Reviewerly/exHarmony huggingface dataset:
DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving

Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In this work, we propose DeepAccident, a large-scale dataset generated via a realistic simulator containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset with 40k annotated samples. In addition, we propose a new task, end-to-end motion and accident prediction, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms. Furthermore, for each scenario, we set four vehicles along with one infrastructure to record data, thus providing diverse viewpoints for accident scenarios and enabling V2X (vehicle-to-everything) research on perception and prediction tasks. Finally, we present a baseline V2X model named V2XFormer that demonstrates superior performance for motion and accident prediction and 3D object detection compared to the single-vehicle model.
Spatio-temporal PV power forecasting considering the time-shift correction and the information fusion strategy of multi-stations

Accurate prediction of PV power is essential to ensuring the safe and economic operation of power systems with high PV penetration. The current PV power prediction scheme considering the spatio-temporal correlation characteristics is relatively simple in data processing, resulting in low prediction accuracy; at the same time, the missing data also poses a great problem to the prediction. Therefore, in order to improve the prediction accuracy and solve the problem of missing data, this paper proposes a PV power spatio-temporal prediction model considering time-shift correction and a multi-station information fusion strategy Firstly, relevant power station clusters are constructed using hierarchical clustering, and a similar daily data filtering model considering the variation characteristics of daily power characteristic curves is proposed to filter the data; Secondly, multiple BP neural network models are constructed and multiple reference power stations with high relevance are predicted using irradiance information; Thirdly, the prediction results of multiple reference power stations are input to the data processing module for time-shift analysis and spatial correlation information fusion correction, which solves the missing data problem of the target power station to be predicted. Finally, it is input to One-dimensional Convolutional Neural Network(1DCNN) to achieve the power prediction of the target power station with missing data. The simulation analysis shows that the root mean square error (RMSE) of a sunny day forecast is 3.31%; the RMSE of a non-sunny day forecast is 9.65%, which proves the accuracy of this two-layer neural network is higher compared to other model structures, so the proposed scheme has certain reliability and accuracy in the prediction of PV power with missing data.
Construction Method of Double-Carbon Domain Knowledge Graph

As the double-carbon concepts of “carbon neutralization” and “carbon peak” are gradually recognized by more and more people, more knowledge in the field of double carbon needs to make people understand it more systematically, to better understand the meaning of double-carbon better. For this reason, this paper proposes a method to construct a knowledge graph in the double carbon field. Firstly, aiming at the semi-structured and unstructured data in the double carbon field, the user-defined Web data processor and MYOMODE parsing method are used to extract the triple. Secondly, through the double carbon-related knowledge obtained from the massive Internet data, the knowledge will be converted into interrelated ternary data. The experimental results show that this method can quickly get valuable and coresponding double-carbon domain knowledge from massive Internet data, and construct it as a knowledge graph.
Research on New Power System Network Security Guarantee System

Based on the characteristics of the new power system with many points, wide range and unattended, this paper studies the specific Cyberspace security risks faced by the disease control side, the station side and the site side, and proposes a new power system Cyberspace security assurance system of “integration of collection, network, side, end, industry and people”. The site side security access measures, the site side civil air defense technology integration measures, the whole business endogenous security mechanism, the whole domain communication security mechanism, the integrated monitoring and early warning and emergency response mechanism are specifically adopted to form a comprehensive integrated security mechanism for the new power system, form a sustainable protection model, effectively improve the security capability, while taking into account the cost and operational complexity of specific implementation links, Provide comprehensive guarantee capability for the safe operation of the new power system.
A High Performance FPGA-based Accelerator for MobileNet

There are many problems in the existing FPGA implementation version of the current lightweight convolutional neural network model MobileNet. For example, the computational latency between depthwise convolution and point convolution is too long, and the frequent communication between the processor and the FPGA leads to low computational efficiency and low resource utilization. This paper proposes an FPGA optimization design for MobileNet, which effectively improves the real-time performance of the system and the resource utilization of the hardware acceleration unit.
Research on Anti-crawler and Anti-Anti-crawler Technology

In the era of big data, the extensive application of crawler technology makes the operation of the website difficult, and the anti-crawler technology of the website also emerges as the times require. How to develop anti-anti-crawler technology to effectively capture data according to anti-crawler Technology is of great significance for obtaining data. This paper introduces the basic architecture process and corresponding coping strategies of the commonly used website anti-crawler technology and crawler, analyzes the technical methods of anti-crawler, and puts forward some suggestions for anti-anti-crawler technology.
Design and Research of Integrated Information Management System for Dual Selection of NETM Tutors Based on SpringBoot

With the general increase of society’s requirements for education, more and more college students are joining the craze of NETM and universities keep putting forward the working concept of dual selection of NETM tutors. It is necessary to build a dual selection system based on SpringBoot framework and in line with agile development thinking. The existing information management system has problems such as low development efficiency, long iteration cycle and difficulty in changing requirements. Based on a comprehensive analysis of the requirements of the NETM tutor double-selection system, we created a work plan for NETM tutor double-selection based on MVVM design pattern, data interaction logic written by SpringBoot framework, data management using MySQL database and middleware such as MyBatisPlus and Redis, create a work plan for dual selection of NETM tutors and implemented the functions based on WeChat platform. The system unifies the login and registration behavior of users, unifies the front and back-end data exchange standards through RESTful style, and is equipped with the back-end management platform of ECharts, realizing the functions of automatic data collection, processing and analysis, and plays an active role in promoting the work related to postgraduate entrance examinations.
RLWE-based Privacy-Preserving Data Sharing Scheme for Internet of Medical Things System

Internet of Medical Things (IoMT) system is a collection of devices and software which are applied in medical care domains. In a IoMT system, patients and doctors conduct remote diagnosis and treatment through a health data sharing scheme. These data sharing schemes faces privacy challenges if the medical information gets leaked. However, the existing privacy-preserving data sharing schemes usually use classical encryption/signature algorithm, which will turn to be not secure under the development of quantum computer. In this paper, we propose a privacy-preserving data sharing scheme using lattice-based cryptography, the ring learning with errors (RLWE) cryptography. Our scheme integrates both quantum-resistant encryption and digital signature algorithm based on RLWE, in order to realize the resistance to quantum attack for the whole life cycle of data. In addition, the performance evaluation shows that the proposed scheme gets 75.7% higher efficiency than the existing schemes.
ENTL: Embodied Navigation Trajectory Learner

We propose Embodied Navigation Trajectory Learner (ENTL), a method for extracting long sequence representations for embodied navigation. Our approach unifies world modeling, localization and imitation learning into a single sequence prediction task. We train our model using vector-quantized predictions of future states conditioned on current states and actions. ENTL's generic architecture enables sharing of the spatio-temporal sequence encoder for multiple challenging embodied tasks. We achieve competitive performance on navigation tasks using significantly less data than strong baselines while performing auxiliary tasks such as localization and future frame prediction (a proxy for world modeling). A key property of our approach is that the model is pre-trained without any explicit reward signal, which makes the resulting model generalizable to multiple tasks and environments.
Research on Urban Domestic Sewage Treatment Technology and Development Trends under the Background of Rural Tourism Development

The diversified lifestyle of people not only increases the amount of domestic sewage discharged, but also tends to complicate the nature and composition of sewage, making the environmental water bodies that are already under great pressure continue to deteriorate. This article analyzes the current situation and development trend of urban domestic sewage treatment technology and explores feasible solutions. However, the resulting sewage can be discharged arbitrarily without proper treatment, which can easily cause secondary water pollution and serious environmental pollution, seriously damaging the urban environment and people's health. This article mainly analyzes the current situation and main problems of urban domestic sewage treatment technology, and studies the development trend of urban domestic sewage treatment.
Human behavior recognition based on improved slowfast network

Aiming at the problem of low accuracy of human action recognition for small targets and small amplitude actions at present, a human action recognition method with improved SlowFast network structure is proposed. This method uses the SlowFast network as the backbone network to extract features of different scales and inputs the features of multiple scales into the proposed spatiotemporal feature pyramid for processing to increase the network's sensitivity to semantic and spatial information at multiple scales. The experimental results on the public dataset AVA show that this method improves the accuracy of human behavior recognition by 0.77mAP compared with the original network, which is more in line with practical application requirements than similar algorithms.
Citra Perempuan dan Ketidakadilan Gender Pada Novel Kerumunan Terakhir dan Relevansinya dalam Pembelajaran Sastra

This research is entitled " Image of Women and Gender Injustice in the Novel Kerumunan Terakhir by Okky Madasari and Their Relevance in Learning Literature in High Schools ". study is to describe: (1) the embodiment of the image of women in novek Kerumunan Terakhir by Okky Madasari, (2) The form of gender injustice in novel Kerumunan Terakhir by Okky Madasari, and (3) The relevance of the research results on image of women and gender injustice in literary learning in senior high school (SMA) class XII.The form of this research is descriptive qualitative research.The data collection method in this study was carried out by reading and notetaking techniques.The data analysis methods used were heuristic and hermeneutic reading methods.The results of research on the image of women and gender injustice in novel Kerumunan Terakhir by Okky Madasari show that there are images of women in novel Kerumunna Terakhir by Okky Madasari including 4 physical aspects, 7 psychological aspects, 8 images of women in the family, and image of women in society amounted to five data.Forms of gender injustice are marginalization totaling 2 data, subordination totaling 4 data, stereotyping 3 data, workloading 3 data, and violence totaling 4 data.The relevance of the research results regarding the image of women and gender injustice can be used in literature learning in class XII Senior High School with Basic Competence (KD) 3.9 Analyzing novel content and language.
Universities’ Involvement in Promoting Digital Entrepreneurship and Future Digital Entrepreneurship Opportunities through Digital Technologies in Indonesia

Abstract Objectives This study aims to identify the roles of various universities in Indonesia in providing education that helps develop digital entrepreneurship: this includes the implications, contributions, and challenges of digitalization; the identification of the impact of digital entrepreneurship on businesses within Indonesia; and an examination of the opportunities and challenges for digital entrepreneurship in Indonesian universities. Methodology The research adopted a qualitative data analysis methodology. This was achieved through secondary data collection, mainly through a content review of the existing literature. Findings The study results indicate that academics have engaged in various forms of digital entrepreneurship, including e-commerce, commercial application development, and digital education business. Each step of digital academic entrepreneurship has been conceived within the context of universities. Digital academic entrepreneurship is the use of digital technology to achieve a goal, and this model includes the following components: motivation, stakeholders, processes, and business forms. Implications Universities must effectively embrace and foster digital academic entrepreneurship. According to this study, there are many ways that the government may encourage digital entrepreneurship at universities. Educational institutions have to give more thought to the growth of student businesses, particularly after firms have been established. The monitoring of student businesses should be handled by a specialized team based at universities. Limitations The main limitation was the fact that the study was limited to secondary data sources; therefore, future research should focus on primary data. Additionally, it was limited to Indonesian universities; future research should be performed on institutions in more advanced countries. Furthermore, the study used a qualitative research method; therefore, future research should use alternative research methodologies.
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains

This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i.e. various levels of label granularity) to train a powerful human parsing network. In practice, two common application scenarios are addressed, termed universal parsing and dedicated parsing, where the former aims to learn homogeneous human representations from multiple label domains and switch predictions by only using different segmentation heads, and the latter aims to learn a specific domain prediction while distilling the semantic knowledge from other domains. The proposed SST has the following appealing benefits: (1) it can capably serve as an effective training scheme to embed semantic associations of human body parts from multiple label domains into the human representation learning process; (2) it is an extensible semantic transfer framework without predetermining the overall relations of multiple label domains, which allows continuously adding human parsing datasets to promote the training. (3) the relevant modules are only used for auxiliary training and can be removed during inference, eliminating the extra reasoning cost. Experimental results demonstrate SST can effectively achieve promising universal human parsing performance as well as impressive improvements compared to its counterparts on three human parsing benchmarks (i.e., PASCAL-Person-Part, ATR, and CIHP). Code is available at https://github.com/yangjie-cv/SST.
Subpixel Interpolation Disparity Refinement for Semi-Global Matching

Semi-Global Matching (SGM) algorithms and their corresponding hardware accelerators, which focus on stereo matching, have been developed in the last few years. However, the interpolation for disparity is much indispensable for real-world applications but still remains refining. This work presents a pixel-level pipeline architecture for the disparity refinement for SGM in case of computing disparity, which refines disparity through subpixel interpolation with an optimized cosine look up table and a compute-friendly parallel divider. The hardware architecture based on optimization algorithms has reached a error rate of only 6.33% for tradition background and 7.30% for occlusion condition, achieved in real-time FPGA as well.
Mobiltechnológiával támogatott kutatásalapú tanulás

Learning supported by mobile technology is extremely popular today because these technologies can support learning in different contexts. Mobile phones, e-book readers, MP3 and portable media players, tablets, laptops, smartphones, and GPS receivers can be considered mobile technology. As it can be seen from this list, they are different IT devices that can be held in the hand. Since they are portable devices, they enable learning regardless of time and place. The learning and teaching processes realized with mobile technology are called mobile learning (m-learning). Not only the device but also the student and learning are mobile. In the 2000s, research was published in the international literature in which mobile technologies supported learning by supplementing inquiry-based learning (IBL). Mobile technology-supported inquiry-based learning (mIBL) offers potential opportunities to support different levels of inquiry and generate new types of research; it helps to arouse and maintain students’ curiosity and motivation; it connects formal school and informal natural science learning related to everyday life. Although the research results so far show that mobile technology provides adequate support for IBL learning processes, practical strategies, recommendations, and guidelines are needed for further development and appropriate educational application. This study reviews the characteristics and practical applications of mobile learning through mobile technologies, emphasizing the support of inquiry-based learning.
Bicol College Property Management System for Bachelor of Science in Hospitality Management Students: Mock Hotel Operation

Bicol College Hospitality Management Department uses third-party software to assess students through the practical exam. Using third-party software makes it difficult for the Hospitality Management Department in terms of flexibility, availability, and scheduling. This study is for Bicol College Property Management System for Bachelor of Science in Hospitality Management Students: Mock Hotel Operation aimed to answer this problem. It has the ability to perform different automated processes of how a hotel is being managed, similar to the third-party software they are using. However, a lot more features have been added, like the technology of Radio Frequency Identification (RFID). The four phases of Rapid Application Development (RAD) helped the researchers to gather enough information to conduct this study successfully. The collection of data focused on three categories: Overall Design, Functionality, and User Friendliness. To test the developed system, the researchers conducted a demonstration, beta testing, and distributed questionnaires with the participants based on a quantitative method. The researchers received positive feedback from the participants. In conclusion, the researchers met and filled the gaps in this study. They had also proven that they successfully met their general and specific objectives.
Statistical Evaluation of Style Dynamics: Lyrical Poems by V. Nabokov in the Mirror of Parts of Speech

The article utilizes quantitative methods of analysis to obtain objective indices which make it possible to evaluate a separate text and the style of the author. The author applies the techniques of textometry (analysis of PoS ratio, type of&#x0D; description, cumulative sum) in order to establish the degree of text stability /dynamics and stability / variability of individual style in general.&#x0D; The database includes verse texts of Vladimir Nabokov created during different periods of creative activity. Poetry was an essential and inseparable part of his writing (as the writer and poet stated himself) which proves the non-accidental character of parameters of his poetic texts and their high relevance for the&#x0D; study of his style. Only texts not less than 30 lines are included in the database in order to avoid fluctuations in frequency of linguistic elements and ensure comparability of the research material.&#x0D; The use of Busemann and variability coefficients made it possible to define the ratio of verbal / nominal description in the texts, while analysis of cumulative sum – the ratio of nouns / verbs in the texts in dynamics (from the beginning to its end). The calculated power function provides precise approximation of the data.&#x0D; The obtained results support the conclusion about the existence of hidden tendencies in the distribution of words of different parts of speech in Nabokov's text, about the growth of variability of their frequencies in the second period of Nabokov’s creative activity, as well as about steady strengthening of the tendency&#x0D; of text verbality growth over the lifespan of the poet.