections are commonly encountered in many biological and medical applications. However, extending binary classification approaches to multiclass problems is not trivial. Instance-based methods such as the K nearest neighbor (KNN) can naturally extend to multiclass problems and usually perform well with unbalanced data, but suffer from the curse of dimensionality. Their performance is degraded when applied to high dimensional data. On the other hand, model-based methods such as logistic regression require the decomposition of the multiclass problem into several binary problems with one-vs.-one or one-vs.-rest schemes. Even though they can be applied to high dimensional data with L1 or Lp penalized methods, such approaches can only select independent features and the features selected with different binary problems are usually different. They also produce unbalanced classification problems with one vs. the rest scheme even if the original multiclass problem is balanced

Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin