Creating Rich and Representative Personas by Discovering Affordances

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIEEE During the last decade, information system designers have used the persona technique to put user needs and preferences at the center of all development decisions. Persona development teams draw on qualitative data, quantitative data or a combination of both to develop personas that are representative of the target users. Despite the benefits of both approaches, qualitative methods are limited by the cognitive capabilities of the experts, whereas quantitative methods lack contextual richness. To gain the advantages of both approaches, this article suggests a mixed qualitative-quantitative approach to create user personas based on the patterns of the affordances they actualize rather than merely the actions they take. It enriches personas by referring to the purposes fulfilled through affordance actualizations, and it grounds personas in readily available objective log data. This study illustrates the practical value of the proposed methodology by empirically creating personas based on real user data. Furthermore, it demonstrates its value by having practitioners compare the suggested method to that of qualitative-only and quantitative-only methods.Concordia Universit

Fuzzy clustering of time series data: A particle swarm optimization approach

With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because of different applications, the problem of clustering the time series data has become highly popular and many algorithms have been proposed in this field. Recently Swarm Intelligence (SI) as a family of nature inspired algorithms has gained huge popularity in the field of pattern recognition and clustering. In this paper, a technique for clustering time series data using a particle swarm optimization (PSO) approach has been proposed, and Pearson Correlation Coefficient as one of the most commonly-used distance measures for time series is considered. The proposed technique is able to find (near) optimal cluster centers during the clustering process. To reduce the dimensionality of the search space and improve the performance of the proposed method, a singular value decomposition (SVD) representation of cluster centers is considered. Experimental results over three popular data sets indicate the superiority of the proposed technique in comparing with fuzzy C-means and fuzzy K-medoids clustering techniques

Optimized location-allocation of earthquake relief centers using PSO and ACO, complemented by GIS, clustering, and TOPSIS

© 2018 by the authors. After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO

Investigation of the effects of constant darkness and light on blood serum cholesterol, insulin and glucose levels in healthy male rats

This study was designed to investigate the effects of constant darkness and light on changes of serum cholesterol, insulin and glucose levels in healthy male rats. In this study, healthy male rats (n = 30) were divided into 3 groups of tens and kept at various light/dark conditions: Control 12:12 light/dark (LD); constant darkness (DD), and constant light (LL) groups for 2 weeks. Blood samples were obtained from retro-orbital sinus before start of experiment and on the 7th and 14th days of the experimental period. The serum cholesterol and glucose levels were measured by the enzymatic method and insulin levels were measured using insulin kit by enzyme-linked immunosorbent assay (ELISA) method. The results of the study showed that the levels of serum cholesterol and glucose on the 7th and 14th days of the experimental period in DD group significantly decreased compared to the LD and LL groups (p < 0.05). On the 14th day of experiment, we observed significant decrease of serum insulin level in the constant darkness group compared with the two other groups (p < 0.05). The study showed that on the 7th and 14th days of experiment, constant light significantly increased serum glucose level without having any significant effects on serum cholesterol and insulin levels. Also, the long period of time (14 days) was found to be more effective in the serum of these metabolic parameters changes than the short period (7 days).Key words: Constant darkness, light, cholesterol, glucose, insulin, healthy male rats

Impact of loganin on pro-inflammatory cytokines and depression- and anxiety-like behaviors in male diabetic rats

Behavioral disturbances are observed in most patients suffering from diabetes. According to some evidence, pro-inflammatory cytokines have a key role both in diabetes and behavioral disorders, such as anxiety and depression. In this study, the effect of chronic administration of loganin, as a bioflavonoid, was investigated on pro-inflammatory cytokines and depression- and anxiety-like behaviors in streptozotocin-induced diabetes in male Wistar rats. Blood levels of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) were assessed by enzyme-linked immunosorbent assay method. Depression- and anxiety-like behaviors were evaluated by forced swimming test (FST), elevated plus maze (EPM), and open field test (OFT), respectively. Body weight was also measured before the interventions and after the experiments in all groups. Our findings show that loganin-treated animals had significantly lower serum concentrations of IL-6 and TNF-α compared with the diabetic group. In the EPM test, loganin treatment significantly increased the percentage of the open arm time and open arm entries. Moreover, loganin treatment significantly decreased the grooming time and restored distance traveled and center crossing in the OFT. However, it decreased immobility time in the FST. Loganin treatment also significantly restored body weight gain and attenuated blood glucose changes in the diabetic rats. These results indicate that loganin possibly alleviates depression- and anxiety-like behaviors associated with diabetes through lowering the blood glucose and pro-inflammatory cytokine levels. More research is required to show the exact mechanism of antidepressant and anxiolytic effects of loganin in diabetes

Accommodating practices during episodes of disillusionment with mobile IT

This study investigates how tablet users react when technology falls short of their expectations. We deploy a data/frame model to study this process and investigate resistance-related reactions and the deployment of accommodating practices at the individual level. Analyzing user blogs that provide narratives on user interaction with tablets, we identify triggers of episodes of disillusionment and illustrate five sensemaking paths that users follow, eventually leading to one of three practices: 1) users choose to defer tasks until the situation changes, or they abandon the platform altogether; 2) they develop workarounds at different levels of proficiency; or 3) they proceed by reframing their expectations of the platform. By revealing user decision-making process during episodes of disillusionment, the findings contribute to information systems post-adoption research. At a practical level, the findings inform IT artifact and application design by offering insights on how users process discrepancies between their expectations and actual use experience