Miracle-dz/UniFAR-FTUs huggingface dataset:
Information data fusion and computer network defense

INTRODUCTION Historically, an enormous amount of research and development on information fusion has been conducted in support of military operations (e.g., fusion of multi-sensor data for target tracking, identification, and threat assessment and situation awareness) (Hall and McMullen, 2004) (Liggins et al., 2008). The research has included development of process models, creation of algorithms for signal and image processing, pattern recognition , state estimation, automated reasoning, and dynamic resource allocation. ABSTRACT Computer networks no longer simply enable military and civilian operations, but have become vital infrastructures for all types of operations ranging from sensing and command/control to logistics, power distribution, and many other functions. Consequently, network attacks have become weapons of choice for adversaries engaged in asymmetric warfare. Traditionally, data and information fusion techniques were developed to improve situational awareness and threat assessment by combining data from diverse sources, and have recently been extended to include both physical (" hard ") sensors and human observers (acting as " soft " sensors). This chapter provides an introduction to traditional data fusion models and adapts them to the domain of cyber security. Recent advances in hard and soft information fusion are summarized and applied to the cyber security domain. Research on the use of sound for human-in-the-loop pattern recognition (sonification) is also introduced. Finally, perspectives are provided on the future for data fusion in cyber security research. This chapter adapts these models to the domain of cyber security. This is a field in which data fusion techniques and terminology are becoming increasingly relevant, given the complex tasks of maintaining overall awareness of a network's current status, projecting future actions of adversaries , and making timely adjustments. Following a discussion of hard and soft information fusion and their relevance to the cyber security domain, we propose a novel means of situational awareness involving an auditory representation (sonification) of network traffic. This chapter is organized as follows. We begin by exploring of some of the background research in three areas of interest – use of humans as soft sensors, data fusion technologies in cyber security , and sonification. The first content area of human-centric information fusion outlines the use of humans as " soft " sensors. The second content area presents the general data fusion framework (the JDL model) as it applies to the cyber security context. The third main content area, sonification, is construed as a cognitive refinement. The chapter concludes with a …
I Am Me: Adolescent Perspectives of a School-Based Universal Intervention Program Designed to Promote Emotional Competence.

The aim of this paper is to describe a school-based universal intervention (the Tilly’s Life Center “I Am Me” program) to promote emotional competence and socialemotional skills in adolescents. We present results of a mixed-methods program evaluation conducted in public schools in Southern California. In Study 1, sixteen students (grades 10-12) participated in focus groups after twelve weeks of intervention. In Study 2, fifty-four students (grades 9-10) participated in either the universal intervention program (I Am Me) or a no-intervention comparison group for thirty-six weeks. We conducted focus groups with 50 adolescents in the I Am Me program and used quantitative methods to measure self-esteem and perceived stress at four time points for both groups in Study 2. Descriptive data suggested improvements in selfesteem and perceived stress among individuals participating in the I Am Me group. Thematic analysis of focus group data indicated that participants perceived improvement in key areas of emotional development, including: emotional competence (understanding, expressing, and regulating emotions), self-regulation, self-esteem, and social skills. Moreover, participants perceived the intervention as relevant and as having a positive lifelong impact on their development. Further research is needed to empirically validate these outcomes using a randomized experimental design.
Face-to-face and remote teaching in a doctoral education course

This case study examines face-to-face and remote teaching in a doctoral education course and ask if flipped classroom, formative assessment and remote teaching increase the teaching quality of a literature review course. To be able to answer the research question, design-based research (DBRC, 2003) and case study (Yin, 2009) were used as a research-pedagogical design and methodological framework. The selection of informants is based on purposeful selection (Maxwell, 2005) where one group of PhD candidates (n = 24) situated at the University of Bergen and one group of PhD candidates (n = 12) situated at Volda University College, were selected. The study indicates that the PhD candidates enhanced their understanding of literature review throughout the PhD course, they appreciated the course design, and the quality of their academic papers and their survey feedback indicated that they had a good learning outcome from the course. Despite some methodological limitations, this study shows that advanced video conferencing systems in combination with a well-prepared teaching design have several positive outcomes and can be used as a starting point for a more large-scale study. The study shows, despite former ÅRGANG 42, NR. 2-2019, S. 194–214 ISSN ONLINE: 1893-8981 195 UNIPED | ÅRGANG 42 | NR. 2-2019 This article is downloaded from www.idunn. no. © 2019 Author(s). This is an open access article distributed under the terms of the Creative Commons CC-BY 4.0 License (https://creativecommons.org/licenses/by/4.0/). mixed experiences with remote teaching, that student participation via several campuses did not necessarily lead to poorer quality and poorer learning outcome for these PhD candidates in this case study. However, the case study also shows the need for more large-scale research which will give a more nuanced and thorough understanding of how we can increase the teaching quality of remote teaching as part of the training component in the PhD.
An Overview of Data Warehousing, Data mining, OLAP and OLTP Technologies

Data warehousing , Data Mining, OLAP, OLTP technologies are essential elements of decision support, which has increasingly become a focus of the database industry. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases.Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing ,Data Mining, OLAP, OLTP technologies with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse.
Empowerment and Mental Health of University Teachers: A Case from Pakistan

The present descriptive study investigates the relationship between university teachers ’empowerment and their mental health in Pakistan. The research is quantitative in nature. Survey method was applied by using a questionnaire having two subscales for data collection. The study targets the faculty members of 26 public and private universities in Lahore. Simple descriptive statistics, t-test for independent sample, and Pearson correlations were used for data analysis. The results indicate a significant association and direct correlation between both variables. It is seen that low empowerment affects the mental health of university teachers. They feel depress, restless, low confidence and a decline in their sense of well-being. It has also been revealed that the mental health of teachers in private sector is affected more adversely in comparison with the public sector teachers as a consequence of lack of empowerment. It is inferred that quality education depends on intellectual interactions between teachers and students. These learning communities are possible only when teachers are fully empowered which leads to their good mental health necessary for the purpose of learning.
Who Does Aid Help? Examining Heterogeneity in the Effect of Grant Aid on Achievement

Does grant aid impact achievement differently for low- and high-income students? I exploit the eligibility requirements of a state scholarship program that awards additional aid to merit-aid students majoring in STEM fields. A triple-difference design, using administrative data from a large institution and exploiting differences over time, by merit-aid recipient status, and by major type (STEM or non-STEM), shows that grant aid increases the GPAs and graduation prospects of low-income students but has little impact on high-income students. Additional analysis suggests that reduction in student part-time work among low-income students may be a potential mechanism for the heterogeneous achievement effects of grant aid by income. These results suggest that merit aid programs could be targeted more effectively than most currently are.
The Effects of Computer Graphic Organizers on the Narrative Writing of Elementary School Students with Specific Learning Disabilities

This study used a multiple baseline across subjects design to explore the effects of using a computer graphic organizer program during planning on the narrative writing compositions of four fourth- and fifth-grade boys with Specific Learning Disabilities (SLD). Four dependent variables were measured: total number of words, total minutes planning, number of common story elements, and overall organization. Baseline planning was done with paper and pencil, while intervention planning was done using the computer graphic organizer program Kidspiration 3.0 (2011). Compositions in both conditions were written and edited using paper and pencil. The results indicated that computer graphic organizers used during planning had a positive effect on three of four dependent measures. When looking at mean scores, participants wrote more words (from 20.25 to 41.86 more), planned for longer periods of time (from 1.00 to 5.00 more minutes), and included more story elements in their compositions (from 0.80 to 3.10 more out of a possible 6.00). There were nominal to no improvements in overall organization across the four participants. The results suggest that teachers of students with SLD should consider using computer graphic organizers for narrative writing planning. Future research should investigate the effects of computer graphic organizers with other writing genres, across multiple writing days per composition, with participants of varied demographics, and/or in combination with other remedial composition instruction.
A Comparison of Knowledge Acquisition Methods for the Elicitation of Procedural Mental Models

KAHLER, SUSAN ELIZABETH. A Comparison of Knowledge Acquisition Methods for the Elicitation of Procedural Mental Models. (Under the direction of Sharolyn A. Converse-Lane.) This study compared the effectiveness of two different knowledge acquisition methods in terms of their ability to measure the structure of a students procedural mental model of computer programming as it evolved in a classroom setting over the course of a semester. Ratings and backward thinking tasks were used to estimate the structure of students procedural mental models at several points throughout an introductory programming course. For both methods, comparisons between the students procedural mental model and a prototype model (i.e., the instructors conceptual model of the knowledge provided in the course) were used to identify how the procedural tasks and the relationships among tasks are represented in the students mental model. The values from the ratings task were input into the Pathfinder scaling algorithm, which compares the structural similarity between a prototype model and the students mental model, to generate an index of similarity. Additionally, the Pathfinder scaling algorithm measured the consistency of the students rating process via the coherence index. The steps recorded during the backward thinking task were compared to the prototype model to identify the number of steps out of sequence. The first hypothesis was that there would be a positive correlation between the index of similarity and the project score. However, this hypothesis was only partially supported, as the correlation between the index of similarity and project score was significant only at test time three. The second hypothesis was that as the student completed each successive project, the index of similarity would increase over the Procedural Mental Models course of the semester. This hypothesis was not supported because the index of similarity never increased beyond the value obtained at test time one. The third hypothesis was that as the student completed each successive project, the coherence index would increase. This hypothesis was not supported, as no main effect of test time on the coherence index was found. The fourth hypothesis was that there would be a negative correlation between the project score and the number of steps out of sequence. The number of steps out of sequence was excluded from further analysis due to low inter-rater reliability. Therefore this hypothesis cannot be evaluated. Lastly, it was hypothesized that the index of similarity would correlate more positively with student's performance than the number of steps out of sequence. Again, without the number of steps out of sequence, this hypothesis could not be fully evaluated. However, for project three, the index of similarity was significantly positively correlated with the project score. Procedural Mental Models A COMPARISON OF KNOWLEDGE ACQUISITION METHODS FOR THE ELICITATION OF PROCEDURAL MENTAL MODELS
Effect of Psychological Need-Supportive Summer Camp: FIT-Dawg Girls Summer Camp

Physical activity (PA) is a major factor related to obesity risk. Research has shown PA interventions among adolescents to be moderately successful in short-term but limited to longer-term. Self-determination theory (SDT) postulates that a psychological need-supportive environment (i.e. one that supports competence, autonomy, and social relatedness) is effective in maintaining volitional motivation which can lead to sustained positive behavioral changes including PA. Although research has supported the central tenets of the SDT, there is limited evidence examining whether a summer camp intervention can sustain improvements in PA motivation and behavior. Thus, this study examined the acute and 12-weeks longer-term effectiveness of a five-day psychological need-support centered summer camp on healthy weight and overweight adolescent girls’ weight management behaviors. A single-group case series study with pre-, post, and 12-week follow-up-test analyses. A sample comprised 42 (Mage = 11.70±1.12) adolescent females. Exercise motivation, PA intention, and PA and dietary behaviors were measured. The findings showed a between-group effect on daily steps (F(1, 19) = 15.83, p = . 001,ηp2 = . 46), moderate-to-vigorous intensity PA (F(1, 19) = 4.58, p = . 046, ηp2 = . 19), energy intake (F(1, 19) = 7.23, p = . 013, ηp2 = . 27), PA intention (F(2, 18) = 6.25, p = . 024, ηp2 = . 28), intrinsic motivation (F(2, 18) = 6.25, p = . 024, ηp2 = . 28), and amotivation (F(2, 18) = 16.25, p < . 001, ηp2 = . 54). A need-supportive summer camp may be especially effective in improving PA motivation and behavior in overweight girls.
A New Collusion Attack Using Interpolation for Multimedia Fingerprinting

Digital fingerprinting is a special digital watermarking technology that can deter legal user to redistribute multimedia content to others. With traitor tracing, it can detect illegal users who use multimedia content illegally. However, collusion attack can avoid some illegal users being detected. With different fingerprinted copies, collusion attack can produce a new colluded fingerprinted copy for get rid of the fingerprint information from the colluded copy, so digital fingerprinting technique should deter collusion attacks. In order to improve the performance of new digital fingerprinting technique in future, this paper presents a new collusion attack approach based on interpolation. The proposed interpolation collusion attack scheme comes from the idea of image fusion. The experimental results demonstrate that the proposed interpolation collusion attack method outperform average attack and maximum attack.
Trajectories of Self-control in a Forensic Psychiatric Sample: Stability and Association with Psychopathology, Criminal History, and Recidivism

The current study examined trajectories of two indicators of self-control—impulsivity and coping skills—in 317 forensic psychiatric patients, as well as associations with psychopathology, crime, and recidivism. Violent recidivism was positively associated with coping skills at admission to the clinic and with impulsivity at discharge. Only a small correlation was found between self-control and criminal history, and there was no association with psychopathology. We found multiple trajectories of self-control using Latent Class Growth Models: more than 89% improving over time. In addition, patients with Cluster C personality disorders showed greater improvement in coping skills. Patients showing less improvement in impulsivity had greater rates of crime and recidivism. We conclude that self-control can be influenced by interventions or treatment, and that both starting values and trajectories of self-control provide valuable information. Interestingly, the associations between self-control and psychopathology, crime and recidivism were not as strong as reported in other populations.
Adding Meaningful Context to Robotics Programs (Work in Progress)

Adding Meaningful Context to Robotics Programs (Work in Progress) Dr. Michele Miller, Michigan Technological University Dr. Michele Miller is a Professor of Mechanical Engineering at Michigan Technological University. She teaches classes on manufacturing and does research in engineering education with particular interest in hands-on ability, lifelong learning, and project-based learning. Dr. Nina Mahmoudian, Michigan Technological University Dr. Nina Mahmoudian is an assistant professor in the Mechanical Engineering-Engineering Mechanics Department at Michigan Technological University. She is the founding director of the Nonlinear and Autonomous Systems Laboratory (NASLab). She is a recipient of 2015 National Science Foundation CAREER award and 2015 Office of Naval Research YIP award. Dr. Mo Rastgaar, Michigan Technological University Mo Rastgaar received the Ph.D. degree in mechanical engineering from Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, in 2008. He is currently an Associate Professor in mechanical engineering and the Director of the Human-Interactive Robotics Lab. His present research focuses on assistive robots by characterizing the agility in the human gait. Dr. Rastgaar is a recipient of 2014 NSF CAREER Award. Saeedeh Ziaeefard, Michigan Technological University Saeedeh Ziaeefard is a PhD student and research assistant with Nonlinear and Autonomous Systems Laboratory (NASLab) in the Department of Mechanical Engineering-Engineering Mechanics at Michigan Technological University. Her research interests include engineering education, control and navigation of autonomous underwater vehicles. Amy Joy Patterson, Michigan Technological University I am an undergraduate student at Michigan Technological University studying Psychology. I work as a research assistant for a number of departments at my university. Jacob Bailey, Nonlinear Autonomous Systems Laboratory
Professional Development: Teachers' Attitudes in Online and Traditional Training Courses.

Humans have a basic need to be constantly engaged in learning. The learning process, however, has undergone interesting changes as a result of the technological revolution, such as the advent of online learning. Technological changes in learning have also affected professional development courses. The purpose of this study is to compare attitudes among teachers enrolled in online and traditional training course in “Pisgah” teaching staff development centers in Israel. This study was conducted using a quantitative method. The study population included 495 teachers. The findings indicated four factors related to teachers’ attitudes: Effectiveness and Application, Environment, Course Assignments, and Attitudes towards ICT (information and communication technology). Significant differences were found in the Environment factor and the Attitudes towards ICT factor between online and traditional training, in favor of online training. A multi-regression analysis found that the effectiveness of a course can be explained by the Environment, Course Assignments, and Attitudes towards ICT factors.
Detecting Malicious Anomalies in IoT: Ensemble Learners and Incomplete Datasets

Anomalies in IoT typically occur as a result of malicious activity. As an example, a point anomaly may occur once network intrusion is attempted, while collective anomaly may result from device being hacked. Due to the nature of the attacks, some anomalies are represented by incomplete captured instances or imbalanced captured datasets. For example, features may have some values missing from the row or may contain both categorical and numerical values. Once pre-processed, these datasets become suitable training sets for any machine learning classifier that detects anomalies. However, there are situations where pre-processing takes large amount of time in the operating phase or simply is not executable due to the nature of the data. For example, a feature that contains unknown number of categorical values, such as strings, cannot be converted into finite number of binary features before the training. In this scenarios, basic machine learning methods, such as Support Vector Machines or Decision Trees either fail to operate or provide poor classification performance. Unlike basic, ensemble learners manage these data instances efficiently and provide good anomaly detection rates. This paper analyses the performance of ensemble learners on incomplete IoT intrusion datasets, represented by point anomalies.
Optimization of XFAST design

An optimization of computations in one unit code phase with restriction of property of detection and false alarm was introduced. The property of detection and false alarm with code Doppler were calculated, then the calculations of searching one code phase were analysed. Two samples with carrier Doppler fixed-6—6 kHz or carrier to noise ratio fixed 46 dBHz are given, the detection probability of 95% and false probability of 10-6 can be achieved. Finally, the results are confirmed by simulation.