Reviewerly/exHarmony huggingface dataset:
Open Anomalous Trajectory Recognition via Probabilistic Metric Learning

Typically, trajectories considered anomalous are the ones deviating from usual (e.g., traffic-dictated) driving patterns. However, this closed-set context fails to recognize the unknown anomalous trajectories, resulting in an insufficient self-motivated learning paradigm. In this study, we investigate the novel Anomalous Trajectory Recognition problem in an Open-world scenario (ATRO) and introduce a novel probabilistic Metric learning model, namely ATROM, to address it. Specifically, ATROM can detect the presence of unknown anomalous behavior in addition to identifying known behavior. It has a Mutual Interaction Distillation that uses contrastive metric learning to explore the interactive semantics regarding the diverse behavioral intents and a Probabilistic Trajectory Embedding that forces the trajectories with distinct behaviors to follow different Gaussian priors. More importantly, ATROM offers a probabilistic metric rule to discriminate between known and unknown behavioral patterns by taking advantage of the approximation of multiple priors. Experimental results on two large-scale trajectory datasets demonstrate the superiority of ATROM in addressing both known and unknown anomalous patterns.
VecoCare: Visit Sequences-Clinical Notes Joint Learning for Diagnosis Prediction in Healthcare Data

Due to the insufficiency of electronic health records (EHR) data utilized in practical diagnosis prediction scenarios, most works are devoted to learning powerful patient representations either from structured EHR data (e.g., temporal medical events, lab test results, etc.) or unstructured data (e.g., clinical notes, etc.). However, synthesizing rich information from both of them still needs to be explored. Firstly, the heterogeneous semantic biases across them heavily hinder the synthesis of representation spaces, which is critical for diagnosis prediction. Secondly, the intermingled quality of partial clinical notes leads to inadequate representations of to-be-predicted patients. Thirdly, typical attention mechanisms mainly focus on aggregating information from similar patients, ignoring important auxiliary information from others. To tackle these challenges, we propose a novel visit sequences-clinical notes joint learning approach, dubbed VecoCare. It performs a Gromov-Wasserstein Distance (GWD)-based contrastive learning task and an adaptive masked language model task in a sequential pre-training manner to reduce heterogeneous semantic biases. After pre-training, VecoCare further aggregates information from both similar and dissimilar patients through a dual-channel retrieval mechanism. We conduct diagnosis prediction experiments on two real-world datasets, which indicates that VecoCare outperforms state-of-the-art approaches. Moreover, the findings discovered by VecoCare are consistent with the medical researches.
Semi-supervised Domain Adaptation in Graph Transfer Learning

As a specific case of graph transfer learning, unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs. However, graphs with topology and attributes usually have considerable cross-domain disparity and there are numerous real-world scenarios where merely a subset of nodes are labeled in the source graph. This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity. To address these challenges, we propose a method named Semi-supervised Graph Domain Adaptation (SGDA). To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding. Thus, the node classifier trained on labeled source nodes can be transferred to the target nodes. Moreover, to address the label scarcity, we propose pseudo-labeling on unlabeled nodes, which improves classification on the target graph via measuring the posterior influence of nodes based on their relative position to the class centroids. Finally, extensive experiments on a range of publicly accessible datasets validate the effectiveness of our proposed SGDA in different experimental settings.
From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement

Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. However, these methods have limited exploration in another major visibility damage, the glow effects in real night scenes. Glow effects are inevitable in the presence of artificial light sources and cause further diffused blurring when directly enhanced. To settle this issue, we innovatively consider the glow suppression task as learning physical glow generation via multiple scattering estimation according to the Atmospheric Point Spread Function (APSF). In response to the challenges posed by uneven glow intensity and varying source shapes, an APSF-based Nighttime Imaging Model with Near-field Light Sources (NIM-NLS) is specifically derived to design a scalable Light-aware Blind Deconvolution Network (LBDN). The glow-suppressed result is then brightened via a Retinex-based Enhancement Module (REM). Remarkably, the proposed glow suppression method is based on zero-shot learning and does not rely on any paired or unpaired training data. Empirical evaluations demonstrate the effectiveness of the proposed method in both glow suppression and low-light enhancement tasks.
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning

Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the underlying community semantics has not been well explored by most previous GCL methods. Research that attempts to leverage communities in GCL regards them as having the same influence on the graph, leading to extra representation errors. To tackle this issue, we define ''community strength'' to measure the difference of influence among communities. Under this premise, we propose a Community-Strength-enhanced Graph Contrastive Learning (CSGCL) framework to preserve community strength throughout the learning process. Firstly, we present two novel graph augmentation methods, Communal Attribute Voting (CAV) and Communal Edge Dropping (CED), where the perturbations of node attributes and edges are guided by community strength. Secondly, we propose a dynamic ''Team-up'' contrastive learning scheme, where community strength is used to progressively fine-tune the contrastive objective. We report extensive experiment results on three downstream tasks: node classification, node clustering, and link prediction. CSGCL achieves state-of-the-art performance compared with other GCL methods, validating that community strength brings effectiveness and generality to graph representations. Our code is available at https://github.com/HanChen-HUST/CSGCL.
Totally Dynamic Hypergraph Neural Networks

Recent dynamic hypergraph neural networks (DHGNNs) are designed to adaptively optimize the hypergraph structure to avoid the dependence on the initial hypergraph structure, thus capturing more hidden information for representation learning. However, most existing DHGNNs cannot adjust the hyperedge number and thus fail to fully explore the underlying hypergraph structure. This paper proposes a new method, namely, totally hypergraph neural network (TDHNN), to adjust the hyperedge number for optimizing the hypergraph structure. Specifically, the proposed method first captures hyperedge feature distribution to obtain dynamical hyperedge features rather than fixed ones, by conducting the sampling from the learned distribution. The hypergraph is then constructed based on the attention coefficients of both sampled hyperedges and nodes. The node features are dynamically updated by designing a simple hypergraph convolution algorithm. Experimental results on real datasets demonstrate the effectiveness of the proposed method, compared to SOTA methods. The source code can be accessed via https://github.com/HHW-zhou/TDHNN.
Specifying and Testing k-Safety Properties for Machine-Learning Models

Machine-learning models are becoming increasingly prevalent in our lives, for instance assisting in image-classification or decision-making tasks. Consequently, the reliability of these models is of critical importance and has resulted in the development of numerous approaches for validating and verifying their robustness and fairness. However, beyond such specific properties, it is challenging to specify, let alone check, general functional-correctness expectations from models. In this paper, we take inspiration from specifications used in formal methods, expressing functional-correctness properties by reasoning about k different executions---so-called k-safety properties. Considering a credit-screening model of a bank, the expected property that "if a person is denied a loan and their income decreases, they should still be denied the loan" is a 2-safety property. Here, we show the wide applicability of k-safety properties for machine-learning models and present the first specification language for expressing them. We also operationalize the language in a framework for automatically validating such properties using metamorphic testing. Our experiments show that our framework is effective in identifying property violations, and that detected bugs could be used to train better models.
Detecting Adversarial Faces Using Only Real Face Self-Perturbations

Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems. Although existing defense techniques achieve high accuracy in detecting some specific adversarial faces (adv-faces), new attack methods especially GAN-based attacks with completely different noise patterns circumvent them and reach a higher attack success rate. Even worse, existing techniques require attack data before implementing the defense, making it impractical to defend newly emerging attacks that are unseen to defenders. In this paper, we investigate the intrinsic generality of adv-faces and propose to generate pseudo adv-faces by perturbing real faces with three heuristically designed noise patterns. We are the first to train an adv-face detector using only real faces and their self-perturbations, agnostic to victim facial recognition systems, and agnostic to unseen attacks. By regarding adv-faces as out-of-distribution data, we then naturally introduce a novel cascaded system for adv-face detection, which consists of training data self-perturbations, decision boundary regularization, and a max-pooling-based binary classifier focusing on abnormal local color aberrations. Experiments conducted on LFW and CelebA-HQ datasets with eight gradient-based and two GAN-based attacks validate that our method generalizes to a variety of unseen adversarial attacks.
Application of Mechatronic Engineering Technology in the Structural Design of Intelligent Robots

The structural design of intelligent robots is crucial to their performance and functions, and the application of mechatronic engineering technology can significantly improve the motion control and perception capabilities of intelligent robots.In this paper, the effects of the application of mechatronic engineering technology in the structural design of intelligent robots on key performance indicators such as movement flexibility, adaptability, execution efficiency, and management complexity are confirmed through experiments.In the comparison between the traditional robot structure and the intelligent robot structure improved by mechatronic engineering, the improved intelligent robot scored 4.8 in terms of movement flexibility, which is 37.1% higher than the traditional structure; in terms of adaptability, the score reached 4.6, an increase of 43.8%; in terms of execution efficiency, the average task completion time was reduced to 4.7 seconds, an increase of 51.6%; and the management complexity score reached 4.5, an increase of 55.2%.This shows that the application of mechatronic engineering technology in the structural design of intelligent robots will provide a higher level of performance and functions for the development of intelligent robots, and promote the wide application of intelligent robots in various fields.
Teaching materials development of role-play game-enhanced English vocabulary acquisition in primary school

This research aims to develop teaching materials using role-play game-enhanced English vocabulary acquisition in primary school. This research was conducted in one of the outstanding primary schools in Makassar, South Sulawesi, Indonesia. This study is formative research—developmental research to improve design theory for designing teaching practices or processes. The development of the material was based on three important steps: preliminary study, development, and validation by experts in learning and teaching materials. Before developing the teaching materials, the syllabus should be made to support the development of the teaching materials. The teaching material was developed based on the teaching objectives with the topics or themes related to the student's daily life. After developing the material, the material was evaluated by two experts in learning and teaching materials. The evaluation was done by giving the prototype of the teaching material and validation sheet to the experts. The result of the study is the teaching material of basic English vocabulary based on the role-play technique for the pupils. It was found that this developed material was categorized as good based on the evaluation of the experts of the learning materials. The basic English vocabulary teaching materials, lesson plans, and teaching materials were designed based on the needs of the students. It was discovered after analyzing the data. It is intended that because the contents of the materials apply to the student's everyday lives, they can read and comprehend them. Role-playing techniques are also used in the creation of instructional materials. For the pupils, this technique is engaging and enjoyable. However, the teaching material still needs to be developed regarding the content, language use, and appearance.
Modeling and Estimation of Solar Radiation through Artificial Neural Network using known Solar Data

&lt;p&gt;The paper attempts to discover a city's global, direct beam, and diffused solar radiation with the help of known solar radiation of its neighbor(s). Two distinctive architecture have been used to find the solar radiation of a city. In the first architecture, we used single input and a single hidden layer of the neuron to find the solar radiation of the city. The solar radiation of Lahore, Multan, and Quetta was found via the solar radiation of Karachi. In the second architecture scheme, we used three inputs and ten neurons' hidden layers to find the solar radiation of a city in the region bounded by three cities lying at the vertices of a triangle. Multan was assumed as such an area; its neighboring cities, Lahore, Quetta, and Karachi, form a triangle, and Multan lies inside the triangle area. In both architectures, three types of solar radiation, DSR, BSR, and GSR, were found with an accuracy of 99.8 %. The coefficient of determination in each case was 0.99. It shows that the results obtained through ANN models agree well with the known values.&lt;/p&gt;&#x0D;
Surveillance of Malaria Cases among Residents of Villages of Tarwa Block, Azamgarh

Malaria is a protozoal disease caused by infection with parasites of genus plasmodium and transmitted to man by certain species of infected female anopheles mosquito. This mosquito is mainly found in tropical countries. Study was conducted in villages of Tarwa block, Azamgarh Uttar Pradesh, India. It was a cross sectional study where total 50 cases were studied. It was found that maximum affected population were from 10-45 year age group. Also found that all cases were suffering from fever followed by myalgia (82%), headache (78%) and retro-orbital pain in 73% of cases. During this surveillance study it was found that main source of mosquito breeding were standing water, containers, drains and gutters, water storage tanks, bird baths, flower pot saucers, ponds and lakes, pools, septic tank and sewage and marshes and wetlands.
A study on the path to enhance the information literacy ability of college students in the context of education informatization

At present, China has entered a digital society and the development of information technology requires us to master a certain level of information literacy.Information literacy, as a high-level part of thinking consciousness, is particularly important for people's lifelong learning, especially in education, where information literacy has become one of the important indicators for evaluating the quality of education in China.This paper firstly analyses the current situation of research on information literacy among college students through literature research method, and then analyses the current problems of information literacy among college students through questionnaire method.According to the survey results, an information literacy assessment system for college students was designed to explore the path of improving college students' information literacy ability, and to improve the information literacy ability of college students and the teaching quality of teachers to a certain extent.
Study on countermeasures to enhance adaptability of vocational education in Hubei higher vocational colleges

Adaptability of higher vocational education in Hubei province has experienced three stages of the early germination, the expansion of the denotation, the ascension of the connotation.But the management system of higher vocational education is not perfect, which hinders the in-depth development of higher vocational education in Hubei province.The integration of industry and education is not deep enough, and school-enterprise cooperation is still at a low level in Hubei province.The teaching mode of higher vocational colleges is backward, which can not support the development of modern industry and high-quality economy of Hubei province.Higher vocational education has failed to highlight the type characteristics, and higher vocational education is still in a secondary position.Hubei Province should promote the reform of higher vocational education system and promote the development of higher vocational education governance to depth.The higher vocational colleges in Hubei province should take the idea of service adaptation as the core and enhance the adaptability of the basic point of higher vocational education.Hubei Province should take the reform of the integration of industry and education as an opportunity to promote the transformation and upgrading of Hubei industry and enhance the adaptability of the education mode of higher vocational colleges.The higher vocational colleges in Hubei province should strengthen the adaptability of all factors in running schools and consolidate the foundation of talent training quality.The higher vocational colleges in Hubei province should strengthen adaptability to improve the modern vocational education system.
Penerapan Model Pembelajaran Kooperatif Gallery Walk untuk Meningkatkan Hasil Belajar Bahasa Inggris Siswa

The goal to be achieved in this study is to find out the application of the Gallery Walk learning model in improving English learning outcomes on the subject of Personal Letter for class XI IPA MAN 3 Bone, Bone Regency. This research is a qualitative descriptive research designed through class room action research. This classroom action research was carried out in two cycles and was carried out in class XI IPA 6 MAN 3 Bone, Bone Regency, for the 2021/2022 academic year, odd semester. The subjects of this study amounted to 31 people. The research instruments were tests and observation sheets. Data were analyzed quantitatively and qualitatively. The results of this study indicate that the Gallery Walk learning model is effectively applied in improving English learning outcomes on the subject of Personal Letter for class XI IPA 6 MAN 3 Bone, Bone Regency. This shows an increase in student scores. In the first cycle, the students' grades and activities were categorized as low with 45.16% completeness which was able to get a score of 70 and above, while in the second cycle, the students' values ​​and activities were categorized as high with 84.85% completeness, which was able to get a score of 75 and above.
Prediction of student online Learning performance based on Machinelearning

This essay uses machine learning methods to predict students’ online learning performance. The characteristicsof student’s characteristics, learning behavior characteristics, and environmental factors are extracted by featureengineering, and the random forest model is used for prediction. The experimental results show that the predictionaccuracy of the random forest model reaches 0.82, which provides an effective means for the prediction of onlinelearning performance.
Practice of providing psychological assistance to law enforcement officers in extreme (crisis) situations

Aim: To identify approaches to the practice of providing psychological assistance to law enforcement officers in extreme (crisis) situations. Material and methods: Research methods: bibliosemantic, forecasting, comparative, analysis, and generalization. Clarification of the etymology of extreme (crisis) situations allowed us to identify approaches and techniques of psychological assistance to law enforcement officers after their stay in extreme (crisis) situations and stressful conditions. Law enforcement officers often experience such emotions in extreme situations and stressful conditions as fear, anger, powerlessness, despair, and confusion. The use of psychological debriefing in work with law enforcement officers can help overcome the negative effects of stress after a crisis incident, as well as develop skills that may be needed in the event of a repeated encounter with similar events. Conclusions: The toolkit of professional psychological assistance to law enforcement officers should focus primarily on increasing their communicative open¬ness and social confidence. The psycho-correctional practice involves the best possible adaptation of law enforcement officers to the requirements of an extreme situation, enabling them to master it, weaken or mitigate these requirements, and try to avoid or get used to them.
Research on Blockchain Cross-chain Mechanism and Application

With the emergence of Ethereum smart contracts, the number of blockchain platforms and decentralized applications has increased rapidly.However, most of the existing blockchain networks operate in an isolated environment, which brings about the scalability and connectivity issues of the blockchain platform and limits its development.Blockchain cross-chain technology has attracted much attention since the birth of Bitcoin.Its purpose is to realize the interconnection between chains and increase the interoperability of blockchains.It is the essential capability of the future blockchain interconnection network.This article systematically analyzes and summarizes the implementation schemes and projects of cross-chain technology.Firstly, it introduces the background and development history of blockchain cross-chain technology and explains the basic scheme of the cross-chain mechanism.Next, it elaborates on the implementation of three cross-chain applications.In the summary section, the advantages and disadvantages of the cross-chain mechanism and multiple cross-chain applications are summarized.Finally, several future research directions of cross-chain technology are listed.