Due to rapid growth of the number of Web pages, web users encounter two main problems, namely: many of the retrieved documents are not related to the user query which is called low precision, and many of relevant documents have not been retrieved yet which is called low recall. Information Retrieval (IR) is an essential and useful technique for Web search; thus, different approaches and techniques are developed. Because of its parallel mechanism with high-dimensional space, Genetic Algorithm (GA) has been adopted to solve many of optimization problems where IR is one of them. This thesis proposes searching model which is based on GA to retrieve HTML documents. This model is called IR Using GA or IRUGA. It is composed of two main units. The first unit is the document indexing unit to index the HTML documents. The second unit is the GA mechanism which applies selection, crossover, and mutation operators to produce the final result, while specially designed fitness function is applied to evaluate the documents. The performance of IRUGA is investigated using the speed of convergence of the retrieval process, precision at rank N, recall at rank N, and precision at recall N. In addition, the proposed fitness function is compared experimentally with Okapi-BM25 function and Bayesian inference network model function. Moreover, IRUGA is compared with traditional IR using the same fitness function to examine the performance in terms of time required by each technique to retrieve the documents. The new techniques developed for document representation, the GA operators and the fitness function managed to achieves an improvement over 90% for the recall and precision measures. And the relevance of the retrieved document is much higher than that retrieved by the other models. Moreover, a massive comparison of techniques applied to GA operators is performed by highlighting the strengths and weaknesses of each existing technique of GA operators. Overall, IRUGA is a promising technique in Web search domain that provides a high quality search results in terms of recall and precision.

Most research studies in the batch process control problem are focused on optimizing system performance. The methods address the problem by minimizing single criterion such as cycle time and tardiness, or bi-criteria such as cycle time and tardiness, and earliness and tardiness. This research demonstrates the use of Stochastic Utility Evaluation (SUE) function approach to optimize system performance using multiple criteria. In long production cycles, the earliness and tardiness weight (utility) of products vary depending on the time. As the time approaches the due-date, it affects contractual penalties, loss of customer goodwill and the storage period for the completed products. It is necessary to reflect the weight of products for earliness and tardiness at decision epochs to decide on the optimal strategy. This research explores how stochastic utility function using stochastic information can be derived and used to strategically improve existing approaches for the batch process control problem. This research first explores how SUE function can be applied to existing model for bi-objective problem such as cycle time and tardiness. Benchmark strategies using SUE function (NACH-SUE, MBS-SUE, No idle and full batch) are compared to each other. The experimental results show that NACH-SUE effectively improves mean cycle time and tardiness performance respectively than other benchmark strategies. Next, SUE function for earliness and tardiness is used in an existing model to develop a tri-objective problem. Typically, this problem is very complex to solve due to its trade-off relationship. However SUE function makes it relatively easy to solve the tri-objective problem since SUE function can be incorporated in an existing model. It is observed that SUE function can be effectively used for solving a tri-objective problem. Performance improvement for averaged value of cycle time, earliness and tardiness is observed under a comprehensive set of experimental conditions.

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Darbe atskleidžiami 4–5 klasių pažangos ir pasiekimų vertinimo ypatumai muzikos pamokose. Baigiamojo darbo tikslas – išsiaiškinti 4–5 kl. mokinių pažangos ir pasiekimų vertinimo muzikos pamokoje ypatumus. Darbo tikslui pasiekti iškelti šie uždaviniai: išanalizuoti mokinių pažangos ir pasiekimų vertinimo sampratą, aptarti vertinimo ypatumus muzikos pamokose, apžvelgti muzikinio ugdymo ypatumus bendrojo lavinimo mokyklų 4–5 klasėse, aptarti 4–5 klasės mokinių amžiaus tarpsnį, ištirti mokinių požiūrį į pasiekimų vertinimo skirtumus 4–5 klasėse. Tyrimas atliktas trijose Šiaulių progimnazijose. Anketinės apklausos metodu buvo siekta sužinoti 4–5 klasių mokinių požiūrį į pažangos ir pasiekimų vertinimą. Išanalizavus mokslinę literatūrą ir atlikus anketinę apklausą gautos tokios išvados: vertinimas – nuolatinis informacijos rinkimas, kaupimas ir apibendrinimas apie mokinio pažangą ir pasiekimus. Vertinant muzikinio ugdymo pažangą ir pasiekimus taikomi diagnostinio, formaliojo ir apibendrinamojo vertinimo būdai. Ypatingai svarbus muzikos ugdyme tampa formuojamasis vertinimas, kuris padeda pasiekti, kad kiekvienas mokinys atskleistų individualius savo gebėjimus. Muzikinis ugdymas, kaip ir kiti mokomieji dalykai gali būti vertinamas formaliuoju (vertinimas pažymiais) ir neformaliuoju (nesiejamas su pažymiu). Lyginant ketvirtos ir penktos klasės muzikinį ugdymą bendrojo lavinimo mokykloje pastebima, jog penktoje klasėje pakinta mokymo turinys, pamokos struktūra, vadovėliai, nauji... [toliau žr. visą tekstą] / The paper discloses 4th-5th forms’ achievement assessment features during music lessons. Aim of the final paper is to figure 4th-5th grade students’ progress and achievement evaluation features in the music lessons. In order to achieve the aim, the following tasks were set: to analyze student progress and achievement evaluation concept, to discuss the evaluation features in the music lessons, to review the features of music education in general education schools in grades 4-5, to discuss grades 4-5 students’ age period, to explore students' views of the differences assessments in the 4th-5th forms. The study was conducted in three Siauliai junior gymnasiums. Questionnaire survey method was aimed to find out 4-5 grade students’ attitude towards progress and outcomes. Analysis of scientific literature and questionnaire survey led to the following conclusions: evaluation is a continuous information gathering, compilation and summarizing about the student's progress and achievements. In the assessment of music education progress and achievements, diagnostic, formal and summative assessment methods are applied. Formative assessment becomes particularly important for music education, it helps to make each student to disclose their individual capacities. Music education, as well as other subjects can be seen in the formal (assessment with marks) and informal (not associated with marks) methods. Comparing the fourth and fifth-grade music education in the general education school it... [to full text]

Arimaa is a strategic board game for two players. It was designed to be simple for human players and difficult for computers. The aim of this thesis is to design and implement the program with features of the artificial intelligence, which would be able to defeat human players. The implementation was realized in the three key parts: evaluation position, generation of moves and search. The program was run on the game server and defeated many bots as well as human players.

While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it. Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own. One approach to obtain such a strategy is to analyze the game rules and create a state evaluation function that can be subsequently used to direct the agent to promising states in the match. In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function. Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent.

This thesis deals with the problem of newborn seizre detection from the Electroencephalogram (EEG) signals. The ultimate goal is to design an automated seizure detection system to assist the medical personnel in timely seizure detection. Seizure detection is vital as neurological diseases or dysfunctions in newborn infants are often first manifested by seizure and prolonged seizures can result in impaired neuro-development or even fatality. The EEG has proved superior to clinical examination of newborns in early detection and prognostication of brain dysfunctions. However, long-term newborn EEG signals acquisition is considerably more difficult than that of adults and children. This is because, the number of the electrodes attached to the skin is limited by the size of the head, the newborns EEGs vary from day to day, and the newborns are reluctant of being in the recording situation. Also, the movement of the newborn can create artifact in the recording and as a result strongly affect the electrical seizure recognition. Most of the existing methods for neonates are either time or frequency based, and, therefore, do not consider the non-stationarity nature of the EEG signal. Thus, notwithstanding the plethora of existing methods, this thesis applies the discrete wavelet transform (DWT) to account for the non-stationarity of the EEG signals. First, two methods for seizure detection in neonates are proposed. The detection schemes are based on observing the changing behaviour of a number of statistical quantities of the wavelet coefficients (WC) of the EEG signal at different scales. In the first method, the variance and mean of the WC are considered as a feature set to dassify the EEG data into seizure and non-seizure. The test results give an average seizure detection rate (SDR) of 97.4%. In the second method, the number of zero-crossings, and the average distance between adjacent extrema of the WC of certain scales are extracted to form a feature set. The test obtains an average SDR of 95.2%. The proposed feature sets are both simple to implement, have high detection rate and low false alarm rate. Then, in order to reduce the complexity of the proposed schemes, two optimising methods are used to reduce the number of selected features. First, the mutual information feature selection (MIFS) algorithm is applied to select the optimum feature subset. The results show that an optimal subset of 9 features, provides SDR of 94%. Compared to that of the full feature set, it is clear that the optimal feature set can significantly reduce the system complexity. The drawback of the MIFS algorithm is that it ignores the interaction between features. To overcome this drawback, an alternative algorithm, the mutual information evaluation function (MIEF) is then used. The MIEF evaluates a set of candidate features extracted from the WC to select an informative feature subset. This function is based on the measurement of the information gain and takes into consideration the interaction between features. The performance of the proposed features is evaluated and compared to that of the features obtained using the MIFS algorithm. The MIEF algorithm selected the optimal 10 features resulting an average SDR of 96.3%. It is also shown, an average SDR of 93.5% can be obtained with only 4 features when the MIEF algorithm is used. In comparison with results of the first two methods, it is shown that the optimal feature subsets improve the system performance and significantly reduce the system complexity for implementation purpose.

While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it. Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own. One approach to obtain such a strategy is to analyze the game rules and create a state evaluation function that can be subsequently used to direct the agent to promising states in the match. In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function. Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent.:Introduction Game Playing Evaluation Functions I - Aggregation Evaluation Functions II - Features General Evaluation Related Work Discussion