Designing the interface between research, learning and teaching.

Abstract: This paper’s central argument is that teaching and research need to be reshaped so that they connect in a productive way. This will require actions at a whole range of levels, from the individual teacher to the national system and include the international communities of design scholars. To do this, we need to start at the level of the individual teacher and course team. This paper cites some examples of strategies that focus on what students do as learners and how teachers teach and design courses to enhance research-led teaching. The paper commences with an examination of the departmental context of (art and) design education. This is followed by an exploration of what is understood by research-led teaching and a further discussion of the dimensions of research-led teaching. It questions whether these dimensions are evident, and if so to what degree in design departments, programmes and courses. The discussion examines the features of research-led departments and asks if a department is not research-led in its approach to teaching, why it should consider changing strategies

Two-layer classification and distinguished representations of users and documents for grouping and authorship identification

Most studies on authorship identification reported a drop in the identification result when the number of authors exceeds 20-25. In this paper, we introduce a new user representation to address this problem and split classification across two layers. There are at least 3 novelties in this paper. First, the two-layer approach allows applying authorship identification over larger number of authors (tested over 100 authors), and it is extendable. The authors are divided into groups that contain smaller number of authors. Given an anonymous document, the primary layer detects the group to which the document belongs. Then, the secondary layer determines the particular author inside the selected group. In order to extract the groups linking similar authors, clustering is applied over users rather than documents. Hence, the second novelty of this paper is introducing a new user representation that is different from document representation. Without the proposed user representation, the clustering over documents will result in documents of author(s) distributed over several clusters, instead of a single cluster membership for each author. Third, the extracted clusters are descriptive and meaningful of their users as the dimensions have psychological backgrounds. For authorship identification, the documents are labelled with the extracted groups and fed into machine learning to build classification models that predicts the group and author of a given document. The results show that the documents are highly correlated with the extracted corresponding groups, and the proposed model can be accurately trained to determine the group and the author identity

Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the word-level and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-the-art emotion prediction algorithms

Mining online diaries for blogger identification

In this paper, we present an investigation of authorship identification on personal blogs or diaries, which are different from other types of text such as essays, emails, or articles based on the text properties. The investigation utilizes couple of intuitive feature sets and studies various parameters that affect the identification performance. Many studies manipulated the problem of authorship identification in manually collected corpora, but only few utilized real data from existing blogs. The complexity of the language model in personal blogs is motivating to identify the correspondent author. The main contribution of this work is at least three folds. Firstly, we utilize the LIWC and MRC feature sets together, which have been developed with Psychology background, for the first time for authorship identification on personal blogs. Secondly, we analyze the effect of various parameters, and feature sets, on the identification performance. This includes the number of authors in the data corpus, the post size or the word count, and the number of posts for each author. Finally, we study applying authorship identification over a limited set of users that have a common personality attributes. This analysis is motivated by the lack of standard or solid recommendations in literature for such task, especially in the domain of personal blogs. The results and evaluation show that the utilized features are compact while their performance is highly comparable with other larger feature sets. The analysis also confirmed the most effective parameters, their ranges in the data corpus, and the usefulness of the common users classifier in improving the performance, for the author identification task

“Why the Anomaly that is Super Bowl Marketing is a Justifiable Investment”

By now, we have well established that the Super Bowl is the holy grail of marketing, the championship for the battle of the brands, and the ultimate showcase of creative prowess which determines bragging rights. This American phenomenon is the exception, because it’s the one time on the calendar where viewers are mesmerized by commercials instead of tuning them out as noise. There are critical strategic objectives which can be satisfied, revolutionizing the brand in the eyes of the consumer and drastically expanding brand awareness. We know the vast benefits that well-executed marketing schemes can have for companies, especially during the Super Bowl, which initiate significant implications. The proof of effectiveness is obvious when observing statistics for the 2018 Super Bowl: • An average viewership of 103.4 million, escalating to 112.3 million at the end of the game. (Nielsen 2018) • 68% of homes with functioning T.V’s were tuned into the Super Bowl broadcast. (Nielsen 2018) • 170.7 million social media interactions across Facebook, Instagram, and Twitter. (Nielsen 2018) • Digital viewership of 2.02 million viewers a minute, a streaming record. (Nielsen 2018) • Price of 30 second advertisement maximized at $5.2 million (American Marketing Association) • Aggregate Super Bowl ad spending over 52 year history (1967-2018):$6.9 billion adjusted for inflation. (AdAge 2018) Granted, there are some viable concerns associated with Super Bowl advertising. Because of immense scrutiny, marketers need to be conscious of the impact repercussions of attempting to make a statement which backfires can have. Attending to and reconciling public backlash can be difficult and can severely damage brand perception. Negative news surrounding the NFL have also been hot topics of debate recently. However, while some of these issues may erode some viewership in the short run, as 2018 statistics minimally decreased from 2017, the future trajectory of the Super Bowl is not truly threatened. Actually, the New York Times (Maheshwari, 2018) explains how “In an era of cord-cutting and ad-skipping, the Super Bowl is a sweet salve for the nation’s marketers.” Because of the evolution of on-demand, marketers are forced to deliberate if T.V. advertisements are worth it, with one exception: live sports. The Atlantic (Thompson, 2013) portrays this concept perfectly, stating “But in a time-delayed video world, the biggest games still drive dependable live audiences, making sports rights the most valuable resource in the whole TV ecosystem.” The consequence of this reality: almost no one records on-demand sports to skip the commercials because we can’t avoid the social media buzz which chronicles how games develop. Because the love for sports will never expire, the Super Bowl will never become obsolete for marketers. At the end of the day, the Super Bowl is the marketing anomaly that has solidified its stranglehold as the pinnacle of advertising. The big game is so rooted into American culture that Super Bowl Sunday has become a holiday for millions across our great nation. As CNN Money (Disis, 2018) explains, “It\u27s simple. The NFL\u27s marquee event is TV\u27s biggest game in town, and nothing else even comes close.” Marketers who need to distinguish their brand as a supreme offering to secure competitive advantage over competitors (ahem, everyone) need to seize the moment. The habitual winners of Super Bowl advertising significantly elevate their status in the hearts and minds of the American people. My declared Super Bowl advertising champion, Anheuser-Busch InBev (responsible for Budweiser and Bud Light), absolutely dominates the American beer market. Super Bowl regulars undoubtedly think of Budweiser’s “Puppy Love” (2014) spot with the legendary Clydesdales or the dramatic “Bud Bowl” (1989-91) series when they crack a cold brew. My theory: it’s no mistake that the best in the Super Bowl advertising realm is also the “King of Beers” because of their supreme strategy and execution on the marketing gridiron’s biggest stage

Examining Accumulated Emotional Traits in Suicide Blogs With an Emotion Topic Model

Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limits the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people’s daily writings (i.e. Blogs), and examining these emotional traits which are predictive of suicidal behaviors. A complex emotion topic (CET) model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i.e., accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and non-suicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors