Browsing by Subject "Training"
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Item 2016 Federal Employee Orientation Programs Best Practices Research Project(2016-10-19) Blankenship, Diane; Hart, Gaia; Foreign Service Institute (U.S.)New employee orientation is rapidly becoming a focus of organizational success within the literature as noted by LinkedIn efforts, a noted leader in employee engagement, and recommendations for training processes in various industries from Federal government, private industry, and the medical community. The evaluation and revision process is an ongoing dynamic process which continues to gather Level 1, 2, and 3 data from participants, analysis and interpretation of data, examination and trend analysis, and research to identify instructional techniques and resources to enhance the learning experience. The FSI Civil Service Orientation Coordinator collaborated with FSI’s first-ever Virtual Fellow as part of the Department’s new program incorporating citizen involvement to conduct research within the Federal government to identify the best practices and industry standards of orientation programs. The research efforts of this project synthesized previous 2015 research in workforce training and this 2016 orientation study, current research, literature review, trends, and the Kirkpatrick Model principles of training and evaluation. This report presents the information in a standard format for each section: literature information, survey results, best practices, and industry standards, after reviewing 30 Federal agencies’ survey responses and many course curriculum agendas. Attempts at gaining insight from Federal leaders cited in the Best Places to Work in the Federal Government survey went unanswered, thus the Federal agencies cited in Best Places survey were not necessarily represented in this report. Responses were from Federal agencies that were members of the OPM Training and Development ListServe. In general, Federal government agencies seem to be lagging behind leading commercial entities and other literature citing best practices and standards in the field of curriculum design and evaluation of workforce orientation training programs. Benchmarking against enlightened leaders in the commercial arena such as Zappos, LinkedIn, and Google would yield more extensive learning to gain expanded insights into designing creative, engaging, cutting-edge, new employee orientation programs.Item Evaluating a Workshop for Non-Traditional College Students Experiencing Career Transitions(American Counceling Association, 2010) Gasser, Courtney E.The world of work has changed dramatically in the past few decades (Arnold & Jackson, 1997; Borgen, 1999; Maglio, Butterfield, & Borgen, 2005). Gone are the days when one would find a satisfying job and keep that same job until retirement. Now, it is far more common to expect that one will be faced with several, if not many, job and career transitions over the course of one’s work life (Arnold & Jackson, 1997). Furthermore, successfully completing a career transition may often require additional training and skills, with the implication for some to attend college to seek degrees. In fact, it is likely that with the current global economic issues, and with larger percentages of unemployed Americans, that some individuals will be looking to change career paths to pursue existing employment opportunities that may be perceived to lead to steadier paychecks. Given these employment trends, it is not a stretch to imagine more individuals seeking out collegiate experiences in order to make these occupational shifts possible. It appears that we have entered into what could be called the age of the “career transitioner.” Career transitioners can be defined as those working adults who are makinga change in their choice of work (Fouad & Bynner, 2008). These transitioners may have experienced either voluntary or involuntary job loss, and may be retraining in preparation to enter another career. Given the trends of our changing world of work, it seems likely that there will be an increasing number of career transitioners in need of counseling interventions tailored to their unique needs. To date, few studies have focused on evaluating career interventions with mature adult workers (Bobek & Robbins, 2005). This study seeks to expand on what is known about effective career interventions for mature adult workers who are experiencing career transitions.Item Expanding Informal Maker-Based Learning Programs for Urban Youth(IEEE, 2020-12-04) Hamidi, Foad; Freeland, Amy; Grimes, Shawn; Grimes, Stephanie; Moulton, Adena; Coy, AndrewThis Research Full Paper contributes to research on creating maker-based learning experiences for youth in diverse informal learning settings. A key research question in this space is how to efficiently and effectively setup maker learning spaces and train educators to deliver high-quality maker curriculum in diverse sites. To study this question, we developed and deployed a multi-phase maker educator training program that included makerspace setup, educator training, and youth program deployment. We deployed three models of the program at three participating sites over roughly nine months. We analyzed data from educator pre- and post- interviews and found that the programs generated considerable interest in the youth and resulted in positive shifts in career aspirations and social and technical skills. Participants emphasized the importance of creating hybrid online and offline resources and training materials. Our participants also identified logistical challenges related to recruiting educators and youth attendance. Finally, participants described possibilities for content localization and the inclusion of participatory approaches to keep youth and educators engaged.Item GENPass: A Multi-Source Deep Learning Model For Password Guessing(IEEE, 2019-09-11) Xia, Zhiyang; Yi, Ping; Liu, Yunyu; Jiang, Bo; Wang, Wei; Zhu, TingThe password has become today’s dominant method of authentication. While brute-force attack methods such as HashCat and John the Ripper have proven unpractical, the research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic contextfree grammar (PCFG) are all based on statistical probability. These approaches require a large amount of calculation, which is time-consuming. Neural networks have proven more accurate and practical in password guessing than traditional methods. However, a raw neural network model is not qualified for crosssite attacks because each dataset has its own features. Our work aims to generalize those leaked passwords and improves the performance in cross-site attacks. In this paper, we propose GENPass, a multi-source deep learning model for generating “general” password. GENPass learns from several datasets and ensures the output wordlist can maintain high accuracy for different datasets using adversarial generation. The password generator of GENPass is PCFG+LSTM (PL). We are the first to combine a neural network with PCFG. Compared with Long short-term memory (LSTM), PL increases the matching rate by 16%-30% in cross-site tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The results demonstrate that the matching rate of GENPass is 20% higher than by simply mixing datasets in the cross-site test. Furthermore, we propose GENPass with probability (GENPass-pro), the updated version of GENPass, which can further increase the matching rate of GENPass.Item The Effect of the Perception of Access to Training and Development Opportunities, on Rates of Work Engagement and Turnover Intent, Among Federal Employees in the United States(2019-01-01) Hassett, Michael Patrick; Edwards, Lauren H; School of Public Policy; Public PolicyWork engagement is characterized by feelings of vigor, dedication, and absorption. Those who have high rates of work engagement tend to have higher rates of job satisfaction, motivation, and job performance and lower rates of turnover intention. Studies examining work engagement have produced results that show that organizational and managerial characteristics can promote work engagement among employees. Two such theories include High Performance Work Systems (HPWS) and the Job Demands-Resource (JD-R) theory. HPWS theorists posit that through the adoption of specific practices, organizations can cultivate employees who are more motivated, committed, and armed with more skills and competencies. Despite the evidence between HPWS and positive outcomes, questions still exist as to how these work systems alter the behavior and attitudes of employees. The JD-R model, examines the characteristics of a given work environment and divides these characteristics between demands and resources. Burnout and engagement are considered mediators between job demands and job resources (antecedents) and outcomes (both negative and positive). A combination of both frameworks is used to examine the relationship between the perception of access to training and development opportunities, as a high performance work practice and job resource, on rates of work engagement and turnover intent, in the federal workforce. Moreover, this dissertations explores to what extent work engagement mediates the perception of access to training and development opportunities on employees' turnover intention. This dissertations uses the federal Employee Viewpoint Survey (FEVS). I use a combination of Ordinary Least Squares analyses to test my hypotheses across the entire federal workforce. I also analyze whether differences exist between agencies with different sizes and different typologies. My analyses supports all of my hypotheses: 1) my IV is positively related to my MV; 2) negatively related to my DV; 3) work engagement mediates the relationship between my IV and DV; 4) differences exist between agencies based on size and typology. This study corroborates the claim that the perceptions of employees influence behavior. Moreover, in an era of increasing budget cuts and efforts to reduce the federal workforce, training may be a viable way to increase work engagement and retain effective employees.Item Unseen Activity Recognitions: A Hierarchical Active Transfer Learning Approach(IEEE, 2017-07-17) Alam, Mohammad Arif Ul; Roy, NirmalyaHuman activity recognition (AR) is an essential element for user-centric and context-aware applications. While previous studies showed promising results using various machine learning algorithms, most of them can only recognize the activities that were previously seen in the training data. We investigate the challenges of improving the recognition of unseen daily activities in smart home environment, by better exploiting the hierarchical taxonomy of complex daily activities. We first (a) design a hierarchical representation of complex activity taxonomy in terms of human-readable semantic attributes, and (b) develop a hierarchy of classifiers which incorporates a cluster tree built on the domain knowledge from training samples. Though this model is rich in recognizing complex activities that are previously seen in training data, it is not well versed to recognize unseen complex activities without new training samples. To tackle this challenge, we extend Hierarchical Active Transfer Learning (HATL) approach that exploits semantic attribute cluster structure of complex activities shared between seen (source) and unseen (target) activity domains. Our approach employs transfer and active learning to help label target domain unlabeled data by spawning the most effective queries. We evaluated our approach with two real-time smart home systems (IRB #HP-00064387) which corroborates radical improvements in recognizing unseen complex activities.Item UnTran: Recognizing Unseen Activities with Unlabeled data using Transfer Learning(IEEE, 2018) Khan, Md Abdullah Al Hafiz; Roy, NirmalyaThe success and impact of activity recognition algorithms largely depends on the availability of the labeled training samples and adaptability of activity recognition models across various domains. In a new environment, the pre-trained activity recognition models face challenges in presence of sensing bias- ness, device heterogeneities, and inherent variabilities in human behaviors and activities. Activity Recognition (AR) system built in one environment does not scale well in another environment, if it has to learn new activities and the annotated activity samples are scarce. Indeed building a new activity recognition model and training the model with large annotated samples often help overcome this challenging problem. However, collecting annotated samples is cost-sensitive and learning activity model at wild is computationally expensive. In this work, we propose an activity recognition framework, UnTran that utilizes source domains' pre-trained autoencoder enabled activity model that transfers two layers of this network to generate a common feature space for both source and target domain activities. We postulate a hybrid AR framework that helps fuse the decisions from a trained model in source domain and two activity models (raw and deep-feature based activity model) in target domain reducing the demand of annotated activity samples to help recognize unseen activities. We evaluated our framework with three real-world data traces consisting of 41 users and 26 activities in total. Our proposed UnTran AR framework achieves ≈ 75% F1 score in recognizing unseen new activities using only 10% labeled activity data in the target domain. UnTran attains ≈ 98% F1 score while recognizing seen activities in presence of only 2-3% of labeled activity samples.Item Zero-Day Attack Identification in Streaming Data Using Semantics and Spark(IEEE, 2017-09-11) Pallaprolu, Sai C.; Sankineni, Rishi; Thevar, Muthukumar; Karabatis, George; Wang, JianwuIntrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.