The Ghana National Service Scheme: Perceptions of former educational personnel, students and guardians

The purpose of this inquiry was to investigate the perceptions of former service personnel, students and their parents about the organizational effectiveness of the Ghana National Service Scheme (GNSS). The inquiry addressed the following questions: How do the participants perceive the effectiveness of the national service program on the Ghanaian society? What are the perceptions of school administrators who worked with service personnel, parents and students vis-à-vis the over all impact of the GNSS on the educational system? What are the perceptions of former service personnel, students and their parents in regard to the impact of the GNSS on them? ^ The GNSS is a part within the ministry of education, which is also part in the Ghanaian social structure. Hence, a systems theory approach which asks, “How and why a system as a whole functions as it does” (Patton, 1990), was utilized in the study. Methodologies included purposive sampling; interviews; participant observation, and follow-up interviews. The study was conducted over a six-moth period. ^ A cross-sectional survey design was used to generate data. The survey was followed up with an ethnographic study where in-depth, face-to-face interviews were conducted together with observations. The results were described and interpreted. ^ The summary of findings concludes that perceptional determinants of the effectiveness of the GNSS were biased by the age and zone of origin but not gender. This partially agrees with Marenin's (1990) except for gender. A significant difference was detected between the responses of those who were officials of the National service Secretariat and of former service personnel. The administrators defended the status quo and demonstrated their deeper knowledge about the scheme. The former personnel and parents freely criticized the program when necessary and did not seem to know much about the GNSS. Respondents mostly stressed the need for the secretariat to focus on the following areas: (1) involvement in the agricultural sector of the economy, (2) involvement in rural mass, civic and health education, (3) adequacy of remuneration and personnel welfare, and (4) ensuring posting of personnel to areas of their expertise. Recommendation for further research concluded the study. ^

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Novel online data cleaning protocols for data streams in trajectory, Wireless Sensor Networks

The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or "dirty" sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.

Personnel selection, safety performance, and job performance: Are safe workers better workers?

The present dissertation consists of two studies that combine personnel selection, safety performance, and job performance literatures to answer an important question: are safe workers better workers? Study 1 tested a predictive model of safety performance to examine personality characteristics (conscientiousness and agreeableness), and two novel behavioral constructs (safety orientation and safety judgment) as predictors of safety performance in a sample of forklift loaders/operators (N = 307). Analyses centered on investigating safety orientation as a proximal predictor and determinant of safety performance. Study 2 replicated Study 1 and explored the relationship between safety performance and job performance by testing an integrative model in a sample of machine operators and construction crewmembers (N = 323). Both Study 1 and Study 2 found conscientiousness, agreeableness, and safety orientation to be good predictors of safety performance. While both personality and safety orientation were positively related to safety performance, safety orientation proved to be a more proximal determinant of safety performance. Across studies, results surrounding safety judgment as a predictor of safety performance were inconclusive, suggesting possible issues with measurement of the construct. Study 2 found a strong relationship between safety performance and job performance. In addition, safety performance served as a mediator between predictors (conscientiousness, agreeableness and safety orientation) and job performance. Together these findings suggest that safe workers are indeed better workers, challenging previous viewpoints to the contrary. Further, results implicate the viability of personnel selection as means of promoting safety in organizations.^

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Personnel director interviewing job applicant

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Personnel manager interviews job applicant

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Personnel director

Inscriptions: Verso: [stamped] Credit must be given to Leinwand from Monkmeyer Press Photo Service]

Personnel director talking on telephone

Inscriptions: Verso: [stamped] Credit must be given to Leinwand from Monkmeyer Press Photo Service]

Woman cleaning apartment window

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Woman cleaning apartment window

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Personnel director interviewing job applicant

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Women at work: personnel director interviewing applicant.

Inscription: Verso: Women at work: miscellaneous occupations. Gail Lewis, personnel directior interviewing applicant, Hertz Corp.

Le style de gestion du personnel comme déterminant des comportements antisociaux observés au travail

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.