Reviewerly/exHarmony huggingface dataset:
Resolution adjustable Lissajous scanning with piezoelectric MEMS mirrors

We previously designed a dual-axis piezoelectric MEMS mirror with a low crosstalk gimbal structure, which is utilized as the key device for further research for laser beam scanning. This paper mainly focuses on studying the Lissajous scanning resolution of this MEMS mirror with frequency ratio and phase modulation. For accurately evaluating the scanning resolution, the center angular resolution of Lissajous scanning is redefined by theoretical calculation and verified with experimental measurement. Meanwhile, the scanning nonlinearity of MEMS mirror is studied carefully. Finally, the MEMS mirror works at the state of pseudo-resonance, and the center angular resolution better than 0.16° (H) × 0.03° (V) is achieved at a scanning Field of view (FoV) of 35.0° (H) × 16.5° (V). Moreover, a feasible route of resolution adjustable Lissajous scanning is provided by optimization of frequency ratio and phase modulation, which is helpful for high definition and high frame rate (HDHF) laser scanning imaging with the dual-axis mirror.
Xsickness in Intelligent Mobile Spaces and Metaverses

Motion sickness is known to be a common problem that influences the comfort and work efficiency of human beings during their daily lives. With the proliferation of increasingly intelligent systems, the detection and mitigation of motion sickness will face more opportunities along with bigger challenges. On the one hand, the technology for integrated sensors in the intelligent system will provide more accurate and efficient methods for motion sickness detection. However, on the other hand, since cyber-physical systems have been gaining increasing concerns in the past two decades, the cyber-physical-social systems introduce and augment the social characteristics of such systems. The interactions between physical space and cyber space increase the chance of sensory conflicts when people use intelligent systems, such as traveling in intelligent cockpits or using metaverse-related virtual reality devices. The multimodal interaction methods and larger screens will cause more sensory conflicts. The symptoms will be more severe compared to traditional motion sickness. In this article, the classifications are first introduced based on the causes of motion sickness. A new type of multifactorial motion sickness (Xsickness) is discussed, which is foreseeable to be common with intelligent development. Then, the current state-of-the-art detection methods for motion sickness and cybersickness are summarized and theoretical methods for Xsickness detection are discussed. Finally, the mitigation methods based on motion reduction and four means of human perception are discussed and the innovative mitigation methods based on the intelligent system are also introduced.
Dense Three-Dimensional Color Reconstruction with data Fusion and Image-Guided Depth Completion for Large-Scale Outdoor Scenes

Accurate three-dimensional (3D) city models are critical for applications, such as digital city, urban planning and geo-graphic surveying and mapping. The synergy of deep learning and LiDAR-based Simultaneous Localization and Mapping (SLAM) has achieved some success in obtaining dense 3D color maps for large-scale outdoor scenes. In such a frame-work, this paper proposes a sensor-fusion system composed of a solid-state LiDAR, an inertial measurement unit (IMU) and a monocular camera by introducing image-guided depth completion into LiDAR SLAM. In the proposed system, the LiDAR-IMU odometry constructs the geometry structure of 3D maps, and subsequently, the images are used to render the texture of 3D color maps and guide the depth completion. In particular, the proposed system generates denser point clouds by exploiting the solid-state LiDAR before further increasing the density of point clouds via the depth completion. Our ex-perimental results show that the proposed system can achieve impressive 3D color models for large-scale outdoor scenes through faster scan and reconstruction process.
Boosting View Synthesis with Residual Transfer

Volumetric view synthesis methods with neural representations, such as NeRF and NeX, have recently demonstrated high-quality novel view synthesis. However, optimizing these representations is slow, and even fully trained models cannot reproduce all fine details in the input views. We present a simple but effective technique to boost the rendering quality, which can be easily integrated with most view synthesis methods. The core idea is to transfer color resid-uals (the difference between the input images and their re-construction) from training views to novel views. We blend the residuals from multiple views using a heuristic weighting scheme depending on ray visibility and angular differ-ences. We integrate our technique with several state-of-the-art view synthesis methods and evaluate the Real Forward-facing and the Shiny datasets. Our results show that at about 1/10th the number of training iterations, we achieve the same rendering quality as fully converged NeRF and NeX models, and when applied to fully converged models, we significantly improve their rendering quality.
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain

JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems. However, most recent progress on image compression mainly focuses on uncompressed images while ignoring trillions of already-existing JPEG images. To compress these JPEG images adequately and restore them back to JPEG format losslessly when needed, we propose a deep learning based JPEG recompression method that operates on DCT domain and propose a Multi-Level Cross-Channel Entropy Model to compress the most informative Y component. Experiments show that our method achieves state-of-the-art performance compared with traditional JPEG recompression methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this is the first learned compression method that losslessly transcodes JPEG images to more storage-saving bitstreams.
A Proposed Cognitive Framework Model for a Student Support Chatbot in a Higher Education Institution

The proposed student support chatbot is a framework model that enables the discovery of theoretical and conceptual design. It is aimed at responding to students' frequently asked questions (FAQs) about pedagogical information, including course registrations, fees, exams, course assessments, grades, appeals against the grades, and anything related to students in a higher education institution (HEI). All the student-related information can be implemented as a real-time conversational system on the web or on a mobile platform of any HEI, such as institutional websites, mobile apps, or integrations with their social media like WhatsApp, Twitter, Facebook Messenger, or Instagram. This design intends to provide cognitive services by identifying the benefits, challenges, and various ways of implementation. Most students individually ask the same question repeatedly to their advisors and course lecturers, which could be discussed and explained earlier, in which the faculties are spending too much time responding to their advisees' or students' questions. The cognitive services include different languages for text-based, voice-based, and graphical user interface requests with sensitive as well as non-sensitive requests. Thus, a student support chatbot is recommended for any HEI to minimize the time spent repeatedly answering questions, and this helps the staff, advisors, and faculties utilize the time on other academic performance.
HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs

We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of which each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach—dubbed HybridFC—that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC .
Ego+X: An Egocentric Vision System for Global 3D Human Pose Estimation and Social Interaction Characterization

Egocentric vision is an emerging topic, which has demonstrated great potential in assistive healthcare scenarios, ranging from human-centric behavior analysis to personal social assistance. Within this field, due to the heterogeneity of visual perception from first-person views, egocentric pose estimation is one of the most significant prerequisites for enabling various downstream applications. However, existing methods for egocentric pose estimation mainly focus on predicting the pose represented in the camera coordinates from a single image, which ignores the latent cues in the temporal domain and results in less accuracy. In this paper, we propose Ego+X, an egocentric vision based system for 3D canonical pose estimation and human-centric social interaction characterization. Our system is composed of two head-mounted egocentric cameras, where one is faced downwards and the other looks outwards. By leveraging the global context provided by visual SLAM, we first propose Ego-Glo for spatial-accurate and temporal-consistent egocentric 3D pose estimation in the canonical coordinate system. With the help of an egocentric camera looking outwards, we then propose Ego-Soc by extending Ego-Glo to various social interaction tasks, e.g., object detection and human-human interaction. Quantitative and qualitative experiments have been conducted to demonstrate the effectiveness of our proposed Ego+X.
A Privacy-Preserving Credit Bank Supervision Framework Based on Redactable Blockchain

Credit bank plays a vital role in constructing a lifelong education system. However, it confronts many security and privacy issues, and it is difficult to maintain the records. Thus, the blockchain technology has been introduced to achieve automatic management of credit bank. Unfortunately, the immutable nature of blockchain makes it impossible for students to delete their private data when they leave, which would bring data leakage risks. As an effective way to protect private data, redactable blockchain allows user to modify the data on the blockchain. However, existing redactable blockchains lack a trusted third party regulatory mechanism to ensure the legality and compliance of delete operations. To addresses these issues, based on redactable blockchain and threshold signature, this paper provides a data management scheme with privacy protection for credit bank, which supports external supervision, and allows secure and efficient deletion of student’s private data on the blockchain while ensuring the privacy data of students. Theoretical analysis and experiments show that the proposed scheme can effectively resist malicious behaviors and has practical performance.
End-to-end UAV obstacle avoidance decision based on deep reinforcement learning

Aiming at the problem that the traditional UAV obstacle avoidance algorithm needs to build offline three-dimensional maps, discontinuous speed control and limited speed direction selection, we study the end-to-end obstacle avoidance decision method of UAV continuous action output based on DDPG(deep deterministic policy gradient) deep reinforcement learning algorithm. Firstly, an end-to-end decision control model based on DDPG algorithm is established. The model can output continuous control variables, namely UAV obstacle avoidance actions, according to the continuous state information perceived. Secondly, the training verification is carried out on the platform of UE4 + Airsim. The results show that the model can realize the end-to-end UAV obstacle avoidance decision. Finally, the 3DVFH(three dimensional vector field histogram) obstacle avoidance algorithm model with the same data source is compared and analyzed. The experiment shows that DDPG algorithm has better optimization effect on the obstacle avoidance trajectory of UAV.
Optimization of Classification Accuracy Using K-Means and Genetic Algorithm by Integrating C4.5 Algorithm for Diagnosis Breast Cancer Disease

Technological development resulted in data proliferating. The data is processed into valid information for daily needs. Data mining is a technique to convert data into useful information. Data mining has been widely used in performing prediction functions, for example, health and medical science. This study using Wisconsin Diagnostic Breast Cancer dataset taken from UCI Machine Learning Repository. The dataset has 32 attributes with 569 samples. This data has a continuous and high dimensional data type, and it makes the C4.5 algorithm need long computation time and extensive storage. This study aims to improve the accuracy of the C4.5 with a combination of K-Means and Genetic Algorithm. These study results compared the accuracy of the C4.5 algorithm before and after applying the combination of K-Means and the Genetic Algorithm for diagnosing breast cancer. The accuracy of C4.5 is 91,228%. Meanwhile, the accuracy of C4.5 after optimized using the K-Means and Genetic Algorithm is 94,824%, with the average number of features are selected 22 features. Thus, the application of K-Means and Genetic Algorithm on the C4.5 Algorithm can improve the accuracy of diagnosing breast cancer by 3,596%.
Secure Distributed Outsourcing of Large-scale Linear Systems

Solving the system of linear algebraic equations (LAE) is the most well known and probably the most important of all numerical computations involving real numbers. Researchers have been committed to developing distributed algorithms to solve such systems for a long time. However, traditional distributed algorithms have serious security risks when the coefficients and the solution are of great value. To address the privacy issue, we propose a new secure distributed outsourcing protocol for solving large-scale LAE systems. Specifically, we give an algorithm for generating a generic repeatedly jointly strongly connected sequence, for the first time as far as we know. Then we embed the matrix masking technique in our distributed system requiring multiple rounds of iteration while keeping the correctness of convergence. In addition, for the first time, we give a method for discriminating by the agents under masking whether the plaintext approximate solution corresponding to the current masked solution satisfies a predetermined constraint. Finally, we give the experimental result to show the practicality of our new protocol.
Mining Opinion Based on Tweets about Student Exchange with Tweepy and TextBlob

To find out people's opinions about student exchange, this research was made to find out by taking a data set from Twitter with a machine learning calculation within the form of Tweepy and after that handling it with TextBlob. TextBlob is one of the libraries in python 2 and python 3 which is used to process textual data. After getting the results, it is expected to use the data to help the community know what student exchange is and its benefits. Social media (Facebook, Instagram, Twitter, etc.) these days can be utilized for analysing the objects, e.g. political sees, items, administrations, etc. Social media is widely used at this time and we choose twitter because twitter is a social media application that is popular in the world and indeed a lot of research using twitter. Twitter had become the third-highest social organization in January 2009 from its previous position of twenty-two. Currently, more than 500 million selected clients in 2012, generating more than 340 million tweets. The conclusion is that there are still many people who do not know what student exchange is as much as 22% and 54% consider student exchange to be negative because they do not know the benefits of student exchange. Positive knowledge about student exchange is only 24%, this will support further research of creating a knowledge management system on student exchange. So hopefully that many people are interested in joining the student exchange program.
Low-Dimensional Space Modeling-Based Differential Evolution for Large-Scale Global Optimization Problems

Large-Scale Global Optimization (LSGO) has been an active research field.Part of this interest is supported by its application to cutting-edge research such as Deep Learning, Big Data, and complex real-world problems such as image encryption, real-time traffic management, and more.However, the high dimensionality makes solving LSGO a significant challenge.Some recent research deal with the high dimensionality by mapping the optimization process to a reduced alternative space.Nonetheless, these works suffer from the changes in the search space topology and the loss of information caused by the dimensionality reduction.This paper proposes a hybrid metaheuristic, so-called LSMDE (Low-dimensional Space Modeling-based Differential Evolution), that uses the Singular Value Decomposition to build a low-dimensional search space from the features of candidate solutions generated by a new SHADE-based algorithm (GM-SHADE).GM-SHADE combines a Gaussian Mixture Model (GMM) and two specialized local algorithms: MTS-LS1 and L-BFGS-B, to promote a better exploration of the reduced search space.GMM mitigates the loss of information in mapping high-dimensional individuals to low-dimensional individuals.Furthermore, the proposal does not require prior knowledge of the search space topology, which makes it more flexible and adaptable to different LSGO problems.The results indicate that LSMDE is the most efficient method to deal with partially separable functions compared to other state-of-the-art algorithms and has the best overall performance in two of the three proposed experiments.Experimental results also show that the new approach achieves competitive results for non-separable and overlapping functions on the most recent test suite for LSGO problems.
Understanding Toxicity Triggers on Reddit in the Context of Singapore

While the contagious nature of online toxicity sparked increasing interest in its early detection and prevention, most of the literature focuses on the Western world. In this work, we demonstrate that 1) it is possible to detect toxicity triggers in an Asian online community, and 2) toxicity triggers can be strikingly different between Western and Eastern contexts.
The method of redundant design schemes and the method of contour minimization of group water supply and sewerage systems

Due to the uneven distribution of water resources across the territory of Russia, many settlements and even cities do not have own sources of water supply and reservoirs, where treated wastewater could be discharged. To solve this problem, group and district water supply and sewerage systems are designed, built, and developed, hundreds and even thousands of kilometers long. The construction and operation of such facilities require significant financial investments every year. Therefore, the issues of the choice of routes, the structure of facilities, and particularly the methods of transporting water and wastewater, justifying the locations of water intakes and treat treatment facilities are relevant and require special attention and development of a comprehensive methodology for optimizing the structure and parameters of water supply and sewerage systems. The paper proposes, on the basis of a previously constructed redundant graph, the method of contour minimization of the structure of facilities and ways of supplying water to end users, transporting wastewater to treatment plants by pipelines and trucks. The redundant graph includes all kinds of connections (edges, arcs) that simulate pipelines and trucks, between vertices that simulate existing and new sources of water and wastewater discharges, sewage treatment plants, pumping stations, control tanks, and end users. The method selects the best (from the point of view of minimal life cycle costs) edges, arcs, and vertices of the graph and determines the corresponding parameters of pipeline and road sections of the network and facilities. This method is based on the procedure of traversing the contours and sequentially replacing the branches of the tree with moves, while the best option is remembered in the form of a spanning tree. Based on the results of the performed numerical experiments, it was shown that the use of combined (pipeline and truck) systems for transporting water in terms of life cycle costs is optimal.
Exploring Natural Language Processing in Model-To-Model Transformations

In this paper, we explore the possibility to apply natural language processing in visual modelto-model (M2M) transformations.Therefore, we present our research results on information extraction from text labels in process models modeled using Business Process Modeling Notation (BPMN) and use case models depicted in Unified Modeling Language (UML) using the most recent developments in natural language processing (NLP).In this paper, we focus on three relevant tasks, namely, the extraction of verb/noun phrases that would be used to form relations, parsing of conjunctive/disjunctive statements, and the detection of abbreviations and acronyms.Relation extraction was attempted to solve by implementing techniques that combine state-of-the-art NLP language models with formal regular expressions grammarbased structure detection.In this paper, we perform thorough testing of the most recent state-of-the-art NLP tools (CoreNLP, Stanford Stanza, Flair, Spacy, AllenNLP, BERT, ELECTRA), as well as custom BERT-BiLSTM-CRF and ELMo-BiLSTM-CRF implementations, trained with certain data augmentations to improve performance on the most ambiguous cases; these tools are used as a foundation for building tools to extract noun and verb phrases from short text labels generally used in UML and BPMN models.Furthermore, we describe our attempts to improve these extractors by solving the abbreviation/acronym detection problem using machine learning-based detection, as well as process conjunctive and disjunctive statements, due to their relevance to performing advanced text normalization.The obtained results show that the best phrase extraction and conjunctive phrase processing performance was obtained using Stanza based implementation, yet, our trained BERT-BiLSTM-CRF outperformed it for the verb phrase detection task.Our acronym detection approach resulted in the precision of 0.78 and F1-Score of 0.73 which may also be considered quite positive.While this work was inspired by our ongoing research on partial model-to-model transformations, we believe it to be applicable in other areas requiring similar text processing capabilities as well.