Reviewerly/exHarmony huggingface dataset:
Multi-relation global context learning for session-based recommendation

Purpose Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users. Design/methodology/approach This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight. Findings We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model. Originality/value First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.
Is Cross-Modal Information Retrieval Possible Without Training?

Encoded representations from a pretrained deep learning model (e.g., BERT text embeddings, penultimate CNN layer activations of an image) convey a rich set of features beneficial for information retrieval. Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semantically aligned to another by a simple mapping without training a deep neural net. In this paper, we take a simple mapping computed from the least squares and singular value decomposition (SVD) for a solution to the Procrustes problem to serve a means to cross-modal information retrieval. That is, given information in one modality such as text, the mapping helps us locate a semantically equivalent data item in another modality such as image. Using off-the-shelf pretrained deep learning models, we have experimented the aforementioned simple cross-modal mappings in tasks of text-to-image and image-to-text retrieval. Despite simplicity, our mappings perform reasonably well reaching the highest accuracy of 77% on recall@10, which is comparable to those requiring costly neural net training and fine-tuning. We have improved the simple mappings by contrastive learning on the pretrained models. Contrastive learning can be thought as properly biasing the pretrained encoders to enhance the cross-modal mapping quality. We have further improved the performance by multilayer perceptron with gating (gMLP), a simple neural architecture.
Machine Learning with Probabilistic Law Discovery: a Concise Introduction

Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. In several aspects, PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. The learning procedure of PLD solves the optimization problem related to the search for rules (called probabilistic laws), which have a minimal length and relatively high probability. At inference, ensembles of these rules are used for prediction. Probabilistic laws are human-readable and PLD based models are transparent and inherently interpretable. Applications of PLD include classification/clusterization/regression tasks, as well as time series analysis/anomaly detection and adaptive (robotic) control. In this paper, we outline the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.
A Brief Discussion on the Logic Problems in Chinese-English News Compilation: Taking China Daily as an Example

In recent years, Chinese government has emphasized the importance of strengthening China's communication ability and forming a discourse power equal to China's international status.China Daily is China's national English daily with an average circulation of more than 900,000 copies worldwide, serving as an important window for China's external publicity.The biggest problem faced by the translator during her working in China Daily is how to accurately translate Chinese news into English.Therefore, this paper is going to discusses how to improve Chinese-English news translation by using the method of case analysis.The case analysis materials in this paper are all from the author's English manuscripts when working in China Daily Website, involving the propaganda and economic development of local provinces and cities.
Research on the Teaching of Piano Accompaniment Arrangement Course in Colleges and Universities

Piano accompaniment is one of the most common and fast forms of musical accompaniment.It requires the performer to use the piano theory knowledge and performance skills to integrate techniques in the keyboard and take into account the harmony, so that the music is presented with more full and abundant emotions.In the practice of piano teaching in colleges and universities, piano accompaniment arrangement is a compulsory course for the development of students' musicianship.However, in the current teaching practice of the course, students' ability to arrange accompaniment is insufficient due to the differences in students' musicianship and the teachers' teaching methods in the course.In response to this, this paper briefly explores the teaching strategies from the current situation of piano accompaniment arrangement course teaching in colleges and universities, combined with the actual teaching practice. 1.
Analysis and Application of Korean Language Textbook—Focusing on Illustrations

This article aims to explore the importance and role of illustrations in teaching materials.Through an analysis of the quantity, location, and provided information of illustrations in the Korean language textbook "Standard Korean 3", the key role of illustrations in teaching materials is revealed, emphasizing the importance of teachers' attention to the role of illustrations in Korean language teaching.The function of illustrations is not only to guide learners into a learning state, help them understand and remember course content, but also to provide a more comfortable and relaxed learning environment, assist learners in understanding abstract concepts, stimulate learning interests and motivation, and provide cultural background knowledge.Therefore, the role of illustrations in language teaching materials is crucial, as they can not only enhance learners' learning interests and motivation but also help learners better understand and remember course content, thereby improving the effectiveness of Korean language learning.In Korean language teaching, teachers should fully utilize illustrations and incorporate them as an important component of the teaching process.
On the Prediction Instability of Graph Neural Networks

Instability of trained models, i.e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems. In this paper, we systematically assess the prediction instability of node classification with state-of-the-art Graph Neural Networks (GNNs). With our experiments, we establish that multiple instantiations of popular GNN models trained on the same data with the same model hyperparameters result in almost identical aggregated performance, but display substantial disagreement in the predictions for individual nodes. We find that up to 30% of the incorrectly classified nodes differ across algorithm runs. We identify correlations between hyperparameters, node properties, and the size of the training set with the stability of predictions. In general, maximizing model performance implicitly also reduces model instability.
Charge Own Job: Saliency Map and Visual Word Encoder for Image-Level Semantic Segmentation

Significant advances in weakly-supervised semantic segmentation (WSSS) methods with image-level labels have been made, but they have several key limitations: incomplete object regions, object boundary mismatch, and co-occurring pixels from non-target objects. To address these issues, we propose a novel joint learning framework, namely Saliency Map and Visual Word Encoder (SMVWE), which employs two weak supervisions to generate the high-quality pseudo labels. Specifically, we develop a visual word encoder to encode the localization map into semantic words with a learnable codebook, making the network generate localization maps containing more semantic regions with the encoded fine-grained semantic words. Moreover, to obtain accurate object boundaries and eliminate co-occurring pixels, we design a saliency map selection mechanism with the pseudo-pixel feedback to separate the foreground from the background. During joint learning, we fully utilize the cooperation relationship between semantic word labels and saliency maps to generate high-quality pseudo-labels, thus remarkably improving the segmentation accuracy. Extensive experiments demonstrate that our proposed method better tackles above key challenges of WSSS and obtains the state-of-the-art performance on the PASCAL VOC 2012 segmentation benchmark.
AD-AUG: Adversarial Data Augmentation for Counterfactual Recommendation

Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply in practice due to the sparsity and bias of the real-world user feedback data. In this paper, we propose a novel counterfactual data augmentation framework AD-AUG to mitigate the impact of the imperfect training data and empower CF models. The key idea of AD-AUG is to answer the counterfactual question: “what would be a user’s feedback if his previous purchase history had been different?”. Our framework is composed of an augmenter model and a recommender model. The augmenter model aims to generate counterfactual user feedback based on the observed ones, while the recommender leverages the original and counterfactual user feedback data to provide the final recommendation. In particular, we design two adversarial learning-based methods from both “bottom-up” data-oriented and “top-down” model-oriented perspectives for counterfactual learning. Extensive experiments on three real-world datasets show that the AD-AUG can greatly enhance a wide range of CF models, demonstrating our framework’s effectiveness and generality.
ARES: Locally Adaptive Reconstruction-Based Anomaly Scoring

How can we detect anomalies: that is, samples that significantly differ from a given set of high-dimensional data, such as images or sensor data? This is a practical problem with numerous applications and is also relevant to the goal of making learning algorithms more robust to unexpected inputs. Autoencoders are a popular approach, partly due to their simplicity and their ability to perform dimension reduction. However, the anomaly scoring function is not adaptive to the natural variation in reconstruction error across the range of normal samples, which hinders their ability to detect real anomalies. In this paper, we empirically demonstrate the importance of local adaptivity for anomaly scoring in experiments with real data. We then propose our novel Adaptive Reconstruction Error-based Scoring approach, which adapts its scoring based on the local behaviour of reconstruction error over the latent space. We show that this improves anomaly detection performance over relevant baselines in a wide variety of benchmark datasets.
An Anti-collision Early Warning and Detection System for Vehicle Visual Blind Area and Its Control Method

This paper designs a collision prevention warning detection system and proposes a control method based on this system.The system and its control method have the paired photoelectric signal transmitting unit and the photoelectric signal receiving unit around the vehicle to detect and prevent the collision in the the driver's visual blind area.The system and its control method can realize the real-time voice alarm inside and outside the vehicle, so as to improve the driving safety and reduce the crash accident during the vehicle driving.
B-LBConA: a medical entity disambiguation model based on Bio-LinkBERT and context-aware mechanism

The main task of medical entity disambiguation is to link mentions, such as diseases, drugs, or complications, to standard entities in the target knowledge base. To our knowledge, models based on Bidirectional Encoder Representations from Transformers (BERT) have achieved good results in this task. Unfortunately, these models only consider text in the current document, fail to capture dependencies with other documents, and lack sufficient mining of hidden information in contextual texts.We propose B-LBConA, which is based on Bio-LinkBERT and context-aware mechanism. Specifically, B-LBConA first utilizes Bio-LinkBERT, which is capable of learning cross-document dependencies, to obtain embedding representations of mentions and candidate entities. Then, cross-attention is used to capture the interaction information of mention-to-entity and entity-to-mention. Finally, B-LBConA incorporates disambiguation clues about the relevance between the mention context and candidate entities via the context-aware mechanism.Experiment results on three publicly available datasets, NCBI, ADR and ShARe/CLEF, show that B-LBConA achieves a signifcantly more accurate performance compared with existing models.
Semantic Segmentation Algorithm of Night Images Based on Attention Mechanism

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm that combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the images, and the decoder divides the fused images output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract images information. Experiments are carried out on publicly available thermal infrared and visible datasets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment.
Virtual Laboratory Design for Learning Electro-Pneumatic Practices in Vocational High Schools

&lt;p style="text-align:justify"&gt;Teaching a subject in a vocational high school (VHS) requires specialized instructional design strategies. Recently, instructional VHS used a computer-based platform to teach theory and practice. The computer will assist teachers in creating instructional media. This study aims to design augmented reality-based virtual laboratory media for electro-pneumatic practicum learning in order to ease teachers and students to learn the pneumatic practice. It is specially designed for practical learning purposes. The method used in this research is research and development. The assessment results from Instructional design experts 4.5, material experts 4.5, media experts 4.4, and language experts 4.8. Based on the assessment of experts, this augmented reality-based virtual laboratory media is feasible to use. Next, the results of this study are a prototype of students' pneumatic practice tools installed on mobile phones consisting of eight worksheets. In the designed augmented reality media, 1) 3-dimensional pneumatics can be rotated in all directions so that students understand. 2) Pneumatic job sheet, where on the augmented reality media designed for the simulation circuit, there is an explanation of the components and how they work, and a simulation through markers.&lt;/p&gt;
The development of angiosperm anatomical ATLAS as laboratory identification reference in plant tissue structure and function for senior high school students grade XIth

This study aims to determine the feasibility of the Angiosperm Anatomical Atlas as the identification guidelines in laboratory activity of structures and functions of plant tissues for grade 11 of high school based on the result of experts' assessments and readability tests of biology teachers and students. This study is a Research and Development (R&D) research with ADDIE (Analysis, Design, Development, Implementation, Evaluation) as the model (Robert Maribe Branch, 2009) which is limited to the stage of ADD (Analysis, Design, Development). The subjects of the research are one material expert, one media expert, two high school biology teachers, and 30 Natural Science students of XIIth. The research object is the prototype of the Angiosperm Anatomical Atlas. The data collection was done using assessment instruments for a material expert, a media expert, biology teachers, and also students. The result of the study was analyzed qualitatively. The result of the study showed that all the Atlas assessment indicator which suitable with the standard of BSNP have been fulfilled. Thus, it is concluded that Atlas has fulfilled the assessment indicator and feasible to be used as the learning media of laboratory activity.
File Storage System using Hybrid Cryptography

Now a days file storage system is used in many areas where we can retrieve the data on the users request However, providing our sensitive, personal data to a third party is not exactly the best practice. The only alternative is to make a storage system of our own but that can be tedious to do so and also even if achieved the major stumbling trying to create a secure file storage system is using cryptography, even though it is an excellent and only security measure, but there is a significant drawback in using such technique. Cryptography is based primarily on generation and the proper using of keys if those keys are accidentally leaked or stolen then the entire data, data security is destroyed. A perfect solution is the use of hybrid cryptography in which current encryption is used in combination for additional security. Hybrid cryptography is a concept where we use multiple encryption algorithms to encrypt the file in such a way that the information security is massively improved. Instead of having one good algorithm we will now have multiple ‘good’ algorithms working in unison to protect the data.
Face Recognition Systems

Abstract: Face recognition systems have become increasingly popular and important in recent years due to their various applications in security, surveillance, and human-computer interaction. These systems use algorithms to detect and recognize human faces in images or videos, and can be trained to identify individuals with high accuracy.
Assessment of the student community’s opinion on the prospects and challenges of implementing Priority-2030 program

The work is devoted to assessing the opinion of the student community of Southwestern State University on the implementation of the strategic academic leadership program “Priority-2030”. The author’s sociological research was carried out, which allowed to identify the characteristics of the educational process and tools for evaluating its effectiveness. The prospects, possible problems and risks that the university may face during the implementation of the Priority-2030 program are studied.
The Effect of Web-Based Tracheostomy Care Game on Nursing Students’ Knowledge Levels and Their Views of The Process

Objective: This study investigated nursing students’ views of the effect of a web-based tracheostomy care game on their knowledge levels during the COVID-19 pandemic.&#x0D; Methods:This pretest-posttest single-group quasi-experimental study was conducted between April and July 2021. The study population consisted of all nursing students in Turkey. The sample consisted of 125 students who filled out the pretest and posttest forms. Participation was voluntary. Participants were recruited using snowball sampling method. Participants entered the website “trakeostomibakimi.com” They filled out the demographic characteristics questionnaire and the tracheostomy care knowledge test (TCKT) on the website. They downloaded the virtual tracheostomy care game on their computers. They played the game as much as they wanted and then filled out the posttest and the student opinion forms.&#x0D; Results: Participants had a higher mean posttest TCKT score than pretest score (p&lt; .05). They stated that the game helped them enjoy learning tracheostomy care and remember their prior knowledge and made them feel like they practiced in a real-life clinical setting.&#x0D; Conclusion: The web-based tracheostomy care game improved nursing students’ knowledge levels. There should also be online educational games tailored to other nursing areas.
A Multi-source Information Graph-based Web Service Recommendation Framework for a Web Service Ecosystem

Web service recommendation remains a highly demanding yet challenging task in the field of services computing. In recent years, researchers have started to employ side information comprised in a heterogeneous Web service ecosystem to address the issues of data sparsity and cold start in Web service recommendation. Some recent works have exploited the deep learning techniques to learn user/Web service representations accumulating information from multiplex sources. However, we argue that they still struggle to utilize multi-source information in a discriminating, unified and flexible manner. To tackle this problem, this paper presents a novel multi-source information graph-based Web service recommendation framework (MGASR), which can automatically and efficiently extract multifaceted knowledge from the heterogeneous Web service ecosystem. Specifically, different node-type and edge-type dependent parameters are designed to model corresponding types of objects (nodes) and relations (edges) in the Web service ecosystem. We then leverage graph neural networks (GNNs) with an attention mechanism to construct a multi-source information neural network (MIN) layer, for mining diverse significant dependencies among nodes. By stacking multiple MIN layers, each node can be characterized by a highly contextualized representation due to capturing high-order multi-source information. As such, MGASR can generate representations with rich semantic information toward supporting Web service recommendation tasks. Extensive experiments conducted over three real-world Web service datasets demonstrate the superior performance of our proposed MGASR as compared to various baseline methods.