Heuristic Guided Evolution

Exploiting knowledge to guide the evolutionary process in evolutionary computing is a concept that has the potential to increase the performance of evolutionary algorithms. The research question of this paper is “Can heuristics derived from past experiences be incorporated into evolutionary computing in order to increase the performance?” In order to answer the research question the following hypothesis is developed: “A heuristically-guided mutation of decision trees will outperform randomly mutated decision trees in terms of classification accuracy.”The methodology for answering the hypothesis is an experiment that tests a knowledge-guided mutation of a decision tree using heuristics created from prior decision trees as a form of knowledge. This is compared with a random mutation of the same decision tree. This experiment supports the theory that using knowledge in the form of heuristics to guide mutation will produce a difference in the performance of the classification of data instances. This supports the need for further research into knowledge guided evolutionary algorithms

The Use of Accounting Screens for Separating Winners from Losers Among the S&P 500 Stocks

This study uses accounting screens based on the Piotroski’s (2000) F-score and the derived MagicP formulae and finds that it is an effective investment strategy, which results in risk-adjusted outperformance of stocks with high book-to-market (BM) ratios over a market weighted benchmark portfolio and its subset of growth stocks. Unlike other studies that utilized similar tests on smaller firms, we examine the performance of large value stocks within the S&P 500 between 2007 and 2014 and find evidence of the value premium. The results were robust to the time period; in fact, the highest-ranked value stocks suffered less severely during the period of market correction

Hour of Code”: Can It Change Students’ Attitudes Toward Programming?

The Hour of Code is a one-hour introduction to computer science organized by Code.org, a non-profit dedicated to expanding participation in computer science. This study investigated the impact of the Hour of Code on students’ attitudes towards computer programming and their knowledge of programming. A sample of undergraduate students from two universities was selected to participate. Participants completed an Hour of Code tutorial as part of an undergraduate course. An electronic questionnaire was implemented in a pre-survey and post-survey format to gauge the change in student attitudes toward programming and their programming ability. The findings indicated the positive impact of the Hour of Code tutorial on students’ attitude toward programming. However, the students’ programming skills did not significantly change. The authors suggest that a deeper alignment of marketing, teaching, and content would help sustain the type of initiative exemplified by the Hour of Code

Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts

Visual question answering (VQA) is the task of answering questions about an image. The task assumes an understanding of both the image and the question to provide a natural language answer. VQA has gained popularity in recent years due to its potential applications in a wide range of fields, including robotics, education, and healthcare. In this paper, we focus on knowledge-augmented VQA, where answering the question requires commonsense knowledge, world knowledge, and reasoning about ideas and concepts not present in the image. We propose a multimodal framework that uses language guidance (LG) in the form of rationales, image captions, scene graphs, etc to answer questions more accurately. We benchmark our method on the multi-choice question-answering task of the A-OKVQA, Science-QA, VSR, and IconQA datasets using CLIP and BLIP models. We show that the use of language guidance is a simple but powerful and effective strategy for visual question answering. Our language guidance improves the performance of CLIP by 7.6% and BLIP-2 by 4.8% in the challenging A-OKVQA dataset. We also observe consistent improvement in performance on the Science-QA, VSR, and IconQA datasets when using the proposed language guidances. The implementation of LG-VQA is publicly available at https:// github.com/declare-lab/LG-VQA

How did university departments interweave the web: a study of connectivity and underlying factors.

Interacting with Computers, 10 (4): pp. 353-373.This paper presents two studies of the use of the WWW in Scottish universities and American land-grant universities. First, we investigated the relationship between the organisational profile of a university department in Scotland and its structural connectivity on the WWW. A Spearman rank order correlation analysis revealed a number of strong correlation relationships between structural connectivity measures and the organisational profile based on research assessment exercise ratings, teaching quality assessments, student–staff ratios and funding levels. Linkage patterns from 13 Scottish academic sites to commercial sites in Britain and America highlighted the impact of culture and the appropriateness of information technologies on the acceptance of the WWW. The second study is a content survey of WWW-based education activities in American land-grant universities to investigate successful applications of these enabling techniques in education. The two studies together highlighted cultural, political and technological interactions in the use of the WWW

Extracting personal information from conversations

Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications. Background facts about users can allow chatbot assistants to produce more topical and empathic replies. In the context of recommendation and retrieval models, personal facts can be used to customize the ranking results for individual users. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge. Such knowledge bases are easily interpretable and can provide users with full control over their own personal knowledge, including revising stored facts and managing access by downstream services for personalization purposes. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Mainstream extraction methods specialize on well-structured data, such as biographical texts or encyclopedic articles, which are rare for most people. In turn, conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts. In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers’ personal attributes: • Demographic attributes, age, gender, profession and family status, are inferred by HAMs - hierarchical neural classifiers with attention mechanism. Trained HAMs can be transferred between different types of conversational data and provide interpretable predictions. • Long-tailed personal attributes, hobby and profession, are predicted with CHARM - a zero-shot learning model, overcoming the lack of labeled training samples for rare attribute values. By linking conversational utterances to external sources, CHARM is able to predict attribute values which it never saw during training. • Interpersonal relationships are inferred with PRIDE - a hierarchical transformer-based model. To accurately predict fine-grained relationships, PRIDE leverages personal traits of the speakers and the style of conversational utterances. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.Personengebundene Fakten sind eine vielseitig nutzbare Quelle für die verschiedensten Anwendungen. Hintergrundfakten über Nutzer können es Chatbot-Assistenten ermöglichen, relevantere und persönlichere Antworten zu geben. Im Kontext von Empfehlungs- und Retrievalmodellen können personengebundene Fakten dazu verwendet werden, die Ranking-Ergebnisse für Nutzer individuell anzupassen. Eine Personengebundene Wissensdatenbank, gefüllt mit persönlichen Daten wie demografischen Angaben, Interessen und Beziehungen, kann eine universelle Schnittstelle für die Speicherung und Abfrage solcher Fakten sein. Wissensdatenbanken sind leicht zu interpretieren und bieten dem Nutzer die vollständige Kontrolle über seine personenbezogenen Fakten, einschließlich der Überarbeitung und der Verwaltung des Zugriffs durch nachgelagerte Dienste, etwa für Personalisierungszwecke. Um den Nutzern den aufwändigen manuellen Aufbau einer solchen persönlichen Wissensdatenbank zu ersparen, können automatisierte Extraktionsmethoden auf den textuellen Inhalten der Nutzer – wie z.B. Konversationen oder Beiträge in sozialen Medien – angewendet werden. Die üblichen Extraktionsmethoden sind auf strukturierte Daten wie biografische Texte oder enzyklopädische Artikel spezialisiert, die bei den meisten Menschen keine Rolle spielen. In dieser Dissertation beschäftigen wir uns mit der Gewinnung von persönlichem Wissen aus Dialogdaten und schlagen mehrere neuartige Deep-Learning-Modelle zur Ableitung persönlicher Attribute von Sprechern vor: • Demographische Attribute wie Alter, Geschlecht, Beruf und Familienstand werden durch HAMs - Hierarchische Neuronale Klassifikatoren mit Attention-Mechanismus - abgeleitet. Trainierte HAMs können zwischen verschiedenen Arten von Gesprächsdaten übertragen werden und liefern interpretierbare Vorhersagen • Vielseitige persönliche Attribute wie Hobbys oder Beruf werden mit CHARM ermittelt - einem Zero-Shot-Lernmodell, das den Mangel an markierten Trainingsbeispielen für seltene Attributwerte überwindet. Durch die Verknüpfung von Gesprächsäußerungen mit externen Quellen ist CHARM in der Lage, Attributwerte zu ermitteln, die es beim Training nie gesehen hat • Zwischenmenschliche Beziehungen werden mit PRIDE, einem hierarchischen transformerbasierten Modell, abgeleitet. Um präzise Beziehungen vorhersagen zu können, nutzt PRIDE persönliche Eigenschaften der Sprecher und den Stil von Konversationsäußerungen Experimente mit verschiedenen Konversationstexten, inklusive Reddit-Diskussionen und Filmskripten, demonstrieren die Praxistauglichkeit unserer Methoden und ihre hervorragende Leistung im Vergleich zum aktuellen Stand der Technik

Explicating the challenges of providing novel media experiences driven by user personal data

The turn towards personal data to drive novel media experiences has resulted in a shift in the priorities and challenges associated with media creation and dissemination. This paper takes up the challenge of explicating this novel and dynamic scenario through an interview study of employees delivering diverse personal data driven media services within a large U.K. based media organisation. The results identify a need for better interactions in the user-data-service ecosystem where trust and value are prioritised and balanced. Being legally compliant and going beyond just the mandatory to further ensure social accountability and ethical responsibility as an organisation are unpacked as methods to achieve this balance in data centric interactions. The work also presents how technology is seen and used as a solution for overcoming challenges and realising priorities to provide value while preserving trust within the personal data ecosystem