Reviewerly/exHarmony huggingface dataset:
Punctuation Restoration with Transformer Model on Social Media Data

Several key challenges are faced during sentiment analysis. One major problem is determining the sentiment of complex sentences, paragraphs, and text documents. A paragraph with multiple parts might have multiple sentiment values. Predicting the overall sentiment value for this paragraph will not produce all the information necessary for businesses and brands. Therefore, a paragraph with multiple sentences should be separated into simple sentences. With a simple sentence, it will be effective to extract all the possible sentiments. Therefore, to split a paragraph, that paragraph must be properly punctuated. Most social media texts are improperly punctuated, so separating the sentences may be challenging. This study proposes a punctuation-restoration algorithm using the transformer model approach. We evaluated different Bidirectional Encoder Representations from Transformers (BERT) models for our transformer encoding, in addition to the neural network used for evaluation. Based on our evaluation, the RobertaLarge with the bidirectional long short-term memory (LSTM) provided the best accuracy of 97% and 90% for restoring the punctuation on Amazon and Telekom data, respectively. Other evaluation criteria like precision, recall, and F1-score are also used.
Self-Attentive Subset Learning over a Set-Based Preference in Recommendation

Recommender systems that learn user preference from item-level feedback (provided to individual items) have been extensively studied. Considering the risk of privacy exposure, learning from set-level feedback (provided to sets of items) has been demonstrated to be a better way, since set-level feedback reveals user preferences while to some extent, hiding his/her privacy. Since only set-level feedback is provided as a supervision signal, different methods are being investigated to build connections between set-based preferences and item-based preferences. However, they overlook the complexity of user behavior in real-world applications. Instead, we observe that users’ set-level preference can be better modeled based on a subset of items in the original set. To this end, we propose to tackle the problem of identifying subsets from sets of items for set-based preference learning. We propose a policy network to explicitly learn a personalized subset selection strategy for users. Given the complex correlation between items in the set-rating process, we introduce a self-attention module to make sure all set members are considered in subset selecting process. Furthermore, we introduce gumble softmax to avoid gradient vanishing caused by binary selection in model learning. Finally the selected items are aggregated by user-specific personalized positional weights. Empirical evaluation with real-world datasets verifies the superiority of the proposed model over the state-of-the-art.
Stereoscopic Camera-Sensor Model for the Development of Highly Automated Driving Functions within a Virtual Test Environment

The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles.Such processes can be achieved by using virtual test environments and virtual sensor models.In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios.For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form.The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines.Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery.In addition, a separate programme for calibrating the model is presented.In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created.It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation.Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.
Quad Key-Secured 3D Gauss Encryption Compression System with Lyapunov Exponent Validation for Digital Images

High-dimensional systems are more secure than their lower-order counterparts. However, high security with these complex sets of equations and parameters reduces the transmission system’s processing speed, necessitating the development of an algorithm that secures and makes the system lightweight, ensuring that the processing speed is not compromised. This study provides a digital image compression–encryption technique based on the idea of a novel quad key-secured 3D Gauss chaotic map with singular value decomposition (SVD) and hybrid chaos, which employs SVD to compress the digital image and a four-key-protected encryption via a novel 3D Gauss map, logistic map, Arnold map, or sine map. The algorithm has three benefits: First, the compression method enables the user to select the appropriate compression level based on the application using a unique number. Second, it features a confusion method in which the image’s pixel coordinates are jumbled using four chaotic maps. The pixel position is randomized, resulting in a communication-safe cipher text image. Third, the four keys are produced using a novel 3D Gauss map, logistic map, Arnold map, or sine map, which are nonlinear and chaotic and, hence, very secure with greater key spaces (2498). Moreover, the novel 3D Gauss map satisfies the Lyapunov exponent distribution, which characterizes any chaotic system. As a result, the technique is extremely safe while simultaneously conserving storage space. The experimental findings demonstrate that the method provides reliable reconstruction with a good PSNR on various singular values. Moreover, the applied attacks demonstrated in the result section prove that the proposed method can firmly withstand the urge of attacks.
Logogram VR: Treadmill-Coupled VR with Word Reflective Content for Embodied Logogram Learning

A logogram is a type of writing system in which each character represents a word. Compared to segmental scripts where the alphabets reflect sounds, learning logograms are disengaging, since each character is not linked to its pronunciation. This paper presents Logogram VR, a virtual reality edutainment game that uses a treadmill and controllers to teach Hanja, which uses logograms. Hanja is a traditional Korean language writing system comprising over 8000 Chinese characters. The system leverages the logogram’s feature that each letter stands for each vocabulary item, as an embodied learning strategy. Specifically, it incorporates each character’s meaning into the VR learning environment, accompanied by gamified actions using a treadmill and VR controllers. We evaluated the system with 33 participants to test its overall usability, while determining the desirable playtime and number of characters for the further enhancement of it. We demonstrated and assessed the system with 125 visitors at an exhibition to disseminate it and verify the results with a wider population sample. The user studies revealed that the system provides a playful experience for learning Hanja without severe motion sickness. The differences in age groups showed that the embodiment approach utilizing meanings and actions in VR may be an effective logogram edutainment strategy, particularly among adolescents.
Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations

Emotion recognition in multi-party conversations (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Recently, many approaches have been devoted to exploiting inter-dependency and self-dependency among participants. However, these approaches remain inadequate in terms of inter-dependency due to the fact that the effects among speakers are not individually captured. In this paper, we design two hypergraphs to deal with inter-dependency and self-dependency, respectively. To this end, we design a multi-hypergraph neural network for ERMC. In particular, we combine average aggregation and attention aggregation to generate hyperedge features, which can allow utterance information to be better utilized. The experimental results show that our method outperforms multiple baselines, indicating that further exploitation of inter-dependency is of great value for ERMC. In addition, we also achieved good results on the emotional shift issue.
Single Image Reflection Removal Based on Residual Attention Mechanism

Affected by shooting angle and light intensity, shooting through transparent media may cause light reflections in an image and influence picture quality, which has a negative effect on the research of computer vision tasks. In this paper, we propose a Residual Attention Based Reflection Removal Network (RABRRN) to tackle the issue of single image reflection removal. We hold that reflection removal is essentially an image separation problem sensitive to both spatial and channel features. Therefore, we integrate spatial attention and channel attention into the model to enhance spatial and channel feature representation. For a more feasible solution to solve the problem of gradient disappearance in the iterative training of deep neural networks, the attention module is combined with a residual network to design a residual attention module so that the performance of reflection removal can be ameliorated. In addition, we establish a reflection image dataset named the SCAU Reflection Image Dataset (SCAU-RID), providing sufficient real training data. The experimental results show that the proposed method achieves a PSNR of 23.787 dB and an SSIM value of 0.885 from four benchmark datasets. Compared with the other most advanced methods, our method has only 18.524M parameters, but it obtains the best results from test datasets.
Regional Quantitative Mineral Prospectivity Mapping of W, Sn, and Nb-Ta Based on Integrated Information in Rwanda, Central Africa

As the need to discovers new mineral deposits and occurrences has intensified in recent years, it has become increasingly apparent that we need to map potentials via integrated information on the basis of metallogeny. Occurrences of mineralization such as tungsten (W), tin (Sn), columbium (Nb), tantalum (Ta), gold (Au), copper (Cu), lead (Pb), zinc (Zn), manganese (Mn) and monazite (Mnz) have been discovered in Rwanda. The objective of this study was to present a regional quantitative mineral prospectivity mapping (MPM) of W, Sn and Nb-Ta mineralization in Rwanda using the random forest (RF) method on the basis of open source data, such as geological maps, Bouguer gravity anomalies, magnetic anomalies, Landsat 8 images, ASTER GDEM, Globeland30, and OpenStreetMap. In addition, a newly introduced interpolation–density–delineation (IDD) process was applied to deal with the blank (masked) areas in remotely sensed mineral alteration extraction. Additionally, a k2-fold cross-validation method was also proposed to obtain more reasonable test errors. Firstly, the metallogenic regularity of W, Sn and Nb-Ta in Rwanda was summarized with the help of articles online. Secondly, original geological, geophysical, and remote sensing data were utilized to generate secondary data. Specifically, the IDD process was applied subsequent to the directed principal component analysis method (DPCA) to reconstruct the alteration anomaly map, and a relevant dataset was formed by the combination of original and secondary data. Thirdly, specific predictor layers for W, Sn and Nb-Ta were selected from relevant data via spatial correlation with known deposits, respectively, and the predictive models were established. Finally, near 26,000 squares were zoned in Rwanda, and RF was optimized and applied, the k2-fold cross-validation method was utilized to assess test errors, metallogenic belts and prospective areas for W, Sn, and Nb-Ta were delineated on the basis of total mineralization potential map and likelihoods map. Results proved that the open source data online were valid for drawing a preliminary mineralization potential map. Furthermore, it was also shown that the IDD method is suitable for the postprocessing of masked alteration anomaly maps. Belt IV-4 in the northwest and belt IV-2, IV-1 in the middle-east of Rwanda, containing a number of prospective areas, possess considerable likelihoods of deposits, and mining in Rwanda is at its dawn, with potential worth expecting.
A Study of Public Attitudes toward Shanghai’s Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo

With the advent of the Internet era, Chinese users tend to choose to express their opinions on social media platforms represented by Sina Weibo. The changes in people's emotions toward cities from the microblogging texts can reflect the image of cities presented on mainstream social media, and thus target a good image of cities. In this paper, we collected microblog data containing "Shanghai" from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. We also explore the impact of the COVID-19 epidemic on Shanghai's urban image in the context of the "Shanghai Territorial Static Management", an important public opinion topic during the COVID-19 epidemic. The results of the study show that the "Shanghai-wide static management" of COVID-19 epidemic has significantly reduced the public's perception of Shanghai and negatively affected the city's image. By analyzing the data results, we summarize the basic characteristics of Shanghai's city image and provide strategies for communicating Shanghai's city image in the post-epidemic era.
Applying War Heritage in the National World War II History Course for College Students in China: An Exploration of Digitization Strategies

Digital media is increasingly being used in formal education. In China, in order to reduce the negative emotions and behaviors of college students in World War Two (WWII) history courses, digitized resources of war heritage are being applied to improve their learning experience. However, their effectiveness remains unconfirmed. Therefore, the purposes of this study are to: (1) test whether the transformation of war heritage into digital resources can help to improve the learning experience of college students; (2) explore the pivotal factors that affect the learning experience of college students; and (3) provide suggestions for improving the digital teaching resources regarding war heritage. The results of the questionnaire survey show that the participants have a positive view of digital teaching resources. In addition, low-interactive digital media can achieve higher learning effects. The multidimensional scaling (MDS) analysis also reveals that creativity in teaching and helping to form students’ values regarding cherishing peace are the foci with respect to improving the learning experience. On this basis, suggestions for improving the digital teaching resources regarding WWII heritage are proposed in this study, as follows: (a) to improve the quality of multisensory experiences in digital teaching media regarding war heritage; (b) to reduce the difficulties with human–computer interaction regarding digital teaching media; (c) to strengthen the correlation between digital resources and the teaching aims; and (d) to build a vision for peace and sustainable development through the narrative of digital media.
Vectorization Method of Satellite Images Based on Their Decomposition by Topological Features

Vectorization of objects from an image is necessary in many areas. The existing methods of vectorization of satellite images do not provide the necessary quality of automation. Therefore, manual labor is required in this area, but the volume of incoming information usually exceeds the processing speed. New approaches are needed to solve such problems. The method of vectorization of objects in images using image decomposition into topological features is proposed in the article. It splits the image into separate related structures and relies on them for further work. As a result, already at this stage, the image is divided into a tree-like structure. This method is unique in its way of working and is fundamentally different from traditional methods of vectorization of images. Most methods work using threshold binarization, and the main task for them is to select a threshold coefficient. The main problem is the situation when there are several objects in the image that require a different threshold. The method departs from direct work with the brightness characteristic in the direction of analyzing the topological structure of each object. The proposed method has a correct mathematical description based on algebraic topology. On the basis of the method a geoinformation technology has been developed for automatic vectorization of raster images in order to search for objects located on it. Testing was carried out on satellite images from different scales. The developed method was compared with a special tool for vectorization R2V and showed a higher average accuracy. The average percentage of automatic vectorization of the proposed method was 81%, and the semi-automatic vectorizing module R2V was 73%.
A proposed cloud‑based platform for facilitating donation services in support to needy-students

The prevalence of needy-students who cannot afford their university fees has become a phenomenon in countries like Jordan. Thus, supporting such students by establishing a reliable cloud-based platform for collecting donations from wealthy people is a very noble action. Such donations will enable needy-students to continue their education to up their development, reduce their poverty, improve their health, scale-up their gender equality, promote peace across their communities, enhance stability in their societies, and enrich the field of cloud-based services development. This paper proposes a new cloud-based platform that aims at connecting donors with needy-students in a very discrete way that preserves and honors the privacy and confidentiality of their information. We claim that our proposed cloud-based platform depicts a unique novelty that can be easily anticipated from the perspectives of its status as a cloud-based donations and financial support services platform; its ability to interconnect its users privately and confidentially; and its ability to easily verify the credentials of its users and confirm their eligibility to receive financial support.
Separable-programming based probabilistic-iteration and restriction-resolving correlation filter for robust real-time visual tracking

Visual tracking methods based on correlation filter (CF) are to estimate the location and the scale of tracking object within video sequences. However, there are still several inadequate hypotheses within the CF framework. These inadequate hypotheses include the constant Gaussian label map, the predetermined object location and the insufficient utilization for features. To further address the problems caused by these hypotheses, this paper proposes a novel separable-programming based probabilistic-iteration and restriction-resolving correlation filter (PRCF). The main innovation points of this work are: 1) the separable-programming based tracking framework is adopted to achieve adaptive regulation incorporation and to simultaneously address location prediction and filter training; 2) the probabilistic-iteration scheme is suggested to update the Gaussian label for each frame to solve the problem of ad-hoc label map; 3) the adaptive feature fusion measure is introduced to abate the effects of insufficient utilization for numerous features; 4) the convergence behavior of PRCF is proved and discussed through theoretical analysis and worst-case convergence rate calculation. Experiments have also been conducted to test the efficiency of suggested PRCF on 7 benchmarks: OTB100, TC128, VOT2016, VOT2019, UAV123, NFS and LaSOT. The results have indicated that: 1) the PRCF obtains favorable performances compared with other 15 state-of-the-art (SOTA) CF-based trackers; 2) the PRCF reports comparable results against 4 deep learning based trackers; 3) the PRCF achieves a real-time speed of 52 frames-pre-second (FPS) on 7 benchmarks averagely. Thus, the PRCF is qualified for practical target tracking scenarios such as video surveillance and unmanned aerial vehicles (UAVs).
Evaluating generative patent language models

Generative language models are promising for assisting human writing in various domains. This manuscript aims to build generative language models in the patent domain and evaluate model performance from a human-centric perspective. The perspective is to measure the ratio of keystrokes that can be saved by autocompletion based on generative patent language models. A higher ratio means a more effective model which can save more keystrokes. This metric can be used to benchmark model performance. The metric is keystroke-based and different from conventional machine-centric metrics that are token-based. In terms of model size, the largest model built in this manuscript is PatentGPT-J-6B, which is state-of-the-art in the patent domain. Based on the metric, it is found that the largest model is not necessarily the best for the human-centric metric. The finding means that keeping increasing model sizes in the patent domain might be unnecessary if the purpose is to assist human writing with autocompletion. Several patent language models are pre-trained from scratch in this research. The pre-trained models are released for future researchers. Several visualization tools are also provided. The importance of building a generative language model in the patent domain is its potential to facilitate creativity and innovations in the future.
Report on Smart Education in China

China’s education is guided by The Global Education 2030 Agenda and focuses on fair, inclusive, and quality education. An important strategic choice for China’s educational reform and development in the new era is to support and lead the modernization of education with educational informatization. As a new form of ICT in education, smart education has become an inevitable trend of future education, which is highly concerned by policymakers and practitioners. In recent years, China has accelerated the transformation of digital education, successively issued policies related to smart education, and vigorously promoted the application of 5G, AI, blockchain, big data, and other new-generation information technologies in education. In 2018, China launched the smart education demonstration areas project, setting off a groundbreaking exploration of the future form of education. The “5G + smart education” application pilot project has pushed the development of smart education in China to a new wave. Nowadays, the National Smart Education Platform for Public Service is becoming the international business card of Chinese education, which has attracted widespread attention at home and abroad.