An innovative approach to teaching structural induction for computer science

Proofs by induction are central to many computer science areas such as data structures, theory of computation, programming languages, program efficiency-time complexity, and program correctness. Proofs by induction can also improve students’ understanding and performance of computer science concepts such as programming languages, algorithm design, and recursion, as well as serve as a medium for teaching them. Even though students are exposed to proofs by induction in many courses of their curricula, they still have difficulties understanding and performing them. This impacts the whole course of their studies, since proofs by induction are omnipresent in computer science. Specifically, students do not gain conceptual understanding of induction early in the curriculum and as a result, they have difficulties applying it to more advanced areas later on in their studies. The goal of my dissertation is twofold: (1) identifying sources of computer science students’ difficulties with proofs by induction, and (2) developing a new approach to teaching proofs by induction by way of an interactive and multimodal electronic book (e-book). For the first goal, I undertook a study to identify possible sources of computer science students’ difficulties with proofs by induction. Its results suggest that there is a close correlation between students’ understanding of inductive definitions and their understanding and performance of proofs by induction. For designing and developing my e-book, I took into consideration the results of my study, as well as the drawbacks of the current methodologies of teaching proofs by induction for computer science. I designed my e-book to be used as a standalone and complete educational environment. I also conducted a study on the effectiveness of my e-book in the classroom. The results of my study suggest that, unlike the current methodologies of teaching proofs by induction for computer science, my e-book helped students overcome many of their difficulties and gain conceptual understanding of proofs induction.

An Innovative Approach to Teaching Structural Induction for Computer Science

Proofs by induction are central to many computer science areas such as data structures, theory of computation, programming languages, program efficiency-time complexity, and program correctness. Proofs by induction can also improve students’ understanding of and performance with computer science concepts such as programming languages, algorithm design, and recursion, as well as serve as a medium for teaching them. Even though students are exposed to proofs by induction in many courses of their curricula, they still have difficulties understanding and performing them. This impacts the whole course of their studies, since proofs by induction are omnipresent in computer science. Specifically, students do not gain conceptual understanding of induction early in the curriculum and as a result, they have difficulties applying it to more advanced areas later on in their studies. The goal of my dissertation is twofold: 1. identifying sources of computer science students’ difficulties with proofs by induction, and 2. developing a new approach to teaching proofs by induction by way of an interactive and multimodal electronic book (e-book). For the first goal, I undertook a study to identify possible sources of computer science students’ difficulties with proofs by induction. Its results suggest that there is a close correlation between students’ understanding of inductive definitions and their understanding and performance of proofs by induction. For designing and developing my e-book, I took into consideration the results of my study, as well as the drawbacks of the current methodologies of teaching proofs by induction for computer science. I designed my e-book to be used as a standalone and complete educational environment. I also conducted a study on the effectiveness of my e-book in the classroom. The results of my study suggest that, unlike the current methodologies of teaching proofs by induction for computer science, my e-book helped students overcome many of their difficulties and gain conceptual understanding of proofs induction.

Database evolution on an orthogonal persistent programming system: A semi-transparent approach

In this paper the problem of the evolution of an object-oriented database in the context of orthogonal persistent programming systems is addressed. We have observed two characteristics in that type of systems that offer particular conditions to implement the evolution in a semi-transparent fashion. That transparency can further be enhanced with the obliviousness provided by the Aspect-Oriented Programming techniques. Was conceived a meta-model and developed a prototype to test the feasibility of our approach. The system allows programs, written to a schema, access semi-transparently to data in other versions of the schema.

Self-regulated learning in computer programming : strategies students adopted during an assignment

The SimProgramming teaching approach has the goal to help students overcome their learning difficulties in the transition from entry-level to advanced computer programming and prepare them for real-world labour environments, adopting learning strategies. It immerses learners in a businesslike learning environment, where students develop a problem-based learning activity with a specific set of tasks, one of which is filling weekly individual forms. We conducted thematic analysis of 401 weekly forms, to identify the students’ strategies for self-regulation of learning during assignment. The students are adopting different strategies in each phase of the approach. The early phases are devoted to organization and planning, later phases focus on applying theoretical knowledge and hands-on programming. Based on the results, we recommend the development of educational practices to help students conduct self-reflection of their performance during tasks.

Integer Linear Programming for Sequence Problems: A general approach to reduce the problem size

Sequence problems belong to the most challenging interdisciplinary topics of the actuality. They are ubiquitous in science and daily life and occur, for example, in form of DNA sequences encoding all information of an organism, as a text (natural or formal) or in form of a computer program. Therefore, sequence problems occur in many variations in computational biology (drug development), coding theory, data compression, quantitative and computational linguistics (e.g. machine translation). In recent years appeared some proposals to formulate sequence problems like the closest string problem (CSP) and the farthest string problem (FSP) as an Integer Linear Programming Problem (ILPP). In the present talk we present a general novel approach to reduce the size of the ILPP by grouping isomorphous columns of the string matrix together. The approach is of practical use, since the solution of sequence problems is very time consuming, in particular when the sequences are long.

Evolution of classifiers for pitch estimation of piano music using cartesian genetic programming

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.

A systemic approach for optimal cooling tower operation

The thermal performance of a cooling tower and its cooling water system is critical for industrial plants, and small deviations from the design conditions may cause severe instability in the operation and economics of the process. External disturbances such as variation in the thermal demand of the process or oscillations in atmospheric conditions may be suppressed in multiple ways. Nevertheless, such alternatives are hardly ever implemented in the industrial operation due to the poor coordination between the utility and process sectors. The complexity of the operation increases because of the strong interaction among the process variables. In the present work, an integrated model for the minimization of the operating costs of a cooling water system is developed. The system is composed of a cooling tower as well as a network of heat exchangers. After the model is verified, several cases are studied with the objective of determining the optimal operation. It is observed that the most important operational resources to mitigate disturbances in the thermal demand of the process are, in this order: the increase in recycle water flow rate, the increase in air flow rate and finally the forced removal of a portion of the water flow rate that enters the cooling tower with the corresponding make-up flow rate. (C) 2009 Elsevier Ltd. All rights reserved.

Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs only at the beginning of periodic intervals. A customized approximate dynamic programming method is introduced for this problem. The authors also present numerical experiments that assess the reliability of the new approach and show that it performs better than a myopic policy.

THE VANISHING DISCOUNT APPROACH FOR THE AVERAGE CONTINUOUS CONTROL OF PIECEWISE DETERMINISTIC MARKOV PROCESSES

This work is concerned with the existence of an optimal control strategy for the long-run average continuous control problem of piecewise-deterministic Markov processes (PDMPs). In Costa and Dufour (2008), sufficient conditions were derived to ensure the existence of an optimal control by using the vanishing discount approach. These conditions were mainly expressed in terms of the relative difference of the alpha-discount value functions. The main goal of this paper is to derive tractable conditions directly related to the primitive data of the PDMP to ensure the existence of an optimal control. The present work can be seen as a continuation of the results derived in Costa and Dufour (2008). Our main assumptions are written in terms of some integro-differential inequalities related to the so-called expected growth condition, and geometric convergence of the post-jump location kernel associated to the PDMP. An example based on the capacity expansion problem is presented, illustrating the possible applications of the results developed in the paper.

Nurturing communication by health professionals toward patients: A communication accommodation theory approach

This study explores the role of nurturing communication in distinguishing interpersonal and intergroup interactions between health professionals and patients, from the perspective of communication accommodation theory (CAT). Participants (47 men and 87 women) rated videotapes of actual hospital consultations on 12 goal and 16 strategy items derived from CAT. Health professionals in interpersonal interactions were perceived to pay more attention to relationship and emotional needs and to use more nurturant discourse management and emotional expression. These results point the way toward elucidating the perceived optimal balance in accommodative behavior, both group based and interpersonal, in these contexts, and they highlight the importance of nurturant communication to this process.

Optimal release strategies for biological control agents: an application of stochastic dynamic programming to population management

1. Establishing biological control agents in the field is a major step in any classical biocontrol programme, yet there are few general guidelines to help the practitioner decide what factors might enhance the establishment of such agents. 2. A stochastic dynamic programming (SDP) approach, linked to a metapopulation model, was used to find optimal release strategies (number and size of releases), given constraints on time and the number of biocontrol agents available. By modelling within a decision-making framework we derived rules of thumb that will enable biocontrol workers to choose between management options, depending on the current state of the system. 3. When there are few well-established sites, making a few large releases is the optimal strategy. For other states of the system, the optimal strategy ranges from a few large releases, through a mixed strategy (a variety of release sizes), to many small releases, as the probability of establishment of smaller inocula increases. 4. Given that the probability of establishment is rarely a known entity, we also strongly recommend a mixed strategy in the early stages of a release programme, to accelerate learning and improve the chances of finding the optimal approach.

Three-dimensional electrical impedance, tomography: A topology optimization approach

Electrical impedance tomography is a technique to estimate the impedance distribution within a domain, based on measurements on its boundary. In other words, given the mathematical model of the domain, its geometry and boundary conditions, a nonlinear inverse problem of estimating the electric impedance distribution can be solved. Several impedance estimation algorithms have been proposed to solve this problem. In this paper, we present a three-dimensional algorithm, based on the topology optimization method, as an alternative. A sequence of linear programming problems, allowing for constraints, is solved utilizing this method. In each iteration, the finite element method provides the electric potential field within the model of the domain. An electrode model is also proposed (thus, increasing the accuracy of the finite element results). The algorithm is tested using numerically simulated data and also experimental data, and absolute resistivity values are obtained. These results, corresponding to phantoms with two different conductive materials, exhibit relatively well-defined boundaries between them, and show that this is a practical and potentially useful technique to be applied to monitor lung aeration, including the possibility of imaging a pneumothorax.

Surgical management of glomus jugulare tumors: a proposal for approach selection based on tumor relationships with the facial nerve Clinical article

Object. The goal of this paper is to analyze the extension and relationships of glomus jugulare tumor with the temporal bone and the results of its surgical treatment aiming at preservation of the facial nerve. Based on the tumor extension and its relationships with the facial nerve, new criteria to be used in the selection of different surgical approaches are proposed. Methods. Between December 1997 and December 2007, 34 patients (22 female and 12 male) with glomus jugulare tumors were treated. Their mean age was 48 years. The mean follow-up was 52.5 months. Clinical findings included hearing loss in 88%, swallowing disturbance in 50%, and facial nerve palsy in 41%. Magnetic resonance imaging demonstrated a mass in the jugular foramen in all cases, a mass in the middle ear in 97%, a cervical mass in 85%, and an intradural mass in 41%. The tumor was supplied by the external carotid artery in all cases, the internal carotid artery in 44%, and the vertebral artery in 32%. Preoperative embolization was performed in 15 cases. The approach was tailored to each patient, and 4 types of approaches were designed. The infralabyrinthine retrofacial approach (Type A) was used in 32.5%; infralabyrinthine pre- and retrofacial approach without occlusion of the external acoustic meatus (Type B) in 20.5%; infralabyrinthine pre- and retrofacial approach with occlusion of the external acoustic meatus (Type C) in 41 W. and the infralabyrinthine approach with transposition of the facial nerve and removal of the middle ear structures (Type D) in 6% of the patients. Results. Radical removal was achieved in 91% of the cases and partial removal in 9%. Among 20 patients without preoperative facial nerve dysfunction, the nerve was kept in anatomical position in 19 (95%), and facial nerve function was normal during the immediate postoperative period in 17 (85%). Six patients (17.6%) had a new lower cranial nerve deficit, but recovery of swallowing function was adequate in all cases. Voice disturbance remained in all 6 cases. Cerebrospinal fluid leakage occurred in 6 patients (17.6%), with no need for reoperation in any of them. One patient died in the postoperative period due to pulmonary complications. The global recovery, based on the Karnofsky Performance Scale (KPS), was 100% in 15% of the patients, 90% in 45%, 80% in 33%, and 70% in 6%. Conclusions. Radical removal of glomus jugulare tumor can be achieved without anterior transposition of the facial nerve. The extension of dissection, however, should be tailored to each case based on tumor blood supply, preoperative symptoms, and tumor extension. The operative field provided by the retrofacial infralabyrinthine approach, or the pre- and retrofacial approaches. with or without Closure of the external acoustic meatus, allows a wide exposure of the jugular foramen area. Global functional recovery based on the KPS is acceptable in 94% of the patients. (DOI: 10.3171/2008.10.JNS08612)

Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: A non-parametric approach

Applying programming techniques to detailed data for 406 rice farms in 21 villages, for 1997, produces inefficiency measures, which differ substantially from the results of simple yield and unit cost measures. For the Boro (dry) season, mean technical efficiency was efficiency was 56.2 per cent and 69.4 per cent, allocative efficiency was 81.3 per cent, cost efficiency was 56.2 per cent and scale efficiency 94.9 per cent. The Aman (wet) season results are similar, but a few points lower. Allocative inefficiency is due to overuse of labour, suggesting population pressure, and of fertiliser, where recommended rates may warrant revision. Second-stage regressions show that large families are more inefficient, whereas farmers with better access to input markets, and those who do less off-farm work, tend to be more efficient. The information on the sources of inter-farm performance differentials could be used by the extension agents to help inefficient farmers. There is little excuse for such sub-optimal use of survey data, which are often collected at substantial costs.

Visual Biology of Hawaiian Coral Reef Fishes. III. Environmental Light and an Integrated Approach to the Ecology of Reef Fish Vision

In the previous two papers in this three-part series, we have examined visual pigments, ocular media transmission, and colors of the coral reef fish of Hawaii. This paper first details aspects of the light field and background colors at the microhabitat level on Hawaiian reefs and does so from the perspective and scale of fish living on the reef. Second, information from all three papers is combined in an attempt to examine trends in the visual ecology of reef inhabitants. Our goal is to begin to see fish the way they appear to other fish. Observations resulting from the combination of results in all three papers include the following. Yellow and blue colors on their own are strikingly well matched to backgrounds on the reef such as coral and bodies of horizontally viewed water. These colors, therefore, depending on context, may be important in camouflage as well as conspicuousness. The spectral characteristics of fish colors are correlated to the known spectral sensitivities in reef fish single cones and are tuned for maximum signal reliability when viewed against known backgrounds. The optimal positions of spectral sensitivity in a modeled dichromatic visual system are generally close to the sensitivities known for reef fish. Models also predict that both UV-sensitive and red-sensitive cone types are advantageous for a variety of tasks. UV-sensitive cones are known in some reef fish, red-sensitive cones have yet to be found. Labroid colors, which appear green or blue to us, may he matched to the far-red component of chlorophyll reflectance for camouflage. Red cave/hole dwelling reef fish are relatively poorly matched to the background they are often viewed against but this may be visually irrelevant. The model predicts that the task of distinguishing green algae from coral is optimized with a relatively long wavelength visual pigment pair. Herbivorous grazers whose visual pigments are known possess the longest sensitivities so far found. Labroid complex colors are highly contrasting complementary colors close up but combine, because of the spatial addition, which results from low visual resolution, at distance, to match background water colors remarkably well. Therefore, they are effective for simultaneous communication and camouflage.