Building a Truly Distributed Constraint Solver with JADE

Real life problems such as scheduling meeting between people at different locations can be modelled as distributed Constraint Satisfaction Problems (CSPs). Suitable and satisfactory solutions can then be found using constraint satisfaction algorithms which can be exhaustive (backtracking) or otherwise (local search). However, most research in this area tested their algorithms by simulation on a single PC with a single program entry point. The main contribution of our work is the design and implementation of a truly distributed constraint solver based on a local search algorithm using Java Agent DEvelopment framework (JADE) to enable communication between agents on different machines. Particularly, we discuss design and implementation issues related to truly distributed constraint solver which might not be critical when simulated on a single machine. Evaluation results indicate that our truly distributed constraint solver works well within the observed limitations when tested with various distributed CSPs. Our application can also incorporate any constraint solving algorithm with little modifications.Comment: 7 page

Generating Weather Forecast Texts with Case Based Reasoning

Several techniques have been used to generate weather forecast texts. In this paper, case based reasoning (CBR) is proposed for weather forecast text generation because similar weather conditions occur over time and should have similar forecast texts. CBR-METEO, a system for generating weather forecast texts was developed using a generic framework (jCOLIBRI) which provides modules for the standard components of the CBR architecture. The advantage in a CBR approach is that systems can be built in minimal time with far less human effort after initial consultation with experts. The approach depends heavily on the goodness of the retrieval and revision components of the CBR process. We evaluated CBRMETEO with NIST, an automated metric which has been shown to correlate well with human judgements for this domain. The system shows comparable performance with other NLG systems that perform the same task.Comment: 6 page

Case reuse in textual case-based reasoning.

Text reuse involves reasoning with textual solutions of previous problems to solve new similar problems. It is an integral part of textual case-based reasoning (TCBR), which applies the CBR problem-solving methodology to situations where experiences are predominantly captured in text form. Here, we explore two key research questions in the context of textual reuse: firstly what parts of a solution are reusable given a problem and secondly how might these relevant parts be reused to generate a textual solution. Reasoning with text is naturally challenging and this is particularly so with text reuse. However significant inroads towards addressing this challenge was made possible with knowledge of problem-solution alignment. This knowledge allows us to identify specific parts of a textual solution that are linked to particular problem attributes or attribute values. Accordingly, a text reuse strategy based on implicit alignment is presented to determine textual solution constructs (words or phrases) that needs adapted. This addresses the question of what to reuse in solution texts and thereby forms the first contribution of this thesis. A generic architecture, the Case Retrieval Reuse Net (CR2N), is used to formalise the reuse strategy. Functionally, this architecture annotates textual constructs in a solution as reusable with adaptation or without adaptation. Key to this annotation is the discovery of reuse evidence mined from neighbourhood characteristics. Experimental results show significant improvements over a retrieve-only system and a baseline reuse technique. We also extended CR2N so that retrieval of similar cases is informed by solutions that are easiest to adapt. This is done by retrieving the top k cases based on their problem similarity and then determining the reusability of their solutions with respect to the target problem. Results from experiments show that reuse-guided retrieval outperforms retrieval without this guidance. Although CR2N exploits implicit alignment to aid text reuse, performance can be greatly improved if there is explicit alignment. Our second contribution is a method to form explicit alignment of structured problem attributes and values to sentences in a textual solution. Thereafter, compositional and transformational approaches to text reuse are introduced to address the question of how to reuse textual solutions. The main idea in the compositional approach is to generate a textual solution by using prototypical sentences across similar authors. While the transformation approach adapts the retrieved solution text by replacing sentences aligned to mismatched problem attributes using sentences from the neighbourhood. Experiments confirm the usefulness of these approaches through strong similarity between generated text and human references. The third and final contribution of this research is the use of Machine Translation (MT) evaluation metrics for TCBR. These metrics have been shown to correlate highly with human expert evaluation. In MT research, multiple human references are typically used as opposed to a single reference or solution per test case. An introspective approach to create multiple references for evaluation is presented. This is particularly useful for CBR domains where single reference cases (or cases with a single solution per problem) typically form the casebase. For such domains we show how multiple references can be generated by exploiting the CBR similarity assumption. Results indicate that TCBR systems evaluated with these MT metrics are closer to human judgements

[DESERTING MEDITATION OF THE GLORIOUS QUR’AN IN NIGERIA: CAUSES AND SOLUTION] العزوف عن تدبر القرآن الكريم في نيجيريا: دوافع وحلول

Meditating the Glorious Qur’an is one of the fundamental goals of its revelation for it advances faith, righteousness and morality, purifies the soul as well as promotes good systems. Many Nigerian Muslims, even if they recite and listen to Allah’s word, they hardly ponder over it. In view of this, the analysis examined factors responsible for this phenomenon with the aim of proffering solution to it. Using both descriptive and analytic methods, the paper identified lack of constant reciting and listening to the Glorious Qur’an, erroneous understanding of the ultimate objective of revealing and reading it, being preoccupied with observing mistakes and otherwise during its recital, deficiency in Arabic language, deserting tafsir literature and using the Qur’an as a source of livelihood as major hindrances against meditating the Book of Allah. It therefore, suggested that frequent reading and attentive listening to the Glorious Qur’an, learning Arabic language and studying tafsir literature, among others, will go a long way in overcoming this challenge. The paper recommended reviewing method of teaching the Qur’an in the country and adoption of Arabic as the language of communication in teaching Arabic subjects as well as medium of instruction for students of Islamic studies in Nigerian tertiary institutions.                                                                                                              إن تدبر كلام الله سبحانه وتعالى من الغايات التي من أجلها أنزل القرآن الكريم لما يترتب عليه من رسوخ الإيمان وزكاء النفوس وصلاح الأعمال واستقامة الأخلاق وسلامة الأنظمة. وقد غفل كثير من المسلمين في نيجيريا عن تدبر كلام ربهم وإن قرؤوه أو استمعوا إليه. لهذه المشكلة استهدفت المقالة تلمس الأسباب التي أدت إلى هذه الغفلة والبحث عن العوامل التي تعين على التغلب عليها. وباستخدام المنهج الوصفي التحليلي توصلت إلى أن من أهم هذه الأسباب قلة قراءة القرآن الكريم والاستماع إليه، والخطأ في فهم الغاية من إنزال القرآن وتلاوته، والانكباب على تتبع الأخطاء، والانشغال بأمور أخرى أثناء القراءة، وعدم فهم العربية، والبعد عن كتب التفسير والاسترزاق بالقرآن. كما توصلت الورقة إلى أن من العوامل التي تعين على التغلب على هذه الأسباب كثرة قراءة القرآن الكريم والإصغاء إليه بالترسل والإخلاص وحضور القلب، وبذل الجهد في تعلم العربية ومطالعة كتب التفسير، والعلم بأن القرآن لم ينزل للقراءة فقط، وإنما أنزل للقراءة والفهم والتدبر والإيمان والعمل. وأوصت المقالة بتحسين طريقة تدريس مادة القرآن الكريم في مدارس نيجيريا وتدريس مادة العربية باللغة العربية. كما أوصت بتدريس مواد الإسلاميات للمتخصصين في الإسلاميات في جامعاتها باللغة العربية بدلا من الإنجليزية

Comparison of Machine Learning Classifiers for Recognition of Online and Offline Handwritten Digits*

Automated recognition of handwritten digits has applications in several industries such as Postal and Banking for reading of addressed packages and cheques respectively. This paper compares four machine learning classifiers namely Naive Bayes, Instance Based Learner, Decision Tree and Neural Network for single digit recognition. Our experiments were conducted using the WEKA machine learning tool on two datasets; the MNIST offline handwritten digits and a collection of online ISGL handwritten digits acquired with a pen digitiser. Experiments were designed to allow for comparison within the datasets in a cross validation and across them where the online dataset is used for training and the offline dataset for testing and vice versa. We also compared classification accuracy at different levels of down sampling. Results indicate that the lazy learning instance based classifier performed slightly better than the neural network with a maximal accuracy of 97.86% and they both outperformed the other two classifiers: Naive Bayes and Decision Tree. The decision tree gave the worst performance of the four classifiers. We also discovered that better results were obtained with using the online digits when tested in a cross validation experiment. However, the pre-processed MNIST offline digits gave higher accuracies when used for training and tested with the online ISGL digits not vice versa. Also, we discovered down sampled size of 14x14 gave the best results for most of the four classifiers although these were not significantly different from the other down sampled sizes of 7x7, 21x21 and 28x28. We intend to investigate the performance of these classifiers in recognition of other characters (alphabets, punctuation and other symbols) as well as extend the recognition task to other levels of text granularity such as words, sentences and paragraphs. Keywords: Digits recognition, machine learning, classifiers, handwritten character recognition, Wek

RGU-ISTI-Essex at TREC 2011 Session Track

Mining query recommendation from query logs has attracted a lot of attention in recent years. We propose to use query recommendations extracted from the logs of a web search engine to solve the session track tasks. The runs are obtained by using the Search Shortcuts recommender system. The Search Shortcuts technique uses an inverted index and the concept of “successful sessions” present in a web search engine’s query log to produce effective recommendations for both frequent and rare/unseen queries. We adapt the above technique as a query expan- sion tool and use it to expand the given queries for Session Track at TREC 2011. The expansion is generated by using a method which aims to consider all past queries in the session. The expansion terms obtained are then used to build a global, uniformly weighted, representation of the user session (RL2). Furthermore, the expansion terms are then combined with a ranked list of results in order to boost terms appearing more frequently in the final results lists (RL3). Finally, we also integrate dwell times and the weighting method obtained taking both result lists and clicks into account for assigning weights to the terms to expand the final query of the session. In addition to that, we submitted a baseline run. It is based on the observation that using the term “wikipedia” to expand the query resulted in a better retrieval performance for the tasks at last year’s session track at TREC 2010

Development of mobile-interfaced machine learning-based predictive models for improving students' performance in programming courses

Student performance modelling (SPM) is a critical step to assessing and improving students' performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students' performance in programming courses were developed using M5P Decision Tree (MDT) and Linear Regression Classifier (LRC). The data used was gathered using a structured questionnaire from 295 students in 200 and 300 levels of study who offered Web programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012 and 2016. Hidden factors that are significant to students' performance in programming were identified. The relevant data gathered, normalized, coded and prepared as variable and factor datasets, and fed into the MDT algorithm and LRC to develop the predictive models. The developed models were obtained, validated and afterwards implemented in an Android 1.0.1 Studio environment. Extended Markup Language (XML) and Java were used for the design of the Graphical User Interface (GUI) and the logical implementation of the developed models as a mobile calculator, respectively. However, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE) and the Root Relative Squared Error (RRSE) were the metrics used to evaluate the robustness of MDT and LRC models. The evaluation results obtained indicate that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE and the RRSE having yielded the least values in all the evaluations conducted. Further results obtained established the strong significance of attitude of students and lecturers, fearful perception of students, erratic power supply, university facilities, student health and students' attendance to the performance of students in programming courses. The variable-based LRC model presented in this paper could provide baseline information about students' performance thereby offering better decision making towards improving teaching/learning outcomes in programming courses

Design of an Automatic Window Using a PIC Microcontroller and Stepper Motor

In this paper, the design of an automatic window is presented. The proposed window closes and opens automatically during and after a rainfall. The automatic system was developed with a focus on hospitals in order to allow medical staff and other supporting staff to concentrate on their primary responsibilities of taking care of patients. The system design includes a PIC16F877A microcontroller which gets activated when a moisture detector sensor sends a high logic signal to it. The microcontroller executes its embedded program by activating the stepper motor through a ULN2003 current-dependent integrated circuit (IC) chip resulting in stepwise control of the window. Hence, the window is automatically closed when rainfall is detected but opens and remains open when no rain is detected. We intend to extend our design to automatic opening and closing of the windows at others times in addition to during and after rainfall; for instance, window opening and closing every day at specific times in the morning and evening respectively