Asynchronous Information Research Articles

Teaching information literacy in online psychology courses: Effects on student performance and self-reported confidence

Using the Framework for Information Literacy published by the Association of College & Research Libraries, we developed an online intervention to increase information literacy. Specifically, we hypothesized that undergraduate students in an information literacy intervention group would increase in information literacy performance and confidence compared to a control group which did not receive asynchronous online information literacy instruction. We used a non-equivalent groups pretest-posttest design and measured students’ ( N = 101) information literacy performance and self-reported confidence before and after asynchronous information literacy instruction. We compared the intervention with a non-intervention control condition using two existing undergraduate psychology courses offered online across two semesters. Partially confirming our hypotheses, we found that providing asynchronous information literacy instruction not only increased students’ information literacy performance but also helped students to evaluate their own information literacy skills more accurately. In contrast, our intervention did not selectively increase confidence in information literacy as both information literacy intervention and control groups similarly increased in information literacy confidence over time. Nevertheless, these findings show that we managed to develop an online, theory-based intervention to increase information literacy. This intervention has the potential to build the foundations for students to engage in scientific conversations by elevating their understanding of how scientific information is produced and how science affects their daily lives.

Teaching Intersectionality in Instructional Librarianship: Asynchronous Information Literacy Instruction in the Health Sciences

This article explores the topic of intersectionality and its applications in teaching graduate students at a health sciences university. The case study examined the creation of an asynchronous information literacy module that centered intersectionality in the research process. Summative assessment data indicated that students were able to critically reflect on gaps in their knowledge to address health inequities, learn the narratives of multiply-marginalized patients, and make connections between research and intersectional justice. The study also revealed areas for improvement such as restructuring the assessment component of the module to better evaluate student success, strengthening the module as a socially engaging learning space for students, and integrating other metrics to measure the student experience. Findings from this study demonstrate how instructional librarians can create equitable and engaging learning spaces through an asynchronous instruction model as well as teaching intersectionality in STEMM-specific disciplines.

V2X-ViTv2: Improved Vision Transformers for Vehicle-to-Everything Cooperative Perception.

In this paper, we study the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present V2X-ViTs, a robust cooperative perception framework with V2X communication using novel vision Transformer models. First, we present V2X-ViTv1 containing holistic attention modules that can effectively fuse information across on-road agents (i.e., vehicles and infrastructure). Specifically, V2X-ViTv1 consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention, which captures inter-agent interaction and per-agent spatial relationships. These key modules are designed in a unified Transformer architecture to handle common V2X challenges, including asynchronous information sharing, pose errors, and heterogeneity of V2X components. Second, we propose an advanced architecture, V2X-ViTv2, that enjoys increased ability for multi-scale perception. We also propose advanced data augmentation techniques tailored for V2X applications to improve performance. We construct a large-scale V2X perception dataset using CARLA and OpenCDA to validate our approach. Extensive experimental results on both synthetic and real-world datasets show that V2X-ViTs achieve state-of-the-art performance for 3D object detection and are robust even under harsh, noisy environments. All the code and trained models will be available at https://github.com/DerrickXuNu/OpenCOOD.

Quality Matters Review of LIB 2210: Applying the QM Rubric for Higher Education to an Information Literacy Course

This article discusses the use of the Quality Matters Rubric for Higher Education, 6th ed. to self-review an asynchronous information literacy course. In this instance, the authors acted as reviewers and subject matter experts (SME). The Rubric proved to be a useful tool that encompasses several instructional design best practices. After the official self-review period, the QM Rubric influenced the enhancement and updates to the course. The authors recommend more use of the QM Rubric by teaching librarians, collaboration with instructors going through a QM Review, and training for online teaching in Library and Information Science graduate programs.

Research on Asynchronous Information Fusion Technology of the Distributed Active/Passive Imaging Detection System

In the process of target cooperative tracking of the distributed multi-imaging detection system, aiming at the problem of asynchronous measurement information from different detectors, a distributed Asynchronous and Heterogeneous Information Matrix Fusion algorithm based on the Cubature Kalman Filter (CKF-AHIMF) is proposed. According to the Local Detector Driven Communication (LDDC) method and CKF nonlinear mapping, IMF distributed fusion algorithm is generalized into nonlinear, asynchronous, and heterogeneous cases to integrate the information from a multi-imaging detection system and to obtain global high-precision target trajectory. Through the digital simulation, it is verified that the CKF-AHIMF algorithm under the LDDC method proposed in this paper is more effective and has higher tracking accuracy than other distributed fusion algorithms in dealing with the problem of target cooperative tracking under asynchronous information from local imaging detectors, which is also valid to implement in precision detection and guidance system.

Decentralized Multi-Robot Information Gathering From Unknown Spatial Fields

We present an incremental scalable motion planning algorithm for finding maximally informative trajectories for decentralized mobile robots. These robots are deployed to observe an unknown spatial field, where the informativeness of observations is specified as a density function. Existing works that are typically restricted to discrete domains and synchronous planning often scale poorly depending on the size of the problem. Our goal is to design a distributed control law in continuous domains and an asynchronous communication strategy to guide a team of cooperative robots to visit the most informative locations within a limited mission duration. Our proposed Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) algorithm extends ideas from asynchronous Bayesian Optimization (BO) to efficiently sample from a density function. It then combines them with decentralized reactive motion planning techniques to achieve efficient multi-robot information gathering activities. We provide a theoretical justification for our algorithm by deriving an asymptotic no-regret analysis with respect to a known spatial field. Our proposed algorithm is extensively validated through simulation and real-world experiment results with multiple robots.

The role of aesthetics in redesigning accessible information literacy modules

The outbreak of COVID-19 added an increased sense of urgency to creating accessible online materials for higher education professionals, including academic librarians, as they explored new ways to engage with users online. Librarians who relied on in-person sessions to provide information literacy instruction to students shifted to synchronous and asynchronous instruction methods. This paper shares the experiences of an instruction librarian tasked with evaluating and redesigning a set of asynchronous information literacy modules for first-year undergraduate students in the summer of 2020. The evaluation and redesign were informed by contemporary debates about the design of online instructional materials from a web design and instructional design (ID) perspective. In particular, the author sought to investigate the claim that increasing the accessibility of online learning objects decreases their aesthetic appeal. Discussions of aesthetics and design are largely absent in the professional literature of library and information science (LIS), yet they are relevant, as many librarians perform ID duties. Since definitions of aesthetics are far-ranging, the author employed a specific framework discussed by Brown et al. — contrast, alignment, repetition and proximity (CARP) — for assessing the aesthetic value of the modules. After utilising a variety of tools to evaluate the accessibility of the modules along with the CARP framework, the author concluded that increasing the accessibility of the modules also increased their aesthetic value.

Asynchronous Information Fusion in Intelligent Driving Systems for Target Tracking Using Cameras and Radars

In this article, an asynchronous information fusion issue is investigated for a camera and a radar in an intelligent driving system. Local camera and radar estimators with missed detections are developed independently at synchronized state update time for target tracking. A Kuhn–Munkers algorithm is used to match local tracking results of camera and radar for fusion estimation of a same target. A fusion estimator is obtained by a matrix-weighted fusion algorithm with wide detection range and reliable fused estimates. Effectiveness of the proposed asynchronous fusion estimator is displayed by experimental results on road vehicle tracking.

Designing an Asynchronous Information Literacy Lecture to Change Learning Styles in ICT Utilization

With the development of information technology, various forms of lectures have been implemented in higher education institutions in addition to traditional face-to-face lectures, such as asynchronous and remote synchronous lectures. In recent years, various educational materials have become available on the Internet, allowing students to learn freely according to their interests. Consequently, we believe that it is necessary to respond to students' learning styles as students increasingly seek these materials and continuously learn on their own. In this paper, we conducted asynchronous lectures that prioritized student autonomy to promote self-directed learning and analyzed students’ learning activities. Furthermore, we gathered and analyzed the learning records of students who took this course during the latter semester to verify whether their learning styles were sustained. We report the results of this analysis.

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

The economic dispatch (ED) problem aims to minimize the total generation cost while satisfying certain constraints, such as valve-point effects, multi-fuel options, prohibited operating zones, transmission losses, and ramp rate limits. In this paper, these constraints are considered simultaneously for the first time, resulting in a complex nonconvex ED problem. A diversity-based parallel particle swarm optimization (DPPSO) is proposed to solve the nonconvex ED problem, where the implementation details—such as evaluation function design, particle definition, and equality and inequality handling strategies—have been carefully discussed. In our approach, the population of DPPSO is divided into different groups to maintain diversity in particles so that the optimization capacity can be enhanced. An asynchronous information–sharing mechanism (AISM) helps decrease the population size. Hence, the computational burden is reduced. Moreover, information in different groups is calculated parallelly and updated asynchronously to improve computational efficiency. Benchmark functions are employed to demonstrate the effectiveness of the proposed method. Furthermore, three nonconvex ED problems are resolved by the proposed method, and state-of-the-art performance has been achieved. In addition, the proposed algorithm is highly modular, making it easy to unite other salient variants of particle swarm optimization (PSO) to improve its performance.

Asynchronous Output Feedback Control of Hidden Semi-Markov Jump Systems With Random Mode-Dependent Delays

This article is concerned with the problem of output feedback control for a class of continuous-time hidden semi-Markov jump systems with time delays. Due to the limitations of the actual environment, system modes are usually undetectable, which are called hidden modes. The controller modes are described as observable modes. Emission probabilities are used to establish the relationship between abovementioned two concepts. The jump parameters are governed by the hidden semi-Markov process, which can better describe the asynchronous information between the controller modes and the system modes. Besides, time delays are considered to be time-varying and dependent on the hidden modes. By employing some mathematical transformation and constructing a novel Lyapunov–Krasovskii functional, some new parameter-dependent sufficient stabilization conditions can be obtained by designing an observed-mode-dependent static output-feedback controller. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.

Information transmission in action video gaming experts: Inferences from the lateralized readiness potential.

Research showed that action real-time strategy gaming (ARSG) experience is related to cognitive and neural plasticity, including visual selective attention and working memory, executive control, and information processing. This study explored the relationship between ARSG experience and information transmission in the auditory channel. Using an auditory, two-choice, go/no-go task and lateralized readiness potential (LRP) as the index to partial information transmission, this study examined information transmission patterns in ARSG experts and amateurs. Results showed that experts had a higher accuracy rate than amateurs. More importantly, experts had a smaller stimulus-locked LRP component (250 – 450 ms) than amateurs on no-go trials, while the response-locked LRP component (0 – 300 ms) on go trials did not differ between groups. Thus, whereas amateurs used an asynchronous information transmission pattern, experts used a reduced asynchronous information transmission pattern or a synchronous pattern where most of processing occurred prior to response execution – an information transmission pattern that supports rapid, error-free performance. Thus, experts and amateurs may use different information transmission patterns in auditory processing. In addition, the information transmission pattern used by experts is typically observed only after long-term auditory training according to past research. This study supports the relationship between ARSG experience and the development of information processing patterns.

Cast a wider net: Leveraging canvas for asynchronous information literacy learning

Despite continued attention from academic librarians, many college students lack essential information literacy (IL) skills required for academic success. New approaches to IL instruction recognize the traditional “one-shot” is not a sufficient strategy. This paper presents the findings of a qualitative Action Research study that investigated how the availability of asynchronous learning modules in Canvas impacts IL learning among community college students. Results indicate that leveraging the existing technology of the Canvas Learning Management System (LMS) is a pragmatic and effective approach to extending IL learning beyond the “one-shot,” and that it facilitates the embedding of IL learning that may contribute to student success.

Evaluation of the Interference’s Impact of Cooperative Surveillance Systems Signals Processing for Healthcare

Patient signals produced from a physical device, such as electrocardiography (ECG), which records the electrical activity of the heart, are vulnerable to keep noise due to various physical constraints of acquisition devices. It is critical for the detection and diagnosis of a variety of diseases. An ECG signal should be displayed as clean and clear as feasible due to its relevance in assisting physicians and doctors in making appropriate judgments. ECG is vulnerable to many types of noise because it is an electrical signal. To improve the quality of assistance for consumers Info a surveillance system by cooperative surveillance systems, high-quality data processing by the observed surveillance systems is required, which predetermines the requirement for high noise immunity of the latter. At the same time, the basics of building cooperative surveillance systems as a network of two-channel asynchronous information communication systems which include numeral of transmitting and receiving process using diverse frequency limits for reception and transmission, failure-prone open single-channel queuing systems, and request signals do not allow to provide the required the noise resistance of the systems under consideration. This paper first gives a characterization of request and interference signal flows in cooperative surveillance systems and briefly examines the features of request signals and their effect on the immunity of cooperative surveillance systems. Then, it calculates the noise resistance of the demand signals of the cooperative surveillance systems for the probabilities of errors: signal skipping; First- and second-degree false alarms in the case of unrelated events, unintended and intra-system impulse interferences in the request channel

Real-Time Asynchronous Information Processing in Distributed Power Systems Control

Centralized power system algorithms are gradually augmented and substituted with distributed counterparts, where timely updated asynchronous information processing is critical for real-time implementation. This paper presents two methods to achieve the desired real-time performance. Distributed sequencing method simulates a synchronous system on an asynchronous network using a distributed logical vector clock. The second method, adaptive asynchronous processing, handles asynchrony at each iteration step through adaptive dynamics of the network agents. Necessity of the developed methods is discussed based on the specific requirements of the communication network in terms of algorithm accuracy, network size and time-varying message delays. A case study on the distributed power system state estimation for networked microgrids is presented to validate the developed methods implementing industrial publish/subscribe protocols. The results demonstrate that the developed information processing methods can be efficiently applied for real-world industrial implementation.

Deep Event Stereo Leveraged by Event-to-Image Translation

Depth estimation in real-world applications requires precise responses to fast motion and challenging lighting conditions. Event cameras use bio-inspired event-driven sensors that provide instantaneous and asynchronous information of pixel-level log intensity changes, which makes them suitable for depth estimation in such challenging conditions. However, as the event cameras primarily provide asynchronous and spatially sparse event data, it is hard to provide accurate dense disparity map in stereo event camera setups - especially in estimating disparities on local structures or edges. In this study, we develop a novel deep event stereo network that reconstructs spatial intensity image features from embedded event streams and leverages the event features using the reconstructed image features to compute dense disparity maps. To this end, we propose a novel event-to-image translation network with a cross-semantic attention mechanism that calculates the global semantic context of the event features for the intensity image reconstruction. In addition, a feature aggregation module is developed for accurate disparity estimation, which modulates the event features with the reconstructed image features by a stacked dilated spatially-adaptive denormalization mechanism. Experimental results reveal that our method can outperform the state-of-the-art methods by significant margins both in quantitative and qualitative measures.

Business research toolkit: The creation of an online, asynchronous business information literacy course

The Business Research Toolkit (BRT) is a free, noncredit-bearing course designed to serve students, faculty, and staff who are interested in learning more about the intricacies of business research. It began as a face-to-face course and evolved into an asynchronous, online course offered in the campus LMS. Transitioning the program to an online-only format required extensive planning and collaboration. This article describes the process of transitioning the toolkit online, answers common questions colleagues have about the toolkit, and provides strategies for practitioners to create similar programs at their institutions.

Model Implementation Of Text And Video Chats With Python Ide Software

Chats are bound to be an efficient way of disseminating synchronous and asynchronous information through textual messages or video communication among various people over a network. An Integrated Development Environment (IDE), is a text editor that allows developers and engineers to enhance the different features of writing executable software. Using IDEs, the programmer’s productivity is enhanced by consolidating common activities involved in writing computer programs into a single application. These activities include source code editing, auto-completing, syntax highlighting, building executable, debugging, and automated testing. Python is a programming language used to develop mostly sophisticated Machine Learning and web applications. An IDE makes coding in Python much easier and efficient. It consists of features like indentation and code folding which are very important aspects in executing Python programs successfully. Also, unresolved imports or undefined methods are indicated with warnings and error signs to give programmers more information on impending problems. A web-based Python Integrated Development Environment (IDE) was developed by applying modern communication techniques of text and video chats. This software delivers facilities to programmers for efficient development of programs. It also consists of auto-completion, code fixing, code editing, management and automated testing. A cloud-based IDE was deployed because it enables programmers to easily access their projects anywhere and at any time. Also, hardware limitation issues are eliminated with no setups and configurations required, as all the information is to be stored in the cloud. Hypertext Markup Language, Javascript, and Bootstrap Cascading Style Sheet were used to design the front-end interface, while Asynchronous Javascript, and Extensible Markup Language, Hypertext Pre-processor and My Structure Query Language were employed for the back-end design. Various users can be able to write, edit, debug, run, and store the codes on the server. The user can also use the IDE on any internet-enabled devices such as personal computers, mobile devices, and tablets.
 Keywords: Synchronous, integrated development environment, interface, online, communication.

Data analytics in wireless systems and IoT issues and challenges

SummaryThe emergence of data engineering along with the support of Online Social Networks is growing by millions every day due to the introduction of wireless systems and Internet of Things. The rapid growth of usage of smart devices helps to create new generation knowledge sharing platforms. Data Analytics has a major role to play in the growth and success of wireless and IoT applications. The growth of data has become exponential and is difficult to analyze. Many researchers depend on the data available on Wireless Systems and IoT for developing new generation services and applications. With the opportunity of information and communication technologies like heterogeneous networking, cloud computing, web services, crowd sensing and data mining, ubiquitous and asynchronous information sharing is feasible. But this also brings out a lot of provocations with respect to conflicting standards, data portability, data aggregation, data distribution, differential context and communication overhead. The smart information and communication technology has changed many features of human lifestyle: personal and work places.

Adaptive Observer-Based Output Regulation of Multiagent Systems With Communication Constraints.

In the absence of assuming that all agents know the system matrix of the external system, for heterogeneous linear multiagent systems (MASs), this article resolves the cooperative regulation problem in the presence of communication constraints. Based on the mild assumption on the digraph, two novel adaptive protocols are presented to solve the cooperative output regulation problem (ORP) via state feedback and measurement output feedback subject to snatchy and asynchronous information exchange between agents, unknown time-varying delays, and probable information losses. Finally, some simulations are offered to demonstrate the validity of our results about the problem.