TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

Analyzing Social and Stylometric Features to Identify Spear phishing Emails

Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack. This information is then used to create sophisticated, genuine-looking attack vectors, drawing the victim to compromise confidential information. What makes spear phishing different, and more powerful than normal phishing, is this contextual information about the victim. Online social media services can be one such source for gathering vital information about an individual. In this paper, we characterize and examine a true positive dataset of spear phishing, spam, and normal phishing emails from Symantec's enterprise email scanning service. We then present a model to detect spear phishing emails sent to employees of 14 international organizations, by using social features extracted from LinkedIn. Our dataset consists of 4,742 targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack emails sent to 5,912 non victims; and publicly available information from their LinkedIn profiles. We applied various machine learning algorithms to this labeled data, and achieved an overall maximum accuracy of 97.76% in identifying spear phishing emails. We used a combination of social features from LinkedIn profiles, and stylometric features extracted from email subjects, bodies, and attachments. However, we achieved a slightly better accuracy of 98.28% without the social features. Our analysis revealed that social features extracted from LinkedIn do not help in identifying spear phishing emails. To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishing emails.Comment: Detection of spear phishing using social media feature

Utilizing Big Data Analytics to Improve Education

Analytics can be defined as the process of determining, assessing, and interpreting meaning from volumes of data. It has been categorized in three different categories - descriptive, predictive and prescriptive. Predictive analysis can serve many segments of society as it can reveal hidden relationship which may not be apparent with descriptive modeling. Analytics advancement plays an important role in higher education planning. It answers several questions such as -which students will enroll in particular course, what courses are on trending or obsolete, what is the level of student satisfaction in the current education system, effectiveness of online study environment, how to design a better curriculum, likelihood of students transfer, drop out or failure to complete the course. Not only, data analytics helps in analyzing above points but also can be helpful in predictive modeling for faculty, administrative and students groups who are looking out for genuine results about the university rankings, based on which they make their decisions. Using the dataset “Academic Ranking of World Universities, 2003-2014”, we studied and analyzed to forecast how university’s management and faculty could adapt to changes to improve their education and thereby the ranking of their universities in the upcoming years. Microsoft SQL Server Data Mining Add-ins Excel 2008 was employed as a software mining tool for predicting the trending university ranking. This research paper concentrates upon predictive analysis of university ranking using forecasting based on data mining technique

Implementing machine learning into industrial plant energy supervision

Machine learning is a concept in which a machine can learn from its past experiences. The machine learning trend is rising in popularity, and this is the reason this method has been chosen for this thesis. The purpose of this thesis is to analyze how well machine learning analyzes energy usage in a zinc plant. This thesis work has been split into two pieces as the electrowinning works separately and the roaster and sulphuric acid plant work together. The roaster and sulphuric acid plant are connected and therefore the power changes affect the whole process. There are thousands of signals from the plant so to make the analyses as good as possible only the essential ones were chosen. The electrowinning data was analyzed with Random Forest and Roaster and sulphury acid data was analyzed with Neural Network. During this thesis multiple programs have been exploited such as SQL was used to extract the data, Microsoft excel was for editing data and the analyses were executed with Orange data mining program as it is a good method to test and visualize data. The results show that the tests are successful and how the different categories can be found with the help of machine learning. And that when watching what the machine learning methods priorities as important factors, it is very different from what we consider as vital information. However, when simulating classes that were not successful there was not as much success as when the simulations were done with the imaginary situations and the values did not match what a real event would look like. Koneoppiminen on konsepti, jossa kone pystyy oppimaan aikaisemmista tapahtumista. Koneoppimisien kiinnostavuus on kasvussa ja tämä on yksi syy, miksi juuri tämä työ valittiin. Tämän tutkielman tarkoituksena on analysoida, miten hyvin koneoppiminen pystyy analysoimaan energian käyttöä sinkin tuotannossa. Tämä työ on jaettu kahteen osaan, koska elektrolyysin energian-käyttö ei suoraan vaikuta pasuttamon ja rikkihappotehtaan energia kulutukseen. Pasutus ja rikkihappotehdas ovat prosessikokonaisuus, joten energia muutokset pasutuksessa vaikkutavat happotehtaaseen. Prosessissa on tuhansia signaaleja ja jotta saisimme analysoinnista mahdollisimman hyvän, oli olennaista valita vain tärkeimmät signaalit. Elektrolyysi osassa käytettiin Random Forest menetelmää ja pasuttamoon ja happotehtaan analysointiin käytettiin neuroverkko menetelmää. Tämän työn aikana käytettiin useita ohjelmia kuten SQL:ää käytettiin datan poimintaan, Microsoft Exceliä käytettiin data muokkaamiseen ja Orange data mining ohjelmaa käytettiin analysointiin ja visualisointiin. Tulokset osoittavat, että testit onnistuivat ja näyttävät myös, miten koneoppi pystyy luokittelemaan eri tyyppistä dataa. Myös, siten miten koneoppiminen priorisoi signaaleja, tämä riippuu todella paljon siitä, miten me katsomme signaalien painoarvoa. Mutta kuin simuloimme vääränlaisia luokkia niin tulokset eivät olleet niin hyviä kuten olimme toivoneet, ja tämä riippuu todennäköisesti siitä että simuloitavat arvot eivät ole saman tyyppisiä kuten miltä arvot olisivat todellisessa vikatilanteessa

Automating iterative tasks with programming by demonstration

Programming by demonstration is an end-user programming technique that allows people to create programs by showing the computer examples of what they want to do. Users do not need specialised programming skills. Instead, they instruct the computer by demonstrating examples, much as they might show another person how to do the task. Programming by demonstration empowers users to create programs that perform tedious and time-consuming computer chores. However, it is not in widespread use, and is instead confined to research applications that end users never see. This makes it difficult to evaluate programming by demonstration tools and techniques. This thesis claims that domain-independent programming by demonstration can be made available in existing applications and used to automate iterative tasks by end users. It is supported by Familiar, a domain-independent, AppleScript-based programming-by-demonstration tool embodying standard machine learning algorithms. Familiar is designed for end users, so works in the existing applications that they regularly use. The assertion that programming by demonstration can be made available in existing applications is validated by identifying the relevant platform requirements and a range of platforms that meet them. A detailed scrutiny of AppleScript highlights problems with the architecture and with many implementations, and yields a set of guidelines for designing applications that support programming-by-demonstration. An evaluation shows that end users are capable of using programming by demonstration to automate iterative tasks. However, the subjects tended to prefer other tools, choosing Familiar only when the alternatives were unsuitable or unavailable. Familiar's inferencing is evaluated on an extensive set of examples, highlighting the tasks it can perform and the functionality it requires

Using Markov Chains and Data Mining Techniques to Predict Students’ Academic Performance

In this study, the academic performance of students from the E-Commerce department at Palestine Technical University – Kadoorie is predicted using a Markov chains model and educational data mining. Based on the complete data regarding the achievements of the students from the 2016 cohort of students obtained from the university’s admissions and registration department, a Markov chain is built, in which the states are divided according to the semester average of the student, and the ratio of students in each state is calculated in the long run. The results obtained are compared with the data from the 2015 cohort, which demonstrates the efficiency of the Markov chains model. For educational data mining, the classification technique is applied, and the decision tree algorithm is used to predict the academic performance of the students, generalizing results with an accuracy of 41.67%