Reviewerly/exHarmony huggingface dataset:
NON-BIASED CFACS MEASUREMENT TOOL: FROM IDEA TO SOFTWARE APPLICATION

We would like to draw the readers' attention to our method of precise facial expressions analysis.Our research pursued two main objectives: (1) developing the methods and algorithms for the direct evaluation of facial expressions using computer vision (CV) technology; (2) creating software for conducting empirical research in psychology and related subjects.We have developed an authentic implementation of cFACS (Rosenberg, Ekman, 2020) as an approach to computer facial expressions analysis.We would like to stress that at present, the neural network approach is de facto dominant.The approach is based on the basic emotions concept, and on the assumption that a neural network can be trained to recognize emotions using a respective set of pictures, or, more rarely, videos.In our opinion, implementing this approach is limited (Baev et al., 2021).We have applied a comprehensive approach to the analysis of facial activity, based on detecting and analyzing time-defined FACS action units' (AUs) combinations.The chosen approach has obvious advantages: 1) employing AUs as facial activity analysis units; 2) the feasibility of assessment of individual features of facial activity; 3) racial and age biases becoming irrelevant.While developing the software, two major methodological principles have been used: 1) the direct assessment of facial surface movements aiming to detect AUs, coupled with the deliberate rejection of employing neural networks for facial events classification; 2) modelling an expert's perception of facial surface movements' peculiarities while detecting certain AUs, taking into account the general topography of facial surface movements.We have developed the EmoRadar software, using our proprietary CV procedures that allow us to evaluate facial surface changes in the areas corresponding to different AUs.In the process, there was created a system of rules, based on which the "raw" data on lighting changes transform into surface movements, and those, in turn, convert into AUs, and, further on, into basic emotions and patters of facial activity.In the course of our software's empirical verification, the method of automated analysis of job interviews was created.We have also performed automatic evaluation of video recordings for forensic psychological examinations and analyzed participants` emotional dynamics, participating in the "SIRIUS-21" project of the Institute of Biomedical Problems of the Russian Academy of Sciences.We believe that the implementation of the above-mentioned principles of the direct approach to facial activity registering and the comprehensive approach to its analysis provides tangible advantages: • High accuracy of facial surface movements assessment.• Feasibility of conducting all kinds of facial analysis (basic emotions, basic emotions emblems, facial activity patterns) based on reliable allocation of 22 main AUs.• Complete absence of racial bias.• Feasibility of assessing children's facial activity (up to 5-7 years old).• Independence from theoretical approaches to the facial expressions analysis (P.
Connotation, goal and path of high quality development of integrated higher education in Yangtze River Delta

The integrated high-quality development of higher education in the Yangtze River Delta is to make use of regional advantages and optimize the allocation of innovation resources to create a high-level skilled talents training platform, industrial collaborative innovation talents, and technological innovation platform.This paper expounds the connotation of the integrated high-quality development of higher education in the Yangtze River Delta, further clarifies its development goals, and promotes the integrated high-quality development of higher education in the Yangtze River Delta through the construction of characteristic entrepreneurial universities and technology-based enterprises, the improvement of innovation resource sharing mechanism, the full play of the leading role of government institutions, and the improvement of governance effectiveness.
Design and Implementation of Student Management System Based on B/S

In today's information age, student management is no longer satisfied with the traditional manual management stage, but develops towards informatization and networking.The system is developed in B / S mode.The data processing function and operation flow are realized by using web technology and B / S framework technology.Firstly, this paper introduces B / S technology and expounds the advantages of student management informatization.Then, the framework of student management system based on B / S is designed to test the performance of the student management system.Finally, the test results show that the running time and delay time of the system are very short, and the CPU occupation is maintained at about 3%.The system has basically achieved the expected functions.
Exploration on the “Online+Offline” Mixed Education Model of College English Curriculum

Education is an important battlefield for the deep development and cultivation of high-tech, and the "online+offline" mixed education model is a new form developed from the former.It can not only achieve effective interaction between teachers and students, but also enhance students' learning enthusiasm and enhance their English application ability.This article compared the academic performance of students before and after adopting the "online+offline" mixed education model, and found that the average score of students after adopting it reached 84.7 points, which was 9.6 points higher than the 75.1 points before adopting it.The results indicated that the "online+offline" blended education model has a significant effect on improving the teaching quality of college English courses.
Discussion on Key Technologies of Computer Artificial Intelligence Recognition

Computer artificial intelligence recognition technology is a kind of operation means based on traditional computer technology, which has become a new type of science and technology today.At present, the scope of computer artificial intelligence recognition technology is very wide, and it is also very vulnerable to other different factors in the development.However, the research on key technologies of computer artificial intelligence recognition is relatively few in China, so relevant personnel must comprehensively study the application process of computer artificial intelligence recognition technology based on their own work experience, so as to provide strong reference for other personnel.
Semantic-Based Classification of Toxic Comments Using Ensemble Learning

A social media is rapidly expanding, and its anonymity feature completely supports free speech. Hate speech directed at anyone or any group because of their ethnicity, clan, religion, national or cultural their heritage, sex, disability, gender orientation, or other characteristics is a violation of their authority. Seriously encourages violence or hate crimes and causes social unrest by undermining peace, trustworthiness, and human rights, among other things. Identifying toxic remarks in social media conversation is a critical but difficult job. There are several difficulties in detecting toxic text remarks using a suitable and particular social media dataset and its high-performance, selected classifier. People nowadays share messages not only in person, but also in online settings such as social networking sites and online groups. As a result, all social media sites and apps, as well as all current communities in the digital world, require an identification and prevention system. Finding toxic social media remarks has proven critical for content screening. The identifying blocker in such a system would need to notice any bad online behavior and alert the prophylactic blocker to take appropriate action. The purpose of this research was to assess each text and find various kinds of toxicities such as profanity, threats, name-calling, and identity-based hatred. Jigsaw's designed Wikipedia remark collection is used for this.
3D Object Detection Based on Neighborhood Graph Search in Dense Scenes

In the field of robotics and autonomous driving, achieving accurate 3D object detection is crucial for the perception of complex traffic environments. Most current research uses deep learning methods to extract object features from point clouds or images. However, these approaches often do not fully utilize the mutual positional information between objects, resulting in low detection accuracy in dense scenes. To address this issue, this paper proposes a frustum point cloud 3D target detection algorithm based on the fusion of camera and LiDAR data. We establish global connectivity of objects based on the degree of overlap between camera detection frames. Then, we design a neighborhood graph search algorithm based on constraint satisfaction to match the camera target detection results with LiDAR clustering results. Finally, the category and distance information of obstacles are displayed in bird’s eye view (BEV). Evaluated on the KITTI benchmarks, our method achieves an average precision (AP) of 88.38% on the easy level, 87.64% on the moderate level, and 80.10% on the hard level in BEV detection. Compared to F-ConvNet, our method shows improvements of 5.49% on the moderate level and 7.33% on the hard level, significantly enhancing recognition accuracy in dense scenes.
An Analysis of Temporal Trends in Anti-Asian Hate and Counter-Hate on Twitter During the COVID-19 Pandemic

Recent studies have documented increases in anti-Asian hate throughout the COVID-19 pandemic. Yet relatively little is known about how anti-Asian content on social media, as well as positive messages to combat the hate, have varied over time. In this study, we investigated temporal changes in the frequency of anti-Asian and counter-hate messages on Twitter during the first 16 months of the COVID-19 pandemic. Using the Twitter Data Collection Application Programming Interface, we queried all tweets from January 30, 2020 to April 30, 2021 that contained specific anti-Asian (e.g., #chinavirus, #kungflu) and counter-hate (e.g., #hateisavirus) keywords. From this initial data set, we extracted a random subset of 1,000 Twitter users who had used one or more anti-Asian or counter-hate keywords. For each of these users, we calculated the total number of anti-Asian and counter-hate keywords posted each month. Latent growth curve analysis revealed that the frequency of anti-Asian keywords fluctuated over time in a curvilinear pattern, increasing steadily in the early months and then decreasing in the later months of our data collection. In contrast, the frequency of counter-hate keywords remained low for several months and then increased in a linear manner. Significant between-user variability in both anti-Asian and counter-hate content was observed, highlighting individual differences in the generation of hate and counter-hate messages within our sample. Together, these findings begin to shed light on longitudinal patterns of hate and counter-hate on social media during the COVID-19 pandemic.
MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs

Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effectiveness of current models, the existing public datasets are still insufficient to meet the need for these potential solutions due to their ignorance of complete exercising contexts, fine-grained concepts, and cognitive labels. In this paper, we present MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge concepts, and over 12 million behavioral records. Specifically, we propose a framework to guarantee a high-quality and comprehensive annotation of fine-grained concepts and cognitive labels. The statistical and experimental results indicate that our dataset provides the basis for the future improvements of existing methods. Moreover, to support the convenient usage for researchers, we release a set of tools for data querying, model adaption, and even the extension of our repository, which are now available at https://github.com/THU-KEG/MOOC-Radar.
Connecting Unseen Domains: Cross-Domain Invariant Learning in Recommendation

As web applications continue to expand and diversify their services, user interactions exist in different scenarios. To leverage this wealth of information, cross-domain recommendation (CDR) has gained significant attention in recent years. However, existing CDR approaches mostly focus on information transfer between observed domains, with little attention paid to generalizing to unseen domains. Although recent research on invariant learning can help for the purpose of generalization, relying only on invariant preference may be overly conservative and result in mediocre performance when the unseen domain shifts slightly. In this paper, we present a novel framework that considers both CDR and domain generalization through a united causal invariant view. We assume that user interactions are determined by domain-invariant preference and domain-specific preference. The proposed approach differentiates the invariant preference and the specific preference from observational behaviors in a way of adversarial learning. Additionally, a novel domain routing module is designed to connect unseen domains to observed domains. Extensive experiments on public and industry datasets have proved the effectiveness of the proposed approach under both CDR and domain generalization settings.
Event-Aware Adaptive Clustering Uplift Network for Insurance Creative Ranking

In the classical e-commerce platforms, the personalized product-tying recommendation has proven to be of great added value, which improves users' purchase willingness to product-tying by displaying the suitable marketing creative. In this paper, we present a new recommendation problem, i.e., the Pop-up One-time Marketing (POM), where the product-tying marketing creative only pops up one time when the user pays for the main item. POM has become a ubiquitous application in e-commerce platforms, e.g., buy the mobile tying mobile case and buy flight ticket tying insurance. However, many existing recommendation methods are sub-optimal for the creative marketing in the POM scenario due to unconsidering the unique characteristics in the scenario. To tackle this problem, we propose a novel framework named Event-aware Adaptive Clustering Uplift Network (EACU-Net) for the POM scenario, which is to our best knowledge the first attempt along this line. EACU-Net contains three modules: (1) the event-aware graph cascading learning, which employs a heterogeneous graph network to comprehensively learn the embedding for the user attributes, event categories, and creative elements by stage. (2) an adaptive clustering uplift network, which learns the sensitivity of users to creatives under the same context. (3) an event-aware information gain network to learn more information from samples with event affection. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superior performance of the proposed model compared with the state-of-the-art method.
Multi-camera person re-identification using spatiotemporal context modeling

Person re-identification (ReID) aims at identifying a person of interest (POI) across multiple non-overlapping cameras. The POI can be either in an image or in a video sequence. Factors such as occlusion, variable viewpoint, misalignment, unrestrained poses, background clutter are the major challenges in developing robust, person ReID models. To address these issues, an attention mechanism that comprises local part/region-aggregated feature representation learning is presented in this paper by incorporating long-range local and global context modeling. The part-aware local attention blocks are aggregated into the widely used modified pre-trained ResNet50 CNN architecture as a backbone employing two attention blocks, i.e., Spatio-Temporal Attention Module (STAM) and Channel Attention Module (CAM). The spatial attention block of STAM can learn contextual dependencies between different human body parts/regions like head, upper body, lower body, and shoes from a single frame. On the other hand, the temporal attention modality can learn temporal contextual dependencies of the same person’s body parts across all video frames. Lastly, the channel-based attention modality, i.e., CAM, can model semantic connections between the channels of feature maps. These STAM and CAM blocks are combined sequentially to form a unified attention network named as Spatio-Temporal Channel Attention Network (STCANet) that will be able to learn both short-range and long-range global feature maps, respectively. Extensive experiments are carried out to study the effectiveness of STCANet on three image-based and two video-based benchmark datasets, i.e., Market-1501, DukeMTMC-ReID, MSMT17, DukeMTC-VideoReID, and MARS. K-reciprocal re-ranking of gallery set is also applied in which the proposed network showed a significant improvement over these datasets in comparison with state of the art. Lastly, to study the generalizability of STCANet on unseen test instances, cross-validation on external cohorts is also applied that showed the robustness of the proposed model that can be easily deployed to the real world for practical applications.
Authorized Redundant Check Support in a Hybrid Cloud Environment

Data deduplication is one of the most significant data contraction ways for removing indistinguishable clones of recreating data, and it's generally used in pall storehouse to minimise storehouse space and save bandwidth. The coincident encryption approach has been developed to cipher the data before outsourcing to insure the confidentiality of sensitive data while easing deduplication. This work is the first attempt to explicitly address the content of authorised data deduplication in order to ameliorate data security. In discrepancy to standard deduplication systems, the discriminational boons of druggies are taken into account in indistinguishable check in addition to the data itself. In addition, we describe numerous innovative deduplication infrastructures that give authorised indistinguishable check in a cold-blooded pallarchitecture. Our approach is secure in terms of the delineations stated in the proposed security model, according to security analysis. We apply a prototype of our proposed authorised indistinguishable check medium as a evidence of conception and take over testbed tests with it. We demonstrate that our proposed authorised indistinguishable check fashion has a low outflow when compared to typical operations
3D visualization and detection of glomeruli in whole mouse kidney

The kidney is an important organ in the body to excrete metabolic waste, and the glomerulus is an essential structure for the kidney to play a role in blood filtration. Abnormal glomerular numbers are associated with nephropathy or circulatory disease. With the development of imaging technology, mesoscopic optical imaging can obtain whole kidney images at single cell resolution. The detection of glomeruli from images is very crucial for understanding the renal function and studying nephropathy. Existing detection methods cannot balance both accuracy and efficiency, so we proposed a deep learning-based glomeruli detection method. First, we imaged an entire mouse kidney with High-Definition fluorescent Micro-Optical Sectioning Tomography (HD-fMOST) and obtained a three-dimensional (3D) kidney image at cellular resolution. Then, we designed an end-to-end 3D convolutional neural network based on the morphological features of glomeruli in the kidney image, which can directly read 3D images and predict the precise coordinates of glomeruli. We used the acquired kidney dataset to train the network and validated the effect of glomeruli detection. Finally, we applied our approach to detect glomeruli in large-scale mouse kidney. The results showed that the proposed method reached the state-of-the-art level, which is more efficient and accurate compared to similar methods. The proposed method will provide a powerful tool for kidney-related research.
E-MODUL BERBASIS ANDROID “KITKAT VERSI 4.4” UNTUK MEMFASILITASI ASYNCHRONOUS LEARNING MAHASISWA PENDIDIKAN MATEMATIKA DI TERNATE

Learning in asynchronous learning in introductory basic mathematics courses in Ternate is hampered by unstable internet conditions. These conditions require the existence of electronic teaching materials that can be accessed when conditions without the internet. So this study aims to develop electronic teaching materials in the form of e-modules that meet valid and practical criteria as an alternative solution to the constraints experienced during the asynchronous learning process in introductory basic mathematics courses. This goal will be achieved through the Research and Development (R&amp;D) method which is carried out following the stages of the ADDIE model. The research subjects involved consisted of 1 instrument expert, 2 material experts, 2 learning technology experts, 1 IT expert, and 11 Mathematics Education students at STKIP Kie Raha-Ternate. Research data were collected using instruments in the form of material validation sheets, media validation sheets, and practicality response questionnaires. The data obtained was analyzed for its validity from the material aspect, media aspect, and practicality aspect. Based on the results of data analysis, it can be shown that the developed e-module has met the valid criteria from the media aspect and the material aspect. In addition, e-modules are also practical when implemented using the asynchronous learning method. So it can be concluded that the research conducted has achieved the stated objectives.AbstrakPembelajaran pada mata kuliah pengantar dasar matematika secara asynchronous learning di Ternate terhambat oleh kondisi internet yang tidak stabil. Kondisi tersebut menuntut adanya bahan ajar elektronik yang dapat diakses ketika kondisi tanpa internet. Maka penelitian ini bertujuan untuk mengembangkan bahan ajar elektronik berupa e-modul yang memenuhi kriteria valid dan praktis sebagai alternatif solusi terhadap kendala yang dialami selama proses asynchronous learning pada mata kuliah pengantar dasar matematika. Tujuan tersebut akan dicapai melalui metode Research and Development (R&amp;D) yang dikerjakan mengikuti tahapan model ADDIE. Subjek penelitian yang terlibat terdiri dari 1 orang ahli instrumen, 2 orang ahli materi, 2 orang ahli teknologi pembelajaran, 1 orang ahli IT, dan 11 mahasiswa Pendidikan Matematika STKIP Kie Raha-Ternate. Data penelitian dikumpulkan menggunakan instrumen berupa lembar validasi materi, lembar validasi media, dan angket respon kepraktisan. Data yang diperoleh dianalisis validitasnya dari aspek materi, aspek media, dan aspek kepraktisan. Berdasarkan hasil analisis data dapat ditunjukkan bahwa e-modul yang dikembangkan telah memenuhi kriteria valid dari aspek media dan aspek materi. Selain itu e-modul juga praktis jika diterapkan menggunakan metode asyncrhonous learning. Sehingga dapat disimpulkan bahwa penelitian yang dilakukan telah mencapai tujuan yang ditetapkan.