Reviewerly/exHarmony huggingface dataset:
Video Frame Interpolation Based on Lightweight Convolutional Unit and Three-scale Encoder

Video frame interpolation (VFI) achieves temporal super-resolution by synthesizing intermediate frame between two original adjacent frames. This paper proposes a lightweight VFI network based on lightweight convolutional unit and three-scale encoder. We first introduce a three-scale encoding-decoding structure with two-level attention cascades to represent the multi-scale motion information. Then, recurrent convolutional layer (RCL) and residual operation are adopted to design the recurrent residual convolutional unit (RRCU) to refine the three-scale structure. Finally, we propose a lightweight unit by combining the depth separable convolution and RRCU, and introduce the local lightweight idea to reduce the model parameters. Experimental results show that the proposed method achieves a better performance against the state-of-the-art methods with fewer parameters.
An Efficient Federated Learning Method Based on Optimized-residual and Clustering

With the development of IoT and big data, communication overhead becomes a major performance bottleneck in federated learning, it is especially important to have an efficient federated learning method that can reduce the communication cost. Previous works used parameter compression methods to reduce the communication cost, but they did not notice that parameter compression leads to a decrease in model accuracy and convergence speed. To address this issue, an efficient federated learning method based on residual optimization and clustering is proposed, which achieves the reduction of communication cost while guaranteeing the model accuracy and convergence speed. Experimental results on the MNIST dataset show that the convergence speed is improved by 18.9% and the communication overhead is only 62.5% compared with the most advanced FedZip scheme, while the accuracy is guaranteed.
Dual Discriminative Relative-of-Gaussians Prior for Blind Deblurring

This paper proposes a novel dual discriminative image prior for blind deblurring. The desired discriminativeness between salient edges and faint textures as well as a sharp and blurred image has been both elegantly formulated via use of relative-of-Gaussians. Utilizing the prior in the maximum-a-posterior estimator, blind deblurring is then transformed into a simple yet intuitive image filtering task via the well-known half-quadratic regularization (HQR). Experimental results on both synthetic and realistic blurred images well demonstrate the practicality of the proposed prior and also its comparable or superior performance to existing state-of-the-art ones.
Learning Visual Representation of Underwater Acoustic Imagery Using Transformer-Based Style Transfer Method

Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering. Although deep learning brings opportunities for target recognition on land and in the air, underwater target recognition techniques based on deep learning have lagged due to sensor performance and the size of trainable data. This letter proposed a framework for learning the visual representation of underwater acoustic imageries, which takes a transformer-based style transfer model as the main body. It could replace the low-level texture features of optical images with the visual features of underwater acoustic imageries while preserving their raw high-level semantic content. The proposed framework could fully use the rich optical image dataset to generate a pseudo-acoustic image dataset and use it as the initial sample to train the underwater acoustic target recognition model. The experiments select the dual-frequency identification sonar (DIDSON) and adaptive resolution imaging sonar (ARIS) as the underwater acoustic data source and also take fish, the most common marine creature, as the research subject. Experimental results show that the proposed method could generate high-quality and high-fidelity pseudo-acoustic samples, achieve the purpose of acoustic data augmentation and provide support for the underwater acoustic-optical images domain transfer research.
Global Calibration of Double Sensors Vision Measurement System Based on Planar Mirrors

Limited by the measurement method and the shape of the measured object, the three-dimensional (3D) scanning equipment can only obtain the point cloud within a limited range of the object. Therefore, a measurement system with two vision sensors placed asymmetrically orthogonally is constructed for the 3D defect detection of industrial parts. Using a single planar target directly for global calibration has low calibration accuracy, so the existing methods typically rely on high-precision external auxiliary devices. The goal of this paper is to estimate the relative pose between the two sensors with a large working distance difference and an angle of approximately 90 degrees between the two optical axes. A mathematical model of the feature extraction error of circle array target in various poses is established. The influence of different target poses on the accuracy of extrinsic parameters solution is comprehensively evaluated using error propagation theory. A global calibration method based on planar mirrors is proposed for a double sensors vision measurement system. This method solves the problem that two cameras cannot simultaneously acquire good images of the same target. Moreover, it does not need to use complex targets and high-precision measurement equipment. The calibration process is flexible and simple, and high calibration accuracy can be achieved. The experimental results show that the translation measurement errors are 0.088 mm, 1.102 mm and 0.164 mm respectively, and the rotation angle measurement errors are 0.057 degrees, 0.013 degrees and 0.014 degrees respectively.
Research on the application of AR technology in reconstructing the digital presentation of urban identity

Augmented reality (AR) technology is playing an important role in the ever-changing conservation of cultural heritage. As economic growth gathers pace, urban living spaces are increasingly being reorganised, and this raises the question of how people can find a sense of identity and community in the new urban settings. To enable people to fully understand their cultural heritage and help them discover a sense of identity in the changing urban social landscape. This will enable people to fully understand their cultural heritage and help them to discover a sense of identity in the changing urban social landscape. This study expanded the effect of popular science education, designed by using AR coding, and set out to evaluate its impact on people reconstructing their identity and sense of belonging to a community in a reconfigured Chinese city. Before developing the design program, using AR coding, the first step was to analyse examples of AR application and coding in existing popular science education. On completing the analysis, we tested our implemented system by carrying out a series of experiments, focussing on a survey of 918 vocational school students in China. The survey gathered data on satisfaction levels, and a statistical analysis was undertaken to determine the correlation between the competence of the students and the degree of satisfaction they reported. The results demonstrated that AR popular science education has the most significant effect on students’ overall educational satisfaction - followed by common competence, and finally information competence. The new program evidenced that it has the potential to improve student competence, and that AR coding can make a major contribution to design education.
Reversible Data Hiding in Encrypted Images Combined with Chaotic System Based on Pixel Prediction and Bit Plane Compression

Chaotic systems have various advantages, such as unpredictability, non-periodicity, and so on. The algorithm for encrypting digital images using chaotic systems has a large key space, sensitive initial values, and fast encryption speed. Therefore, in this paper, we propose a reversible data hiding in encrypted images (RDHEI) combined with a chaotic system. The algorithm introduced the SHA-256 method to generate the initial value of the chaotic system and the key of RDHEI by manipulating the plaintext image. Moreover, the chaotic system is used to further encrypt the encrypted image after the data hiding in the RDHEI process. The improvement enhances the sensitivity and security of the key and strengthens the image encryption. The experimental results evaluated by histogram, information entropy, correlation of neighboring pixel points, and other indicators show that the proposed algorithm has comparable performance with state-of-the-art and has more intense encryption.
Power Equipment Nameplate Text Detection Based on Improved Multiscale Feature Fusion Network

Power nameplate information is essential for managing and maintaining power equipment. In response to issues such as low contrast and high text density in text detection for power nameplates. To address these issues, we propose a text detection algorithm for power nameplates based on the DBIR network (Differential Binarization with Inception-ResNet v2), which integrates multi-scale feature fusion and a Fusion Attention Module (FAM). The DBIR network can learn features of different scales and use the FAM module to fuse deep and shallow features, thus capturing more spatial location information of the text and improving text localization accuracy. Given the current absence of publicly available power nameplate datasets, we propose a hybrid dataset that combines power nameplates with natural scenes. This approach effectively simulates power scenarios and enhances the generalization capability of the model. We evaluated the model on several datasets, including the power nameplate dataset, and obtained promising results. Specifically, the DBIR network achieved a detection accuracy of 87.1%, a recall of 83.2%, and an F-measure of 85.1%.
Enhancing CNN-based Face Mask Detection and Usage Classification using Capsule Networks

Convolutional Neural Network is a popular algorithm for image and audio processing. However, it contains drawbacks of inefficient classifications with positional and angular differences. Therefore, the Capsule Network algorithm was used to enhance the classification of the type and usage of face masks. The study utilized 9872 images to process, using brightness, contrast, and horizontal flips for data augmentation. The results showed a slightly better performance in CapsNet vs. CNN ResNet-50. The model was improved by training the model in equal images per classification and getting the best epochs and batch size hyperparameters. It is further validated through video input of surroundings in areas of the Philippines and found issues such as misclassification of dark-colored masks, skin-toned masks, and far subjects. It is recommended to increase the dataset size, especially with misclassified cases, change the model's architecture, and try comparisons with other algorithms.
Violence Detection Using Affective Features from Upper Body Expressions

In recent years, places such as elderly care houses, nursing homes, schools, prisons, psychic wards are prone to aggressive and violent and abusive interactions. This leads to the need for a system that could automatically detect such abhorrent behaviours. With the rapid increase in the use of surveillance cameras, the implementation of such a system can help in deterring such intolerable incidents and thus providing a much safer environment. In the paper, a violence detection system is proposed using affective features for the anger emotion in order to enhance to efficiency of the existing violence detection systems. OpenPose toolbox is used for extracting 2D skeleton keypoints from the RWF2000 dataset and MATLAB Classification Learners app is used for classification, producing encouraging results.
Learned Structure-Based Hybrid Framework for Martian Image Compression

Recent landing marches on Mars have enabled the access to Martian surface images, which act as an important vehicle to demystify the evolution and habitability of Mars, in terms of climate, geography, etc. Transmitting Martian images thus calls for efficient compression methods to ensure the high-quality reconstruction from distant communication, in which the research is yet to start. To address this issue, we propose in this paper a learnt structure-based hybrid (LSH) framework to compress Martian images. More specifically, we first observe that the structural consistency exists across Martian images, which motivates us to propose a structural compression network (SCN). The aim of SCN is to compactly represent the structural information of Martian images, thus allowing for the compression at extremely low bit-rates. Then, we propose a detail compensation network (DCN) to reconstruct the missing details when we restore from the structural information, which benefits from improved compression efficiency by reduced bit-rates. The experimental results have verified the superior performances of our LSH method on compressing Martian images, against existing state-of-the-art methods.
Designing and Evaluating Interactions for Handheld AR

Mobile devices such as phones and tablets have become the most prevalent AR devices and are applied in various domains, including architecture, entertainment, and education. However, due to device-specific characteristics, designing and evaluating handheld AR interactions can be especially challenging as handheld AR displays provide 1) limited input options, 2) a narrow camera field of view, and 3) restricted context awareness. To address these challenges with design recommendations and research implications, the dissertation follows a mixed-methods approach with three research questions (RQs): On the one hand, specific aspects of fundamental 3D object manipulation tasks (RQ1) and awareness and discoverability (RQ2) are explored and evaluated in controlled lab studies. For increased ecological validity, the developed interaction concepts are also investigated in public interactive exhibitions. These studies then inform the creation of a framework for designing and evaluating handheld AR experiences using VR simulations of the interaction context (RQ3).
iOS Augmented Reality Application for Immersive Structural Biology Education

In this day and age, there is a need for more tools in education that take advantage of the new frontiers of technology. Students have grown up in a digital world and can quickly adapt to new methods of learning. XR, or extended reality, involves the use of technology to upgrade the physical world. Augmented reality (AR), in particular, has a nice balance of enhancing the physical world with digital models while also remaining convenient and accessible since it can be done through a smartphone. This study looked at whether an AR iOS application could be an effective and engaging learning tool. The application was designed to be an augmented reality game and exergame by being a scavenger hunt that accompanied a tour. Preliminary results reveal that participants enjoyed the game and found that it made the tour more educational and slightly more engaging.
Lightweight Personnel Detection Network Based on Reinforcement Feature Learning

Object detection is an important research branch in the field of computer vision, and personnel detection has rich application scenarios and use values. Deep learning is widely used in the field of personnel detection, but the traditional convolutional neural network is complex and needs the support of high computing power GPU, which is difficult to deploy on embedded devices. At the same time, the lack of feature information caused by too small scale or occlusion is the main reason for the reduction of personnel detection accuracy. To solve these problems, this paper proposes a lightweight personnel detection network based on reinforcement feature learning; At the same time, convolution can be separated by the depth of channel mixing mechanism to further reduce network parameters; Secondly, this paper designs a hole convolution module to obtain more discriminative feature information, and uses the hole space pyramid pool structure and the attention mechanism with position information to carry out effective feature fusion, thus improving accuracy and reasoning speed. Experiments on multiple data sets and multiple hardware platforms show that the proposed algorithm is better than the original YOLOv4 micro-network in terms of accuracy, speed, model parameters and volume, and is more suitable for deployment in embedded devices with limited resources.
Exploring and Evaluating the Potential of 2D Computational Notebooks

Computational notebooks are popular tools for data science and presentation of computational narratives. However, their 1D structure introduces and exacerbates user issues, such as messiness, tedious navigation, inefficient use of large screen space, performance of non-linear analyses, and presentation of non-linear narratives. In this Ph.D., we address these issues through the design, exploration, and evaluation of computational notebooks which use 2D space to organize cells, or 2D computational notebooks. Specifically, we explore whether users would use 2D space, design and evaluate a 2D computational notebook prototype for individual work, explore how users collaborate in 2D space for data science and education, create and validate a theoretical understanding of how nonlinear processes in data science cause problems when forced into a linear, 1D computational notebook, and build upon the foundation we have made to refine 2D computational notebooks. Our work contributes insights on if and how expanded space usage can improve computational notebooks.
Exploring Human Values in Mixed Reality

Immersive technologies such as mixed reality (MR) are gradually emerging from laboratory testing to real-world applications. However, the real-world applications of MR technologies raise more complex human-value issues than in laboratory scenarios. The aim of this Ph.D. research is to understand human values in the MR context on different population groups and how to address future challenges. The main contributions of this research are (1) a review of human values in mixed-reality literature, (2) a designed workshop for exploring values in MR, and (3) insights for incorporating human values in MR applications.
Algorithmic Parody Protection in the European Union: CDSM Directive and DSA Regulation Perspective

Abstract This research aims to evaluate the CDSM Directive and DSA regulation effectiveness in protecting the EU copyright exception for parody, caricature, and pastiche set in Article 5 (3)(k) of the InfoSoc Directive and capture quintessential issues of the regulation that make the conditions for potentially reduced ability to disseminate creative parody content in the online content-sharing service platforms in the EU. The subject of this paper is the complex relationship between algorithmic content recognition tools and creative parody content qualification. Based on EU copyright law doctrinal consensus, a legislative move to introduce liability for copyright-infringing content to online content-sharing service providers induces algorithmic content moderation on digital platforms. The triangular challenge to balance EU copyright and exception protection without disrupting the online content-sharing service leads to algorithmic recognition-based parody recognition solutions.
MindfulBloom: Spatial Finger Painting for Mindfulness Intervention in Augmented Reality

Mindfulness interventions emphasize the importance of being present in the moment. While many digital mindfulness tools are limited to flat screens, extended reality (XR) offers more immersive solutions. However, existing XR mindfulness practices often lack the connections to user’s real-world present moment, essential for mindfulness. We introduces MindfulBloom, a novel approach merging augmented reality (AR) and finger painting interaction for digitally supported mindfulness. Building on studies that highlight finger painting’s therapeutic potential, our system guides users through an intuitive finger painting interaction in their workspace, translating stress clutters into spatial, artistic expressions. We explores its potential to direct body attention to the present moment, offering a creative solution to the in-the-moment mindfulness interventions.
Sentence Compression Using Natural Language Processing

Using extractive text summarization, we attempt sentence compression in this study. As a result, we can train a deep learning model to complete the job by rewriting it as a multi-label deletion-based issue. We can create summaries that are mainly grammatically sound and useful with fewer training examples and yet earn F1 scores that are comparable to those from previous exams. Similar outcomes were obtained using a (Proof of Concept) POC of the model on a confidential dataset held by the author's employer. We describe a unique unsupervised sentence compression approach that uses Stanford Typed Dependencies to extract information items and an NLP sentence compression engine to produce compressed phrases. An automated examination demonstrates that our strategy yields superior outcomes.
The Utilization of Geographic Information Systems for Settlement Location Determination in Supporting Sustainable Development: Study Case in Sedayu Sub-District, Bantul Regency, DIY

Abstract The phenomenon of unsuitable settlement becomes a major issue in environmental problems in big cities. The purpose of this study is to provide innovation design recommendations for determining the location of appropriate settlements to realize the implementation of smart cities. The resulting output of this study is an innovation in settlement location determination. The innovation using a geographic information system equipped with a variety of spatially processed information with overlay techniques. The results of the geographic information system process are then extracted into a mobile application design. Study case in Sedayu District show that the area consists of 36.9% of the area is not suitable for analysis; 36.4% of the area is not suitable for use, and 26.7% of the area is suitable for use. Mobile application design in this study called Smart Application for Smart Resident with two main features, My Contribution (KOKU) and Recommendations and Settlement Locations feature. This innovation can help the community by providing information and recommendations for suitable, less suitable, and unsuitable settlement locations. Thus, the existence of the result study can be a consideration of the community in determining the location of settlements by aspects of sustainable development.
Implementasi Metode Cluster Analysis K-Means dalam Segmentasi

UMKM smilax ointment is a business that sells goods in the form of ointments that are useful for the male reproductive organs. These MSMEs still use traditional business strategies and use computer technology only for database creation. Smilax ointment UMKM product is Smilax cream which consists of 3 packages, namely 100 grams, 80 grams, and 40 grams. The research was conducted using the UMKM database and the clustering method used was k-means clustering because K-means is non-hierarchical in accordance with the data obtained from UMKM. In addition, k-means is able to classify data very quickly compared to fuzzy k- means[1]. This research includes the stages of data collection, preprocessing, analysis process (k-means), evaluation, and conclusions. The results of the clustering classification which was carried out four times based on the type of packaging, obtained a value of k = 2 where the cluster "0" is the unproductive region/city and cluster "1" is the most productive region/city, namely Jakarta and Jayapura both for the 100 gram, 80 gram, packaging type. and 40grams.