Reviewerly/exHarmony huggingface dataset:
Using interactive technologies to enhance student engagement in higher education online learning

AbstractThe challenge addressed in this study is how the effectiveness of Padlet, Panopto video-embedded quizzes and Google Docs can be evaluated in terms of student engagement. Using a range of data sources, we discussed whether each technology influenced various dimensions of student engagement. The study found that certain technologies improved students’ behavioral and cognitive engagement and they provided students with flexible opportunities for social, collaborative and emotional engagement. By enhancing the online opportunities for behavioral, cognitive and interpersonal engagement, specific technologies helped engage students with what and how they were learning. It concludes with further recommendations aimed toward the intentional use of technology for improving students’ online learning engagement.Keywords: Student engagementonline learningPadletPanopto video-embedded quizzesGoogle Docs Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe 2019 Technology Demonstrator grant was received from the University of Southern Queensland.Notes on contributorsSeyum GetenetSeyum Getenet is a senior lecturer of Mathematics Education at the University of Southern Queensland, School of Education. His research interest is in online learning and technology use, numeracy assessment and mathematics teachers’ knowledge and how professional development is used as an enabler for change.Eseta TualauleleiEseta Tualaulelei is a senior lecturer in Education. She teaches and conducts research in intercultural communication, multicultural education and teacher professional development, online learning and learning with technology. Collaborating with educational and community organizations, she aims to find ways to improve the educational experiences of diverse learners and their families.
The Digital Human Revolution

This paper explores the creation of advanced ’digital humans’ using ’Metahuman’ technology [Fang et al. 2021] and their role in elevating storytelling across platforms. UME Studios’ groundbreaking work in the virtual reality experience "Stay Alive My Son" [Pin 2000] (SAMS) [Bousis 2021] is discussed, highlighting their achievement in representing real people at high fidelity. The design and implementation process of digital humans in SAMS, along with the technical and ethical considerations [IEEE 2022] involved, are examined.
Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition

For the deaf population that speaks Arabic, Arabic Sign Language (ArSL) is an essential means of communication. This research presents a convolutional model for recognizing Arabic sign language because of the importance of clear communication. We hope to improve the deaf community's access to communication and broaden its sense of belonging by harnessing deep learning's power and fine-tuning the model to ArSL's particularities. To represent the complex hand movements and visual patterns that are characteristic of ArSL, the proposed model makes use of a variety of carefully made architectural decisions, such as the number of layers, the size of the kernels, the activation functions, and the pooling approaches. Our model outperforms state-of-the-art machine learning techniques, as shown by experimental findings on a large dataset. These results not only lay the groundwork for future developments in sign language recognition, but also demonstrate the promise of our technique in improving communication for the Arabic-speaking deaf community.
FIMAR: Fast incremental memory acquisition and restoration system for temporal-dimension forensic analysis

Recent advances in anti-forensic techniques, including encryption, obfuscation, and kernel-structure manipulations, make conventional storage, network, and memory forensics difficult. For example, the code of packed malware is encrypted on storage, network packets, and system RAM to hide its existence; it appears briefly in plain text on the system RAM during its execution phase. Memory forensics, therefore, has been a vital component of today's digital forensics. Conventional memory acquisition techniques, however, capture only the current state of a system RAM; if the timing of the acquisition is late, the evidence could be lost forever. Since the state-of-the-art anti-forensic attacks leave only the slightest evidence for the shortest time, finer temporal acquisition and analysis are needed. This paper presented a Fast Incremental Memory Acquisition and Restoration (FIMAR) system that enables us to acquire multiple memory snapshots (i.e., memory dumps) over time and to construct a timeline of past activities on a computer. A thin hypervisor and its Second Level Address Translation (SLAT) were used to track memory page changes and to obtain atomic memory snapshots. The hash calculation of 4 KiB chunks was used to detect updated parts of the system RAM to reduce the transfer size. We implemented the incremental memory acquisition algorithms using a thin hypervisor called BitVisor. Finally, we tested the developed FIMAR system to analyze BlueSky ransomware with anti-forensic logic. The developed FIMAR system improved the atomicity of memory snapshots using TLB shootdown and considering per-core Extended Page Tables (EPTs) compared to previous hypervisor-based memory acquisition systems.
Memory efficient data-free distillation for continual learning

Deep neural networks suffer from the catastrophic forgetting phenomenon when trained on sequential tasks in continual learning, especially when data from previous tasks are unavailable. To mitigate catastrophic forgetting, various methods either store data from previous tasks, which may raise privacy concerns, or require large memory storage. Particularly, the distillation-based methods mitigate catastrophic forgetting by using proxy datasets. However, proxy datasets may not match the distributions of the original datasets of previous tasks. To address these problems in a setting where the full training data of previous tasks are unavailable and memory resources are limited, we propose a novel data-free distillation method. Our method encodes knowledge of previous tasks into network parameter gradients by Taylor expansion, deducing a regularizer relying on gradients in network training loss. To improve memory efficiency, we design an approach to compressing the gradients in the regularizer. Moreover, we theoretically analyze the approximation error of our method. Experimental results on multiple datasets demonstrate that our proposed method outperforms the existing approaches in continual learning.
W-Bad: Interception, Inspection, and Interference with Web Proxy Auto-Discovery (WPAD)

The Web Proxy Auto-Discovery Protocol (WPAD) was developed decades ago as a way to automatically configure Web proxies for clients in a given network environment. However, almost since its inception it has been identified as being vulnerable—both in design and implementation. Yet even today it is found in popular operating systems and browsers. In this paper, we chronicle the history of the domain name wpad.domain.name, which has caused grief for users worldwide, due to router firmware bugs and efforts to intercept, inspect, and interfere with Web requests. We measure its delegation history, the manner in which it was opportunistically used for abuse, and the set of vulnerable clients over time.
Laser beam scanning-based 3D structured light scanner combining a bi-resonant MEMS mirror with low-cost imaging sensor

This paper presents a technique of structured light 3D scanning based on Laser Beam Scanner (LBS) using a bi-resonant vacuum-packaged MEMS mirror and a low-cost imaging sensor. A single infrared continuous-wave laser source, together with the MEMS scanner, generates dynamic Lissajous patterns delivering a continuous increase of scan precision over time. A collimated laser beam is directly coupled with the MEMS mirror without the need for diffractive optics. Lissajous patterns are captured by a low-cost CMOS imaging sensor having a short integration time while a bandpass filter ensures that only infrared signals are detected from the camera. Due to the non-repeatability of the generated Lissajous patterns, all pixels of the sensed environment are illuminated within few consecutive images. The spatial depth resolution is defined by the sensor resolution, enabling very high-density 3D scans due to the high imaging resolution achievable by CMOS sensors. Advanced image processing algorithms are implemented to accurately calculate pixel-wise depth on taken images. The hardware complexity of the proposed system is significantly reduced compared to other 3D sensing solution, which leads to a lower bill of materials. All components are mass-producible and easy to assemble, making the system advantageous for high volume markets and suitable for integration into consumer products. The proposed 3D scanning system can be integrated in AR and MR headsets for use-cases such as hand gesture scanning and indoor SLAM. Due to its high resolution capability, the presented 3D scanning technique can also be used for facial scanning and avatar generation.
Single-shot structured light with diffractive optic elements for real-time 3D imaging in collaborative logistic scenarios

We introduce an innovative concept for 3D imaging that utilizes a structured light principle. While our design is specifically tailored for collaborative scenarios involving mobile transport robots, it is also applicable to similar contexts. Our system pairs a standard camera with a projector that employs a diffractive optical element (DOE) and a collimated laser beam to generate a coded light pattern. This allows a three-dimensional measurement of objects from a single camera shot. The main objective of the 3D-sensor is to facilitate the development of automatic, dynamic and adaptive logistics processes capable of managing diverse and unpredictable events. The key novelty of our proposed system for triangulation-based 3D reconstruction is the unique coding of the light pattern, ensuring robust and efficient 3D data generation, even within challenging environments such as industrial settings. Our pattern relies on a perfect submap, a matrix featuring pseudorandomly distributed dots, where each submatrix of a fixed size is distinct from the others. Based on the size of the working space and known geometrical parameters of the optical components, we establish vital design constraints like minimum pattern size, uniqueness window size, and minimum Hamming distance for the design of an optimal pattern. We empirically examine the impact of these pattern constraints on the quality of the 3D data and compare our proposed encoding with some single-shot patterns found in existing literature. Additionally, we provide detailed explanations on how we addressed several challenges during the fabrication of the DOE, which are crucial in determining the usability of the application. These challenges include reducing the 0th diffraction order, accommodating a large horizontal field of view, achieving high point density, and managing a large number of points. Lastly, we propose a real-time processing pipeline that transforms an image of the captured dot pattern into a high-resolution 3D point cloud using a computationally efficient pattern decoding methodology.
Book Review

This book explores how blockchain technology is disrupting economies and transforming societies. It offers detailed insights into the synergistic and complementary effects of blockchain and other new and emerging technologies such as artificial intelligence, the internet of things, satellite imagery, and digital twins. The book highlights how the developments of ecosystems around blockchain are helping to realize the potential of this technology to act as a “trust machine” across more contexts and settings. Multidisciplinary in both scope and perspectives, the book looks at blockchain's potential for impacting challenges in supply chain management, security, privacy and compliancy issues, and transforming payment and settlement systems. It further provides insights into the opportunities, barriers, and enablers of blockchain adoption by organizations.
Behaviour and Bot Analysis on Online Social Networks

The internet is home to a multitude of social networks that provide users with a sense of community and connection across the world. Among these, Twitter and Reddit are two of the most popular. While Twitter users follow and interact with other users (tweets), Reddit users follow and interact with communities known as subreddits. In addition to mainstream social networks, alternative platforms such as Parler exist for users who prefer less moderated online environments. However, there are also malicious users, such as bots and trolls, who exploit social networks for malicious purposes. Therefore, separating malicious behaviors from legitimate ones is crucial. This research aims to evaluate Botometer and RepScope systems to analyze the temporal posting behaviors of Twitter, Reddit, and Parler users and to identify bots, trolls, and malicious behaviors.
Simulation Method for the Physical Deformation of a Three-Dimensional Soft Body in Augmented Reality-Based External Ventricular Drainage

Intraoperative navigation reduces the risk of major complications and increases the likelihood of optimal surgical outcomes. This paper presents an augmented reality (AR)-based simulation technique for ventriculostomy that visualizes brain deformations caused by the movements of a surgical instrument in a three-dimensional brain model. This is achieved by utilizing a position-based dynamics (PBD) physical deformation method on a preoperative brain image.An infrared camera-based AR surgical environment aligns the real-world space with a virtual space and tracks the surgical instruments. For a realistic representation and reduced simulation computation load, a hybrid geometric model is employed, which combines a high-resolution mesh model and a multiresolution tetrahedron model. Collision handling is executed when a collision between the brain and surgical instrument is detected. Constraints are used to preserve the properties of the soft body and ensure stable deformation.The experiment was conducted once in a phantom environment and once in an actual surgical environment. The tasks of inserting the surgical instrument into the ventricle using only the navigation information presented through the smart glasses and verifying the drainage of cerebrospinal fluid were evaluated. These tasks were successfully completed, as indicated by the drainage, and the deformation simulation speed averaged 18.78 fps.This experiment confirmed that the AR-based method for external ventricular drain surgery was beneficial to clinicians.
Let It Go: Relieving Garbage Collection Pain for Latency Critical Applications in Golang

Garbage Collection (GC) is a representative automatic memory manager widely deployed in popular programming languages, such as Java, C\#, and Golang (Go). Through GC, these languages provide programmers with flexibility and safety. However, GC leads to non-trivial overhead in compute and memory resources during application runtime. GC threads compete with non-GC threads (mutators) of an application, which particularly impacts latency-critical (LC) applications and causes long tail latency. Existing GC approaches do not efficiently address the interference, as GC is triggered passively without a global insight of the application; or they employ incremental GC to reduce the interference, while the incremental progress is not dynamically tailored during GC process according to runtime characteristics, which leads to significant performance degradation upon bursty requests.
Ilúvatar: A Fast Control Plane for Serverless Computing

Providing efficient Functions as a Service (FaaS) is challenging due to the serverless programming model and highly heterogeneous and dynamic workloads. Great strides have been made in optimizing FaaS performance through scheduling, caching, virtualization, and other resource management techniques. The combination of these advances and growing FaaS workloads have pushed the performance bottleneck into the control plane itself. Current FaaS control planes like OpenWhisk introduce 100s of milliseconds of latency overhead, and are becoming unsuitable for high performance FaaS research and deployments.
Libra: Harvesting Idle Resources Safely and Timely in Serverless Clusters

Serverless computing has been favored by users and infrastructure providers from various industries, including online services and scientific computing. Users enjoy its auto-scaling and ease-of-management, and providers own more control to optimize their service. However, existing serverless platforms still require users to pre-define resource allocations for their functions, leading to frequent misconfiguration by inexperienced users in practice. Besides, functions' varying input data further escalate the gap between their dynamic resource demands and static allocations, leaving functions either over-provisioned or under-provisioned. This paper presents Libra, a safe and timely resource harvesting framework for multi-node serverless clusters. Libra makes precise harvesting decisions to accelerate function invocations with harvested resources and jointly improve resource utilization by profiling dynamic resource demands and availability proactively. Experiments on OpenWhisk clusters with real-world workloads show that Libra reduces response latency by 39% and achieves 3X resource utilization compared to state-of-the-art solutions.
Current Status and Key Issues of Data Management in Tertiary Hospitals: A Case Study of Seoul National University Hospital

In the era of the Fourth Industrial Revolution, where an ecosystem is being developed to enhance the quality of healthcare services by applying information and communication technologies, systematic and sustainable data management is essential for medical institutions. In this study, we assessed the data management status and emerging concerns of three medical institutions, while also examining future directions for seamless data management.To evaluate the data management status, we examined data types, capacities, infrastructure, backup methods, and related organizations. We also discussed challenges, such as resource and infrastructure issues, problems related to government regulations, and considerations for future data management.Hospitals are grappling with the increasing data storage space and a shortage of management personnel due to costs and project termination, which necessitates countermeasures and support. Data management regulations on the destruction or maintenance of medical records are needed, and institutional consideration for secondary utilization such as long-term treatment or research is required. Government-level guidelines for facilitating hospital data sharing and mobile patient services should be developed. Additionally, hospital executives at the organizational level need to make efforts to facilitate the clinical validation of artificial intelligence software.This analysis of the current status and emerging issues of data management reveals potential solutions and sets the stage for future organizational and policy directions. If medical big data is systematically managed, accumulated over time, and strategically monetized, it has the potential to create new value.
COLTI: Towards Concurrent and Co-located DNN Training and Inference

Deep learning models are extensively used in a wide range of domains, e.g., scientific simulations, predictions, and modeling. However, training these dense networks is both compute and memory intensive, and typically requires accelerators such as Graphics Processing Units (GPUs). While such DNN workloads consume a major proportion of the limited onboard high-bandwidth memory (HBM), they typically underutilize the GPU compute resources. In such scenarios, the idle compute resources on the GPU can be leveraged to run pending jobs that can either be (1) accommodated on the remainder HBM, or (2) can share memory resources with other concurrent workloads. However, state-of-the-art workload schedulers and DNN runtimes are not designed to leverage HBM co-location to improve resource utilization and throughput. In this work, we propose COLTI, which introduces a set of novel techniques to solve the aforementioned challenges by co-locating DNN training and inference on memory-constrained GPU devices. Our preliminary evaluations of three different DNN models implemented in the PyTorch framework demonstrate up to 37% and 40% improvement in makespan and memory utilization, respectively.
Secondary Use Provisions in the European Health Data Space Proposal and Policy Recommendations for Korea

This article explores the secondary use provisions of the European Health Data Space (EHDS), proposed by the European Commission in May 2022, and offers policy recommendations for South Korea.The authors analyzed the texts of the EHDS proposal and other documents published by the European Union, as well as surveyed the relevant literature.The EHDS proposal seeks to create new patient rights over electronic health data collected and used for primary care; and establish a data sharing system for the re-use of electronic health data for secondary purposes, including research, the provision of personalized healthcare, and developing healthcare artificial intelligence (AI) applications. These provisions envisage requiring both private and public data holders to share certain types of electronic health data on a mandatory basis with third parties. New government bodies, called health data access bodies, would review data access applications and issue data permits.The overarching aim of the EHDS proposal is to make electronic health data, which are currently held in the hands of a small number of organizations, available for re-use by third parties to stimulate innovation and research. While it will be very challenging for South Korea to adopt a similar scheme and require private entities to share their proprietary data with third parties, the South Korean government should consider making at least health data collected through publicly funded research more readily available for secondary use.
Honing USD: Lessons Learned and Workflow Enhancements at Walt Disney Animation Studios

During development for Strange World, we expanded on an existing Pixar’s Universal Scene Description (USD) based pipeline, transitioning Disney’s XGen to be authored in USD and paving the way for more departments to explore Houdini-based workflows. Due to the increase in data on our USD stages, in combination with remaining optimization opportunities from the first iteration of our USD pipeline, Walt Disney Animation Studios presents techniques and lessons learned to optimize our asset structure and redesign our change management pipeline to uphold interactive performance, all while sustaining existing artist workflows.
A Non-Contact Human-Computer Interaction Method Based on Gaze

Gaze interaction has a broad application prospect in the field of human-computer interaction. Traditional gaze interactive sensing devices are expensive and inconvenient to use. The gaze point estimation model based on deep learning is complex and poor in generalization, and difficult to deploy on intelligent terminals. This paper proposed a gaze point estimation network based on deep learning and its personalized calibration method which can adapt to different devices and users. The gaze point estimation network used eye images, eye corners and the camera relative to screen position as inputs to extract feature embeddings. Then the feature embeddings were fed to the trained personalized SVR model to obtain the final gaze point estimation. Tests were conducted on both the self-built gaze dataset and the open source dataset to verify the effectiveness of the proposed model in terms of accuracy and latency. On this basis, a simple human-computer interaction application was implemented by analyzing the fixation point and identifying the user's control behavior.