Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot

In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.Peer reviewedFinal Published versio

Robust Watermarking using Hidden Markov Models

Software piracy is the unauthorized copying or distribution of software. It is a growing problem that results in annual losses in the billions of dollars. Prevention is a difficult problem since digital documents are easy to copy and distribute. Watermarking is a possible defense against software piracy. A software watermark consists of information embedded in the software, which allows it to be identified. A watermark can act as a deterrent to unauthorized copying, since it can be used to provide evidence for legal action against those responsible for piracy.In this project, we present a novel software watermarking scheme that is inspired by the success of previous research focused on detecting metamorphic viruses. We use a trained hidden Markov model (HMM) to detect a specific copy of software. We give experimental results that show our scheme is robust. That is, we can identify the original software even after it has been extensively modified, as might occur as part of an attack on the watermarking scheme

New Digital Audio Watermarking Algorithms for Copyright Protection

This thesis investigates the development of digital audio watermarking in addressing issues such as copyright protection. Over the past two decades, many digital watermarking algorithms have been developed, each with its own advantages and disadvantages. The main aim of this thesis was to develop a new watermarking algorithm within an existing Fast Fourier Transform framework. This resulted in the development of a Complex Spectrum Phase Evolution based watermarking algorithm. In this new implementation, the embedding positions were generated dynamically thereby rendering it more difficult for an attacker to remove, and watermark information was embedded by manipulation of the spectral components in the time domain thereby reducing any audible distortion. Further improvements were attained when the embedding criteria was based on bin location comparison instead of magnitude, thereby rendering it more robust against those attacks that interfere with the spectral magnitudes. However, it was discovered that this new audio watermarking algorithm has some disadvantages such as a relatively low capacity and a non-consistent robustness for different audio files. Therefore, a further aim of this thesis was to improve the algorithm from a different perspective. Improvements were investigated using an Singular Value Decomposition framework wherein a novel observation was discovered. Furthermore, a psychoacoustic model was incorporated to suppress any audible distortion. This resulted in a watermarking algorithm which achieved a higher capacity and a more consistent robustness. The overall result was that two new digital audio watermarking algorithms were developed which were complementary in their performance thereby opening more opportunities for further research

A study of the relationship between a child's achievement and adjustment in grades one and three.

Thesis (Ed.M.)--Boston Universit

Examination of student nurses' self-recognition and codependence

Objectives: Because the nursing profession demands the provision of services and continuous interaction with people uninterruptedly for 24 hours a day, self-recognıtıon is important for nurses. With the patient aiming to fulfil his/her needs for care and the nurse without adequate self-recognıtıon aiming to compensate her own emotional needs, both parts can reciprocally compromise on their own needs and become dependent on each other in the relationship. The aim of this study to examine the self-recognition of the nursing students and the conditions of co-dependence. Methods: The research has been designed to be quantitative, cross-sectional and correlative. Sample of the research consists of 446 students attending nursery undergraduate program. Giessen Test (GT), Co-dependency Assessment Tool (CODAT) and a data collection form, which includes socio-demographic characteristics, have been applied to par ticipants. Data analysis has been carried out with the SPSS 18.0 packet program. Mean, standard deviation, and the minimum and the maximum values were calculated for the quantitative variables. Pearson’s correlation test was used for analyzing the independent variables. Results: The study demonstrated that the fourth-grade students preferred to be controlled, socially potent, and sub missive, and they had depressive personality characteristics. Regarding the co-dependency status of the nursing stu dents, it was observed that the first-grade students tended to focus on the others, their self-worth was high, and they displayed a hiding self compared to the fourth-grade students. A positive correlation was found between self-aware ness and co-dependency. Also, it was found that self-worth was positively correlated with the social potency-social impotency and hypomanic-depressive features (p<0.01). Conclusion: Self-recognıtıon and co-dependency characteristics of student nurses were affected by self-worth, so cial potency/impotency, and hypomanic-depressive characteristics. İn order to prevent/correct co-dependence and to improve self-recognıtıon adequately in student nurses, education and training activities for supporting students' self-worth, self-recognition, affecting their mood and social potency favorably

The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference

State Highlights 1/17/1946

This is the student newspaper from Western State High School, the high school that was on the campus of Western Michigan University, then called State Highlights, in 1946

Musipher: Hiding information in music composition

In this paper, we present a new way of hiding information. We store the information directly in the process of composing music, based on musical theory. We created an algorithm to produce music based on binary string, where each bit is transformed into a music composition decision. We follow simple rules to make music, which sounds good. We conducted survey to find whether our solution works, and found promising results of our approach