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The computational power requirements of real-world optimization problems begin to exceed the general performance of the Central Processing Unit (CPU). The modeling of such problems is in constant evolution and requires more computational power. Solving them is expensive in computation time and even metaheuristics, well known for their eficiency, begin to be unsuitable for the increasing amount of data. Recently, thanks to the advent of languages such as CUDA, the development of parallel metaheuristics on Graphic Processing Unit (GPU) platform to solve combinatorial problems such as the Quadratic Assignment Problem (QAP) has received a growing interest. It is one of the most studied NP-hard problems and it is known for its high computational cost. In this paper, we survey several of the most important metaheuristics approaches for the QAP and we focus our survey on parallel metaheuristics using the GPU.

The study presented in this paper highlights an important issue that was subject for discussions and research about a decade ago and now have gained new interest with the current advances of grid computing and desktop grids. New techniques are being invented on how to utilize desktop computers for computational tasks but no other study, to our knowledge, has explored the availability of the said resources. The general assumption has been that there are resources and that they are available. The study is based on a survey on the availability of resources in an ordinary office environment. The aim of the study was to determine if there are truly usable under-utilized networked desktop computers available for non-desktop tasks during the off-hours. We found that in more than 96% of the cases the computers in the current investigation was available for the formation of part-time (night and weekend) computer clusters. Finally we compare the performance of a full time and a metamorphosic cluster, based on one hypothetical linear scalable application and a real world welding simulation.

This paper reviews recent state-of-the-art H.264 sub-pixel motion estimation (SME) algorithms and architectures. First, H.264 SME is analyzed and the impact of its functionalities on coding performance is investigated. Then, design space of SME algorithms is explored representing design problems, approaches, and recent advanced algorithms. Besides, design challenges and strategies of SME hardware architectures are discussed and promising architectures are surveyed. Further perspectives and future prospects are also presented to highlight emerging trends and outlook of SME designs.

A person’s preference to select or reject certain meals is influenced by several aspects, including colour. In this paper, we study the relevance of food colour for such preferences. To this end, a set of images of meals is processed by an automatic method that associates mood adjectives that capture such meal preferences. These adjectives are obtained by analyzing the colour palettes in the image, using a method based in Kobayashi’s model of harmonic colour combinations. The paper also validates that the colour palettes calculated for each image are harmonic by developing a rating model to predict how much a user would like the colour palettes obtained. This rating is computed using a regression model based on the COLOURlovers dataset implemented to learn users’ preferences. Finally, the adjectives associated automatically with images of dishes are validated by a survey which was responded by 178 people and demonstrates that the labels are adequate. The results obtained in this paper have applications in tourism marketing, to help in the design of marketing multimedia material, especially for promoting restaurants and gastronomic destinations.

This paper presents empirical research aimed at studying what characterizes successful information technology (IT) projects. There are often doubts about what characterizes project success and who actually defines it. In this paper, we have reviewed the literature and present significant contributions to the discussion of what characterizes successful IT projects. Furthermore, a survey was conducted in Norway to collect data on successful IT projects. Research results show that the five most important success criteria are: (1) the IT system works as expected and solves the problems, (2) satisfied users, (3) the IT system has high reliability, (4) the solution contributes to improved efficiency and competitive power, and (5) the IT system realizes strategic, tactical and operational objectives.

IT business value research examines the organizational performance impacts of information technology. In this paper, we apply the value configuration of the value shop to describe and measure organizational performance. The value shop consists of the five primary activities of problem understanding, solutions to problems, decisions on actions, implementation of actions, and evaluations of actions in an iterative problem-solving cycle. Police investigation work is defined as value shop activities. Our empirical study of Norwegian police results in significant relationships between information technology use and investigation performance for all primary activities. The most important primary activities for IT use are problem understanding and implementation of actions, as both significantly improve value shop performance.

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.

Technology-enabled feedback encompasses many significant features including AI. It also allows students greater flexibility in providing feedback, and the university/institute enables them to make data-driven decisions as the collected data are free from bias and data analyses are accurate. There are different technology-centric feedback collections such as Open edX Insight, speech-to-text, Kahoot, Dialogflow, and so on. These technology-centric feedback collections mean collecting more realistic data from the students. Both qualitative and quantitative data could be analyzed by applying technology-centric data analyses tools. AMOS, LABLEAU, SAS, SPSS, Stata, Statista, XLSTAT, and ROOT are a few of the quantitative data analyses software, and Atlas, Airtable, Coda, Condens, MaxQDA, Notion, and NVivo are a few of the qualitative data analyses software. To make the students’ feedback and survey more successful we recommend sharing survey methods/survey questions with other educational institutions, conducting broader surveys, broad topics to ask questions on, technology-based analysis of qualitative feedback comments, and including skills development-focused questions.

This paper presents partial results of a research developed during the year 2002 to 2006. Population analyzed was Science and Technology teachers of Spanish universities. The investigation has worked with sample academic users, distributed among 19 Spanish universities. The main contribution of this study is to present a BIQUAL tool. It is useful for the evaluation of the quality services in university libraries and especially of Science and Technology. This tool is created using the user's point of view. Results identify behaviour of these users and the aspects that concern the quality of the service in this environment. We also discuss about some problems and difficult experimented in this research. We analyzed the use of quantitative methods, in special, survey as well as it effectiveness to library quality management.

Public Libraries can surely play a significant social, cultural and economic role. Improvements on quality however are a necessary prerequisite. On the other hand, quality is a complex and subjective concept, which should incorporate at any given time the true (expressed and implied) needs of all interested parties. This paper investigates and empirically assesses the current perceptions for quality in Greek public libraries in order to suggest a way forward for quality management implementation. For that purpose a survey based on semi-structured interviews with the directors of Greek public libraries has been constructed and the results are presented.

In order to more fully understand the cultural shifts in technology within and between generations, a sample survey of several public libraries in a rural university community will be analyzed. The following subjects will be addressed and compared on a generational level within this poster presentation: programming, technology—types and usage, and frequency and purpose of visits. A thorough analysis of the data in this case study will provide insight into the changing role of technology use among two distinct generations of library patrons.

The paper reports a project conducted within the framework of the National Library of Sweden's Expert Group on Library Statistics. The project is financed by the National Library of Sweden, and it aims to capture how library managements make use of the current library statistics (based on the ISO-standard) in the decision-making process. Questions are also asked about the quality and usefulness of the existing statistical data, whether data covers all relevant aspects of library activity, and what additional data is required in order to enhance the validity of collected statistics.

The primary method used is semi-structured group interviews (focus groups). Four such interviews were held with the participation of 15 library directors from academic and other research or special libraries from different parts of Sweden. The results show that there is considerable ambivalence among the informants concerning the usefulness of current statistics. On the one hand there is severe criticism of what today is reported on a national level (too much and too little), on the other hand statistical data is used locally to underpin arguments supporting library activities.

Stakeholders representing university or institutional management were not available for group interviews. With the support of the interviewed library directors a group of 12 vice-chancellors or other senior managers were identified and approached with a mail survey. Questions were asked about what kind of documentation of library activities they use or prefer, be they quantitative, qualitative or of other kind.