Insight Research Collection

Arguably, our current understanding of creativity has a few gaps that might benefit from some analysis. In the paper, I review the main empirical findings and theoretical proposals on the core cognitive processes of creative thinking, outlining some of the deficiencies therein. I then develop a meta-analysis of the interactions between the main components of the creative universe; namely, the World, Language and Experience. In this analysis, I try to show that creativity often emerges at the interstices between some aspect of the World and our Experience (our understanding of the World), or some aspect of the World and Language (our linguistic descriptions of that World), or some aspect our Experience and Language. To demonstrate these points, I use this analysis to explain the emergence of extreme literary creativity in Ireland at the turn of the last century. More generally, it is hoped that this analysis offers a new perspective on all aspects of creativity and how they might be approached.

Nowadays, financial data analysis is becoming increasingly importantin the business market. As companies collect more and more data fromdaily operations, they expect to extract useful knowledge from existing collecteddata to help make reasonable decisions for new customer requests, e.g. usercredit category, churn analysis, real estate analysis, etc. Financial institutes haveapplied different data mining techniques to enhance their business performance.However, simple approach of these techniques could raise a performance issue.Besides, there are very few general models for both understanding and forecastingdifferent financial fields. We present in this paper a new classification modelfor analyzing financial data. We also evaluate this model with different realworlddata to show its performance

It has previously been shown that low-frequency neuromuscular electrical stimulation (NMES) techniques can induce increases in energy expenditure similar to those associated with exercise. This study investigated the metabolic and cardiovascular effects of a 1 h session of lower limb NMES and compared cardiovascular response with that observed during walking in nine obese subjects (three males) (age = 43.8 ± 3.0 years; body mass index (BMI) = 41.5 ± 1.8 kg/m2). The NMES protocol consisted of delivering a complex pulse pattern to the thigh muscles for 1 h. The walking test consisted of five 4-min bouts starting at 2 km/h with 1 km/h increments up to 6 km/h. In both tests, an open-circuit gas analyser was used to assess O2 consumption (O2), CO2 production (CO2), respiratory exchange ratio (RER), and heart rate (HR). Rates of fat oxidation (RFO) and carbohydrate oxidation (CHO) were estimated by indirect calorimetry. One hour of NMES significantly increased O2, HR, RER, and mean energy expenditure compared with resting values, reaching 8.7 ± 1.3 mL·min−2·kg−1 (47% of O2peak), 114.8 ± 7.5 bpm, 0.95, and 318.5 ± 64.3 kcal/h, respectively. CHO, but not RFO, increased during 1 h of NMES. With NMES, CHO was greater and RFO was less than at all walking speeds except 6 km/h. Lactate also increased more with NMES, to 3.5 ± 0.7 mmol versus a maximum of 1.5 ± 0.3 mmol with the walking protocol. These results suggest that NMES can be used in an obese population to induce an effective cardiovascular exercise response. In fact, the observed increase in energy expenditure induced by 1 h of NMES is clinically important and comparable with that recommended in weight management programs.

In this paper we describe a generic approach to modeling user reputation in online social platforms based on an underlying model of collaboration. This distinguishes our approach from more conventional reputation models which are often based around ad-hoc activity metrics. We evaluate our model with respect to a conventional reputation model used by 3 social Q&A websites, each based on a different topical domain.

This paper presents a Recurrent Neural Network approach for the multi purpose machines Job Shop Scheduling Problem. This case of JSSP can be utilized for the modelling of project portfolio management besides the well known adoption in factory environment. Therefore, each project oriented organization develops a set of projects and it has to schedule them as a whole. In this work, we extended a bi-objective system model based on the JSSP modelling and formulate dit as a combination of two recurrent neural networks. In addition, we designed an example within its neural networks that are focused on the Make span and the Total Weighted Tardiness objectives. Moreover, we present the findings of our approach using a set of well known benchmark instances and the discussion about them and the singularity that arises

The world of web search is changing. Mainstream search engines like Google and Bing are adding social signals to conventional query-based services while social networks like Twitter and Facebook are adding query-based search to sharing-based services. Our search and discovery system, Yokie, harnesses the wisdom of the crowd of communities of Twitter users to create indexes of proto-content (or recently shared content) that is typically not yet indexed by mainstream search engines. The system includes an architecture [13] for a range of contextual queries and ranking strategies beyond standard relevance. In this paper, we focus on evaluating Yokies ability to retrieve timely, relevant and exclusive results with which users interacted and found useful, compared to other standard web services.

A new generation of curation services provides users with a set of tools to manually curate and manage topical collections of content. However, given curation is ultimately a manual effort, it still requires significant effort on the part of the curator both in terms of collecting and managing content. We are interested in providing additional assistance to users in their curation tasks, in particular when it comes to efficiently adding content to their collection, and examine recommender systems in an effort to automate this task. We examine a number of recommendation strategies using live-user data from the popular Scoop.it curation service.

Background: There is emerging evidence that isometric Neuromuscular Electrical Stimulation (NMES) may offer a way to elicit therapeutically significant increases in whole-body oxygen uptake in order to deliver aerobic exercise to patients unable to exercise volitionally, with consequent gains in cardiovascular health. The optimal stimulation frequency to elicit a significant and sustained pulmonary oxygen uptake has not been determined. The aim of this study was to examine the frequency response of the oxygen uptake and evoked torque due to NMES of the quadriceps muscles across a range of low frequencies spanning the twitch to tetanus transition. Methods: Ten healthy male subjects underwent bilateral NMES of the quadriceps muscles comprising eight 4 minute bouts of intermittent stimulation at selected frequencies in the range 1 to 12 Hz, interspersed with 4 minutes rest periods. Respiratory gases and knee extensor torque were simultaneously monitored throughout. Multiple linear regression was used to fit the resulting data to an energetic model which expressed the energy rate in terms of the pulse frequency, the torque time integral and a factor representing the accumulated force developed per unit time. Results: Additional oxygen uptake increased over the frequency range to a maximum of 564 (SD 114) ml min-1 at 12 Hz, and the respiratory exchange ratio was close to unity from 4 to 12 Hz. While the highest induced torque occurred at 12 Hz, the peak of the force development factor occurred at 6 Hz. The regression model accounted for 88% of the variability in the observed energetic response. Conclusions: Taking into account the requirement to avoid prolonged tetanic contractions and to minimize evoked torque, the results suggest that the ideal frequency for sustainable aerobic exercise is 4 to 5 Hz, which coincided in this study with the frequency above which significant twitch force summation occurred.

Advancing age is associated with a gradual decline in muscle strength, exercise tolerance and subsequent capacity for activities of daily living. It is important that we develop effective strategies to halt this process of gradual decline in order to enhance functional ability and capacity for independent living. To achieve this, we must overcome the challenge of sustaining ongoing engagement in physical exercise programmes in the sedentary elderly population, particularly those who experience barriers to exercise participation. Recent developments in electrical muscle stimulation technology could provide a potential solution. In this pilot case-control study we investigated the effects of a self-directed home based programme of electrical muscle stimulation training on muscle strength and exercise tolerance in a group of 16 healthy elderly volunteers (10f, 6m). Study participants completed 30 separate 1-hour electrical muscle stimulation sessions at home over a 6-week period. We observed significant improvements in quadriceps muscle strength and 6-minute walk distance, suggesting that this form of electrical muscle stimulation training has promise as an exercise modality in the elderly population.

The purpose of this study is to investigate how datafrom a foot mounted accelerometer can be used to detect motorpattern healthy subjects performed walking trails under twodifferent conditions; normal and stiff ankle walking. Lowerbody kinematic data were collected as well as accelerometerdata from both feet. An algorithm is presented which quantifiesrelevant swing phase characteristics from the footaccelerometer. Peak total acceleration during initial swing wassignificantly higher in the stiff ankle condition (M = 33.10, SD =5.12) than in the normal walking condition (M = 29.47, SD =5.75; t(7) = 4.32, p = .003, two-tailed). There was a large effectsize (eta squared = 0.853). Time between peak accelerationduring initial swing to foot strike was significantly shorter inthe stiff ankle condition (M = 0.42, SD = 0.02) than in thenormal condition (M = 0.44, SD = 0.03; t(7) = -2.54, p = .039,two- tailed). There was a large effect size (eta squared = 0.693).Simple to process metrics from tri-axial accelerometer data onthe foot show potential to detect changes in ankle kinematicpatterns.

Case-based reasoning (CBR) attempts to reuse past experiences to solve new problems. CBR ideas are commonplace in recommendation systems, which rely on the similarity between product queries and a case base of product cases. But, the relationship between CBR and many of these recommenders can be tenuous: the idea that product cases made up of static meta-data type features are experiential is a stretch; unless one views the type of case descriptions used by collaborative filtering (user ratings across products) as experiential. Here we explore and evaluate how to automatically generate product cases from user-generated reviews to produce cases that are based on genuine user experiences for use in a case-based product recommendation system.

In this paper we describe a novel approach to case-based product recommendation. It is novel because it does not leverage the usual static, feature-based, purely similarity-driven approaches of traditional case-based recommenders. Instead we harness experiential cases, which are automatically mined from user generated reviews, and we use these as the basis for a form of recommendation that emphasises similarity and sentiment. We test our approach in a realistic product recommendation setting by using live-product data and user reviews.

Supplementing product information with user-generated content such as ratings and reviews can help to convert browsers into buyers. As a result this type of content is now front and centre for many major e-commerce sites such as Amazon. We believe that this type of content can provide a rich source of valuable information that is useful for a variety of purposes. In this work we are interested in harnessing past reviews to support the writing of new useful reviews, especially for novice contributors. We describe how automatic topic extraction and sentiment analysis can be used to mine valuable information from user-generated reviews, to make useful suggestions to users at review writing time about features that they may wish to cover in their own reviews. We describe the results of a live-user trial to show how the resulting system is capable of delivering high quality reviews that are comparable to the best that sites like Amazon have to offer in terms of information content and helpfulness.

Why are some events more surprising than others? We propose that events that are more difficult to explain are those that are more surprising. The two experiments reported here test the impact of different event outcomes (Outcome-Type) and task demands (Task) on ratings of surprise for simple story scenarios. For the Outcome-Type variable, participants saw outcomes that were either knownor less-knownsurprising outcomes for each scenario. For the Task variable, participants either answered comprehension questions or provided an explanation of the outcome. Outcome-Type reliably affected surprise judgments; known outcomes were rated as less surprising than less-known outcomes. Task also reliably affected surprise judgments; when people provided an explanation it lowered surprise judgments relative to simply answering comprehension questions. Both experiments thus provide evidence on this less-explored explanation aspect of surprise, specifically showing that ease of explanation is a key factor in determining the level of surprise experienced

What are the 'cognitive after-effects' of making a similarity judgement? What, cognitively, is left behind and what effect might these residues have on subsequent processing? In this paper, we probe for such after-effects using a visual searcht ask, performed after a task in which pictures of real-world objects were compared. So, target objects were first presented in a comparison task (e.g., rate the similarity of this object to another) thus, presumably, modifying some of their features before asking people to visually search for the same object in complex scenes (with distractors and camouflaged backgrounds). As visual search is known to be influenced by the features of target objects, then any after effects of the comparison task should be revealed insubsequent visual searches. Results showed that when people previously rated an object as being high on a scale(e.g., colour similarity or general similarity) then visual search is inhibited (slower RTs and more saccades in eye tracking)relative to an object being rated as low in the same scale. There was also some evidence that different comparison tasks (e.g., compare on colour or compare on general similarity) have differential effects on visual search.

In this article we demonstrate how algorithmic probability theory is applied to situations that involve uncertainty. When people are unsure of their model of reality, then the outcome they observe will cause them to update their beliefs. We argue that classical probability cannot be applied in such cases, and that subjective probability must instead be used. In Experiment 1 we show that, when judging the probability of lottery number sequences, people apply subjective rather than classical probability. In Experiment 2 we examine the conjunction fallacy and demonstrate that the materials used by Tverksy and Kahneman(1983) involve model uncertainty. We then provide a formal mathematical proof that, for every uncertain model, there exists a conjunction of outcomes which is more subjectively probable than either of its constituents in isolation.

This study utilised the OpenSim platform to develop an EMG driven model of the lumbar spine by expanding an existing model and incorporating a plugin to represent intervertebral stiffness. Subject-specific kinematic data and surface EMG activity were recorded from 4 subjects during flexion and extension, lateral bending, and axial rotation. The model was used to predict muscle excitation patterns necessary to produce the recorded motions, and the patterns were compared with the recorded EMG data. The model was then driven with the recorded EMG data, and new excitation patterns were calculated for the deep muscles for which EMG data was not available. Simulations were conducted for intervertebral lumbar stiffness corresponding to preloading of 0N, 250N and 500N. The model-predicted excitation patterns were most comparable to recorded EMG data for the flexion and extension motions. Excitation levels predicted for all motions were sensitive to the applied preload. Although activation patterns remained similar, there was a substantial variation in model-predicted muscle excitation levels with change in intervertebral stiffness.

Automatically identifying informative reviews is increasingly important given the rapid growth of user generated reviews on sites like Amazon and TripAdvisor. In this paper, we describe and evaluate techniques for identifying and recommending helpful product reviews using a combination of review features, including topical and sentiment information, mined from a review corpus.

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.

Empirical social networks are often aggregate proxies for several heterogeneous relations. In online social networks, for instance, interactions related to friendship, kinship, business, interests, and other relationships may all be represented as catchall 'friendships.' Because several relations are mingled into one, the resulting networks exhibit relatively high and uniform density. As a consequence, the variation in positional differences and local cohesion may be too small for reliable analysis. We introduce a method to identify the essential relationships in networks representing social interactions. Our method is based on a novel concept of triadic cohesion that is motivated by Simmel's concept of membership in social groups. We demonstrate that our Simmelian backbones are capable of extracting structure from Facebook interaction networks that makes them easy to visualize and analyze. Since all computations are local, the method can be restricted to partial networks such as ego networks, and scales to big data.