Reviewerly/exHarmony huggingface dataset:
Multimodal object tracking by exploiting appearance and class information

In this work, we study the method exploiting natural language network to improve tracking performance. We propose a novel architecture which can combine class and visual information presented in tracking. To this end, we introduce a multimodal feature association network, allowing us to correlate the target class with its appearance during training and aid the localization of the target during inference. Specifically, we first utilize an appearance model to extract the target visual features, from which we obtain appearance cues, for instance shape and color. In order to employ target class information, we design a learned lightweight embedding network to embed the target class into a feature representation. The association network of our architecture contains a multimodal fusion module and a predictor module. The fusion module is used to combine features from class and appearance, yielding multimodal features with more expressive representations for the subsequent module. The predictor module is used to determine the target location in the current frame, from which we associate the class to the appearance. The class embedding module thus can learn appearance cues by exploiting the back-propagation functionality. To verify the abilities of our method, we select the official training and test splits of the LaSOT with annotated images and classes to perform experiments. In particular, we analyze the imbalance in the samples and employ a class validator discriminator to alleviate this problem. Extensive experimental results on LaSOT, UAV20L and [email protected] demonstrate our method achieves competitive results while maintaining a considerable real-time speed.
Registration of microtomography images: challenges and approaches

The quantification of appearance and geometrical changes between structures captured across several images relies on the determination of correspondences between these image structures. Automatic methods for establishing sparse and dense spatial correspondences are feature tracking and image registration, respectively. Main registration challenges are weak image features, complex appearance change, large shape changes, and huge images. These challenges can make the problem intractable. We present approaches to overcome these challenges for registration tasks including microtomography images. Applications include assessing tissue changes due to embedding material, comparison of microtomography with optical microscopy images, and optimization of process parameters for materials.
A steganography embedding method based on CDF-DWT technique for data hiding application using Elgamal algorithm

Steganography is a data hiding method used for security purposes. While hiding more data in the embedding process, the data may be lost and cause some security problems. The steganography embedding method is used to avoid this problem. In this manuscript, the steganography embedding method is proposed based on the Cohen-Daubechies-Feauveau Discrete Wavelet Transform (CDF-DWT) technique for data hiding applications using the Elgamal algorithm. The cover image is considered to hide the secret data and then the cover image edges are detected and filtered by using the Speeded-Up Robust Features (SURF) method. Elgamal algorithm is used to encrypt the input data. This method aims to safeguard the secret data using the steganography method with increase on embedding efficiency, embedding capacity, carrier capacity and reduce execution time. The proposed CDF-DWT technique with Elgamal algorithm shows better results with Peak to signal noise ratio (PSNR) of 45.78%, Mean Square Error (MSE) of 2.09%, and Bit Error Rate (BER) of4.56%, the execution time of 5.98%, Carrier Capacity of 85.90% and Embedding Capacity of 80.67%. The obtained values are compared with the existing state-of-the-art method in data hiding.
Research on Mahout-based personalised recommendation application for libraries

Personalised recommendation systems are widely used in various fields because they can provide personalised recommendation services to users based on their characteristics or historical behaviour data. This paper addresses the problem of low utilization of library resources due to the lack of personalized service capability in libraries, and implements personalized recommendation service in libraries through the collaborative filtering recommendation module provided by Mahout framework, and the test results also verify the initial personalized recommendation effect.
Efficient Exploration in Crowds by Coupling Navigation Controller and Exploration Planner

Autonomous exploration in scenes with moving pedestrians is critical for deploying autonomous mobile robots in populated places such as malls, airports, and museums. The existence of dynamic obstacles poses challenges on achieving an efficient, safe, and robust exploration system: the robot may get stuck in the pedestrians without making progress in scene coverage; it may collide with humans and hurt them; the human-robot collision will fail the exploration process or cause large drift and artifacts in simultaneous localization and mapping (SLAM). In this work, we propose a framework that can solve these challenges by tightly coupling a reinforcement learned navigation controller and a hierarchical exploration planner enhanced with a recovery planner. The navigation controller provides a value function describing the distribution of crowds around the robot, which will be leveraged by exploration planner and recovery planner to minimize the human-robot interruptions. We evaluate the proposed exploration framework against several methods on a set of indoor benchmarks with pedestrians, verifying the advantages of our method in terms of exploration efficiency, navigation safety, and SLAM quality.
A Study on the Quality Improvement of Language Teaching in the IoT Environment

IoT networks and Teaching System have attracted much attention from researchers of industry and academia. As a typical application of IoT tworks and Teaching System, the system studied in this paper is a three-way platform for IoT technology, language teaching methods, and language teaching quality is incorporated into the framework of language teaching system. Based on the current stage of language teaching methods in China and its digital needs, the role of IoT-based language teaching models on language teaching quality improvement is investigated by establishing an IoT language teaching model and using conventional comparison methods to compare conventional language teaching quality improvement models with language teaching models based on IoT platforms to consider the impact of IoT technology on language teaching quality improvement. The results show that in the educational context where digitalization and intelligence are the trends, the intelligent language teaching model integrating IoT technology is much better than the language teaching model under the conventional model in terms of teaching quality, and through IoT technology, more language enthusiasts can become language learners and improve their learning ability. The work in this paper provides an important application guidance to IoT networks and Teaching System.
Research of color-coded structured light based on convolutional code

Color Structured light is one of the important methods to obtain 3D information. Although it has the advantage of high efficiency, its reliability is easily affected by the ambient light and the color shape of the object to be measured. In this paper, the reason of low coding reliability is studied. Aiming at increasing the coding reliability of structured light, convolutional code is introduced into structured light three-dimensional measurement system to improve the reliability of system measurement. A new kind of color-coded structured light pattern is generated by combining convolution code with Gray code, which has the ability of correcting error bit at any position and has a relative high efficiency. In addition, the maximum likelihood decoding method using Viterbi algorithm is introduced as decoding method for convolutional code. Finally, the effectiveness of the coding and decoding scheme is proved by simulation experiments.
Multi-data fusion based omnidirectional/directional millimeter wave radar for blind-free detection

In omnidirectional/directional MMW radar multi-data fusion for road network operation monitoring technology, as the vehicle will be obscured while in motion. It causes the MMW radar sensor to fail to scan and detect the corresponding vehicle for a short or long period of time. This phenomenon can make the system unable to continuously track and locate a particular vehicle. Therefore, the paper investigates the compensation method for vehicle monitoring when obstacles and vehicles are obscured based on omnidirectional/directional MMW radars multi-data fusion, and proposes for the first time an omnidirectional/directional MMW radar vehicle breakpoint inertia compensation technique to pre-determine the trajectory, driving status and position of the lost vehicle. This ensures that in the event of a temporary loss of this target information, the data remains coherent and unchanged. The technical difficulties of obstruction and vehicle obscuration on highways are solved.
Spectrum analysis of the local fringe-frequency extraction method

The Window Fourier Ridge (WFR) is one of the useful methods in local frequency extraction from a fringe pattern. But the background of the pattern and the inherent noises existed in the pattern will disturb the extracted result, which need to be removed before operation. To solve this problem, we modify the WFR method by spectrum normalization of the fringe pattern in its frequency domain. Then, the accuracy of the extracted local fringe frequency is enhanced greatly due to the inhibition of the disturbance from its background. The principle of the normalized spectrum method is detailed by spectrum analysis of the fringe pattern. The conducted simulations and experiment results show that the local fringe-frequency estimated from the fringe pattern with background by the proposed method is as good as that from the fringe pattern without background by traditional WFR method.
Application of improved mayfly algorithm in image enhancement

Aiming at the problems of fuzzy boundary and color deviation in marine biological images, using the advantages of fast convergence speed and strong optimization performance of mayfly optimization algorithm, an improved mayfly algorithm (RCMA) based on reverse opposition-based learning is proposed in this paper. Firstly, the optimized initial mayfly population is obtained by reverse opposition-based learning to enhance the diversity of mayfly population and improve the convergence speed of the algorithm; Then the linear decreasing weight method is used to update the position of male and female mayflies, which reduces the risk of premature mayfly algorithm and avoids the oscillation phenomenon near the global optimal solution in the later stage of the algorithm; Finally, the double gamma function is introduced, and the improved mayfly algorithm is used to find the optimal gamma value, which is used to improve the contrast of the image and enhance the image. Simulation results show that the RCMA proposed in this paper has a certain enhancement effect.
Virtual Reality and Youth Incarceration: Methodological Reflections from a Media Education and Research Program

Background: This research aimed to provide young people in a juvenile rehabilitation centre (JRC) with access and exposure to virtual reality (VR) as a growing media technology industry, to offer media education that was fun and engaging, and to introduce the digital arts to participants as a potential career path. The project evolved through a partnership with the Washington State Librarian who, as a part of her role overseeing public library activities, wanted to ensure that newly acquired VR equipment was made available to as many people as possible. This effort included libraries within sites of incarceration. Analysis: This article presents a reflective and analytical discussion on the success and challenges of creating, implementing, and researching a VR art design program in a JRC. Conclusions and Implications: Carceral logics are entangled in research and education, in constant tension with anti-oppressive methods in place. Improvisational action as a design method in media education programs, including VR art design with incarcerated youth, may support greater participation and stronger research outcomes.
Design and optimization of Zstandard algorithm based on concurrent streaming of multiple hash tables

When compressing massive data, using software implementation of Zstandard (Zstd) algorithm will occupy a lot of processor resources, and it will lead to performance bottleneck due to serial data dependency, hardware acceleration of Zstd algorithm is an effective solution to the above problems. Therefore, in this paper, we propose a hardware architecture for concurrent streaming of multiple hash tables applicable to the Zstd algorithm. Using this design scheme, the RTL-based VCS simulation verification shows that the compression ratio almost reaches the standard of software, the compression speed of hardware is about 5.5 times of software compression speed, and the decompression speed of hardware is about 5 times of software, where the compression speed reaches up to 1.17GB/s and decompression speed reaches up to 1.89GB/s, and compared with the existing hardware implementation of Zstd, the compression speed is about 11.4% higher than that of the existing hardware implementation of Zstd.
Reading Between the Lines in Discussing Multicultural Books for Children: Preliminary Results

ABSTRACT Selecting multicultural books for children is very important because multicultural literature helps children understand the issues of cultural diversity and gender biases. This study is a part of a larger research project that aims to build a culture‐rich environment in organizing and selecting books for children. In this study, we investigate whether the sentiments in online reviews of children's multicultural literature would represent readers’ opinions on the books and whether readers’ positive, negative, or neutral attitudes would be directly associated with the overall ratings of books. The preliminary results show that the review rating scores did not accurately indicate readers’ minds in discussing books. This study has an implication of increasing awareness of the ethnic or racial minority themes in children's literature by uncovering readers’ minds in discussing children's multicultural books.
An <scp>Information‐Resilient Big‐Data</scp> Workbench with <scp>PDP‐DREAM</scp> Software

ABSTRACT PORTAL‐DOORS Project DREAM Software, available as an open‐source C#‐centric codebase from a Github public repository at PDP‐DREAM, implements the PDP‐DREAM principles and PDP‐FAIR metrics with web‐enabled workbench software for distributed data repositories in the Nexus‐PORTAL‐DOORS‐Scribe Cyberinfrastructure. PDP‐DREAM Software has been developed for Microsoft platform technologies with ASP.NET Core, SQL Server, and Internet Information Server. As a web‐enabled workbench, PDP‐DREAM provides many features for big data management with tools and services to support information resilience in defense of truth in science and integrity in research.
User Perception and Eye Movement on A Pandemic Data Visualization Dashboard

This study utilized a two-phase user experiment to explore people's perceptual and cognitive states interacting with the COVID-19 dashboard to obtain outbreak information. Specifically, 27 participants were assigned to interact with this dashboard with different color arrangements and performed image-memory, search, and browse visualization tasks sequentially. We found that the participants expected to obtain both global pandemic trends and single region/date statuses from the dashboard to help them grasp important information in the shortest possible time. They also allocated their attention differently to the dashboard's content areas to match their individual visual movement and reading logics. Our participants indicated that the pandemic data visualization dashboard should use a principal-color selection that is alarming but without causing panic. In the study's second phase, an eye-tracking experiment, it was found that the participants' actual eye paths deviated from our expectations: clustering around headings and text, rather than on visualized charts or graphs as anticipated. Based on these findings, we provide design implications for builders of future data-visualization and disaster dashboards.
Implementing Virtual Reality Training for Librarians: Supporting Patrons in Crisis

ABSTRACT This research proposes a study that is part of a larger, interdisciplinary, and multi‐institutional project that examines the usability of VR training for library and information science (LIS) graduate students and professionals in gaining skills for interacting effectively with patrons in crisis. This project is entirely novel and innovative in its approach. To date, no virtual reality training for crisis management in libraries exists. Additionally, we hypothesize that our project will ultimately enable us to: reach a wider audience with training; consider the usability of VR training in LIS education; introduce training in low‐stakes environments that does not pose harm to patrons in crisis; engage students in an immersive learning experience. This project can create possibilities for how we approach LIS education within our department and support training students in vital, but difficult to teach, soft skills such as empathy and communication (Saunders &amp; Bajjaly, 2021).
“Prove It!” A User Centered Design for the <scp>Blockchain‐Based</scp> Research Lifecycle Transparency Framework

ABSTRACT This work develops a user‐centric interface for an existing blockchain‐based research transparency framework. As researchers in different disciplines may have different research data management and reproducibility needs, we first conducted an exploratory study to comprehend their motivations and expectations when managing research records. We analyzed data from a design thinking workshop with 17 participants and conducted six interviews. We have three critical components in a user‐centric interface design: 1) user‐decided autonomy, 2) a progress dashboard, and 3) research‐team management. In the future, we would like to build an scholarly information ecosystem that fosters safe research lifecycle transparency with the assistance of technology. Thus, we plan to accumulate more interview data from different fields to improve the design of the user‐centric client.
Investigation of an innovative stitching-embedded characterization method for imprinting large-aperture continuous phase plates

In high-power laser systems, the large-aperture continuous phase plates (CPPs) are designed and fabricated with continuous varying structured topography to serve as the class of phase-modulating beam-conditioning optics. To achieve better optical performance, its fabrication highly depends on the reiterative figuring to eliminate the form error relative to the designed CPP surface. However, the characteristics of large aperture and complex structured topography make the quality inspection of CPPs still challenging. A typical inspection process includes measuring the CPPs with the sub-aperture stitching technique, followed by the characterization procedure with respect to the CPP design description. This paper proposes an innovative stitching-embedded characterization method for imprinting the large-aperture CPPs, which combines the sub-aperture stitching and characterization procedure together. First, the concept and mathematical model of the stitching-embedded characterization method is presented. Then, both numerical simulations and experiments were undertaken to verify the proposed method, and the results demonstrate its feasibility and reasonability to provide the more reliable form error evaluation while simplify the analysis procedure compared with the conventional inspection process.
Designing a Mobile Game for Introducing Learners to a Soap Making process

Bunno's Fabulous Soap-Making Challenge is intended to be both, a game that is played for fun and a game from which subject content can be learned. The game is modeled on and represents authentic, real-world chemical processes. Specifically, it promotes the learning of aspects central to the soap-making process. The game is a resource-managing game in which players plan, organize, and execute the production of soap. Players source the raw materials, acquire the technical equipment, create an efficient lab setup and produce and sell soap in an economically sustainable way. The game is centrally based on the idea of constructivist learning. Players encounter an inspiring and challenging situation and are active, in control, and make their own decisions and experiences. Their actions trigger immediate responses and are consequential. The main contribution of this article is a detailed description, a conceptual explanation, and a critical discussion of the game design. In addition, this article briefly describes the educational theory which informs the project, how the game design is actually realized in the implemented game and how it can be played, and the game's educational content and the projected learning outcomes.
Strategic Framework Towards Open Science Adoption in Malaysia

Open science is one of the key features of data-intensive research methods. Despite its manifold benefits, the interest and willingness of Malaysian researchers to share their research data are noticeably low. Likewise, Malaysia has not performed well in recent international open data assessments. Hence, The Ministry of Science, Technology and Innovation (MOSTI) addressed the Malaysia Open Science Platform (MOSP) as one of the key strategies in National Science, Technology, and Innovation Policy 2021-2030. This study aimed to determine the organizational enablers that facilitate the delivery of MOSP strategic management by employing three qualitative approaches: literature review, desk research and semi-structured interview. This study determined leadership, people and cooperation as soft enablers while technology and systems as hard enablers. The change and stakeholder management were proposed as success factors in delivering these variables. A strategic framework was also presented as a foundation for MOSTI in formulating a robust and comprehensive strategy.
Design Thinking Framework for Values Capturing and Technical Features Validation for Digital Healthcare Platform

For practitioners and platform developers, capturing values and validating of technical features of a large healthcare eco-system is a great challenge, due a large number of stakeholders and their complex relationship. This paper addresses this challenge by utilizing the design thinking as a framework. However, although design thinking is a well-proven customer-centric tool, implementation for a large platform is difficult. To solve this, a workflow incorporated stakeholder mapping, customer journey mapping, and value proposition canvas is proposed ensuring a systematic empathy, insights extraction, and ideation for technical features.As a study case, the proposed method is experimented during the development of the Thailand Health Dataspace (THDS), a digital healthcare platform enabling an integration of third-party stakeholders to public health dataspace. 529 participants from different stakeholder groups were involved. The proposed method has successfully provided a set of technical features while captured the values which each stakeholder is expecting from THDS.