The Transition Process in Office Automation and Its Impact on Clerical Workers: A Case Study

The purpose of this research was to study the transition process of office automation and its impact on clerical workers. The objective was to provide computer-naive managers with recommendations on factors to consider when managing the office automation change process. The topic was investigated using a case study approach. The setting was a large, multi-function, research-oriented, urban university on the west coast. Data were gathered through interviews, observations and examination of documents. Twenty-five clerical workers (representing different segments of the campus and having experience using different types of computer-assisted office equipment), who had experienced the transition process of office automation, were interviewed in depth. Ten of those subjects were additionally interviewed in group settings. Administrative personnel who had responsibility for managing computing resources were interviewed for background data. Information was gathered from the subjects concerning their experiences with and perceptions of the automation change process, and the impact of automation on their jobs. The data were analyzed by the following categories: (a) factors affecting the transition process, (b) factors impacting on efficient use of computer-assisted equipment, (c) job changes resulting from office automation, and (d) factors associated with the use of different types of computer-assisted equipment. Key findings were that (a) the prospect of office automation can be anxiety-producing for potential users, (b) most users did not receive adequate training, (c) lack of training may result in underutilization of computer-assisted equipment, (d) there was no indication that automation diminished communication among users, and (e) most of the subjects reported high job satisfaction after automation. It was the researcher\u27s conclusion that the significant issue of office automation is how the automation change process is managed, not the automation per se. It is recommended that managers include users in office automation decision-making in order to minimize problems associated with user anxiety, training, job design and efficient use of the equipment

Enhanced feature mining and classifier models to predict customer churn for an e-retailer

Customer Churn, an event indicating a customer abandoning an established relation with a business is an important problem researched well both in academic and commercial interest. Through this work, we propose an improved prediction model that emphasizes on an effective data collection pipeline through varied channels capturing explicit and implicit customer footprints. Our goal is to demonstrate how Feature selection algorithms can improve classifier efficiency. We also rank prominent features which play a vital role in customer churn. Our contributions through this paper can be broadly categorized into 3 folds: First, we show how popular data mining tools in Hadoop stack help extract several implicit customer interaction metrics including Sales and Clickstream logs generated as a result of customer interaction. Second, through Feature Engineering techniques we verify that some of the new features we propose have a definite impact on customer churn. Finally, we establish how Regularized Logistic Regression, SVM and Gradient Boost Random Forests are the best performing models for predicting customer churn verified through comprehensive cross-validation techniques

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Human Factors Considerations in System Design

Human factors considerations in systems design was examined. Human factors in automated command and control, in the efficiency of the human computer interface and system effectiveness are outlined. The following topics are discussed: human factors aspects of control room design; design of interactive systems; human computer dialogue, interaction tasks and techniques; guidelines on ergonomic aspects of control rooms and highly automated environments; system engineering for control by humans; conceptual models of information processing; information display and interaction in real time environments

Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

The man/machine interface in information retrieval: Providing access to the casual user

This study is concerned with the difficulties encountered by casual users wishing to employ Information Storage and Retrieval Systems. A casual user is defined as a professional who has neither time nor desire to pursue in depth the study of the numerous and varied retrieval systems. His needs for on-line search are only occasional, and not limited to any particular system. The paper takes a close look at the state of the art of research concerned with aiding casual users of Information Storage and Retrieval Systems. Current experiments such as LEXIS, CONIT, IIDA, CITE, and CCL are presented and discussed. Comments and proposals are offered, specifically in the areas of training, learning and cost as experienced by the casual user. An extensive bibliography of recent works on the subject follows the text