A machine learning resource allocation solution to improve video quality in remote education

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing internet traffic on the video quality in an educational context is also presented

Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling

The recent global pandemic has transformed the way education is delivered, increasing the importance of videobased online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi- Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-theart solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results

Cognitive-Behavioral Therapy

Cognitive-behavioral therapy (CBT) is the merging of behavioral and cognitive therapies that mostly focuses on working with the client in the present. Although there are many approaches to CBT, there tend to be some common features. For example, CBT is generally a directive approach to psychotherapy that helps clients to challenge their problematic thoughts and to change the behaviors associated with those thoughts. In addition, most approaches to CBT are structured and time limited and include some type of homework where the client can practice the cognitive and behavioral strategies learned in the therapeutic setting. This entry focuses mostly on CBT as defined by Aaron Beck, one of the early founders of this approach

On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.Comment: 26 pages, 4 figure

Admission Policy Review: Strengthening Indigenous In-Community Training Programs

Canada’s colonial past significantly impacts prospective Indigenous student postsecondary enrollment. For the past fifty years, postsecondary institutions have focused on assimilation and cultural renewal. One assumption is Indigenous learners share similar educational experiences including ease and access to westernized high school programs with a credit or term system and ease and access to transcripts and criminal records checks often required for postsecondary admission. This Organizational Improvement Plan (OIP) addresses the Problem of Practice (PoP) in admission procedures that do not consider Indigenous knowledges, experiences, and criteria for entry into postsecondary programming in SMH Department at LAC College. As an academic manager in SMH Department and facilitator of college career programs in Indigenous communities in central Canada, I explore the organizational context at LAC College and propose a solution to the PoP. This OIP includes a review of LAC College’s admission policy and implementation of an Indigenized admission process. Adaptive and distributed leadership perspectives are the approaches utilized in this OIP. The Critical Paradigm is the underlining perspective, and the voices of Indigenous colleagues and educational partners inform my perspectives in this OIP. I will conclude by discussing the Hiatt 2013 ADKAR change theory and evaluation plan utilized in this OIP. Keywords: Utilization Focused Evaluation, Critical Paradigm, Distributed Leadership, Ethical Leadership, Truth and Reconciliation, Indigenous admission criteria, In-Community Training

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

A Community-Based Accommodation Program for Adults with Autism and Mental Retardation

There is a paucity of treatment literature for significant and intractable behavior problems in adults with autism and mental retardation. Four adults with autism, severe to profound mental retardation, and serious, long-term behavior problems participated in an accommodation training program as an adjunct to more traditional behavioral and medical treatments. The accommodation program consisted of designing highly structured and predictable daily routines to reduce the impact of environmental factors that had previously resulted in behavior problems. Following three to six years of participation in the accommodation program, a significant treatment effect size was obtained for all participants. The benefits of this approach for improving the treatment-resistant problem behaviors and quality of life for adults with autism and mental retardation in a community-based setting are discussed as well as directions for future research

Learning-based ship design optimization approach

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multiagent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application

A Comparison of the Effects of Low- and High-Technology Activity Schedules on Task Engagement of Young Children with Developmental Disabilities

Individuals with intellectual and developmental disabilities may have challenges with executive functioning skills (e.g., planning and organization). Research has shown that external supports, such as activity schedules, increase independence and task engagement. With the availability of mobile devices, activity schedules can be presented to individuals in a flexible and durable manner. Three elementary school students used a low-technology paper-based activity schedule (LT), a high-technology activity schedule (HT) on an iPad, and an ultra high-technology schedule with audio and video (UHT) on an iPad for the same routine. Results demonstrated increased on-task behavior with the use of an activity schedule over none. However, there were no significant differences in on-task behavior among paper-based and iPad-based schedules. Still, preference assessments demonstrated students favored the ultra-high-technology schedule. Implications of these findings and future research are discussed