Reviewerly/exHarmony huggingface dataset:
Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers

Key-phrase extraction concerns retrieving a small set of phrases that encapsulate the core concepts of an input textual document. As in other text mining tasks, current methods often rely on pre-trained neural language models. Using these models, the state-of-the-art supervised systems for key-phrase extraction require large amounts of labelled data and generalize poorly outside the training domain, while unsupervised approaches generally present a lower accuracy. This paper presents a multilingual unsupervised approach to key-phrase extraction, improving upon previous methods in several ways (e.g., using representations from pre-trained Transformer models, while supporting the processing of long documents). Experimental results on datasets covering multiple languages and domains attest to the quality of the results.
Supply Chain Management—A Case Study of Huawei’s Supply-Chain Chip Shortage

Today’s business environment is more competitive and complex. Decreased trade barriers and more developed countries promoting businesses bring more businesses into the market. There is also more competition among organizations because businesses are introducing newer and cheaper innovations at a faster rate Innovation in organizational processes can make significant changes in the product output. Supply chain management is a potential area for technological innovations. Therefore, this paper will focus on the extent to which automation technology and blockchain technology support modern supply chains, and take Huawei as an example to study how Huawei’s global supply chain provides it with a competitive advantage. And makes a study on the current supply chain dilemma faced by Huawei and puts forward feasible solutions. The study found that automation can benefit enterprises by achieving faster and more efficient operations in inventory management and warehouse management. However, enterprises also need to be aware of the high cost of using automation. Blockchain technology can improve supply chain stability and security through smart contracts and shared ledger management. But there are also problems, such as a lack of laws and regulations that reduce the overall efficiency of the supply chain. Huawei’s use of reverse logistics and management of distribution channels have enhanced its supply chain’s competitive edge, but now it faces a chip shortage. For the current Huawei, finding backup suppliers, stabilizing consumer confidence, and maintaining investment in chip research and development are feasible ways to solve the existing problems.
Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains

Qualitative modeling can be applied to the control of dynamic systems by following these steps: (1) learning a qualitative model of the controlled dynamic system from the system's behaviors in time, (2) using the learned model to derive a qualitative plan for the control task, and (3) executing the qualitative plan on an actual dynamic system. This approach has been demonstrated in the usual cart-pole control domain as significantly more sample efficient than the usual variants of reinforcement learning, by at least two orders of magnitude. The qualitative approach also enables better explanation of the learned control strategy through symbolic planning. In this paper, we generalize the cart-pole problem to uneven terrains, such as driving over a crater or a hill. We study whether the learned flat-surface qualitative controller can be successfully transferred to the tasks of negotiating uneven terrain. Experiments show that the flat-surface qualitative controller is remarkably robust on new, more difficult tasks.
MACO: A Modality Adversarial and Contrastive Framework for Modality-Missing Multi-modal Knowledge Graph Completion

Recent years have seen significant advancements in multi-modal knowledge graph completion (MMKGC). MMKGC enhances knowledge graph completion (KGC) by integrating multi-modal entity information, thereby facilitating the discovery of unobserved triples in the large-scale knowledge graphs (KGs). Nevertheless, existing methods emphasize the design of elegant KGC models to facilitate modality interaction, neglecting the real-life problem of missing modalities in KGs. The missing modality information impedes modal interaction, consequently undermining the model’s performance. In this paper, we propose a modality adversarial and contrastive framework (MACO) to solve the modality-missing problem in MMKGC. MACO trains a generator and discriminator adversarially to generate missing modality features that can be incorporated into the MMKGC model. Meanwhile, we design a cross-modal contrastive loss to improve the performance of the generator. Experiments on public benchmarks with further explorations demonstrate that MACO could achieve state-of-the-art results and serve as a versatile framework to bolster various MMKGC models.
Retrieval-Augmented Knowledge-Intensive Dialogue

Large pre-trained language models have been shown to be powerful in open-domain dialogue. However, even the largest dialogue models suffer from knowledge hallucination, generating statements that are plausible but factually incorrect. Recent works, such as RAG and FiD, have introduced retrieval methods to alleviate this issue by bringing in external knowledge sources. Based on this research direction, we propose a plug-and-play method to enhance the generator’s performance by introducing demonstration-based learning, which allows the generator to better understand the task. Furthermore, we propose a novel representation-interaction ranking model, RM-BERT, inspired by residual connections, which can more effectively represent the semantic information of context and document to improve retrieval accuracy. Experimental results indicate that the generator improvement method is applicable to multiple datasets and multiple models simultaneously. Additionally, we demonstrate that RM-BERT achieves performance close to BERT while significantly reducing computational overhead.
Interest Aware Dual-Channel Graph Contrastive Learning for Session-Based Recommendation

The key issue of session-based recommendation (SBR) is how to efficiently predict the next interaction item based on the item sequence of anonymous users. In order to mine the complex multivariate relationship between items and sessions, we propose a novel model for session-based recommendation named Interest aware Dual-channel Graph Contrastive learning (IDGC). By generating hypergraph and global graph, we focus on item relationships in different aspects, and we create the dual-channel interest-item embedding learning module to dig the higher-order relationships between items and users’ interests. To deal with the problem of long-distance information transmission between non-adjacent items, we set the interest node in each session for interest awareness and base on the contrastive learning strategy to enrich the information of the two graphs. At the same time, we exploit position information and time interval information to enhance the session representation. Extensive experiments show that IDGC has significant performance improvement on all evaluation metrics on three benchmark datasets.
Learning to Attentively Represent Distinctive Information for Semantic Text Matching

Pre-trained language models (PLMs) such as BERT have achieved remarkable results in the task of Semantic Text Matching (STM). Nevertheless, existing models face challenges in discerning subtle distinction between texts, although it is the vital clue for STM. Concretely, the alteration of a single word causes significant variation of semantics of the entire text. To solve the problem, we propose a novel method of attentively representing distinctive information for STM. It comprises two components, including Reversed Attention Mechanism (RAM) and Sample-based Adaptive Learning (SAL). RAM reverses the hidden states of texts before computing attentions, which contributes to the highlighting of mutually-different syntactic constituents when comparing texts. In addition, during the initial stage of training, the model may acquire some biases. For example, it may ignore the distinctions between sentence pairs and simply classify sentence pairs with high lexical overlap as positive examples, because a majority of positive examples exhibit such high lexical overlap. SAL is designed to facilitate the model in comprehensively acquiring the semantic knowledge hidden in the distinctive constituents. Experiments on six STM datasets demonstrate the effectiveness of our proposed approach. Furthermore, we employ ChatGPT to generate textual descriptions of distinction between texts and empirically validate the significance of distinctive information in semantic text matching task.
A Task-Oriented Dialog Model with Task-Progressive and Policy-Aware Pre-training

Pre-trained conversation models (PCMs) have achieved promising progress in recent years. However, existing PCMs for Task-oriented dialog (TOD) are insufficient for capturing the sequential nature of the TOD-related tasks, as well as for learning dialog policy information. To alleviate these problems, this paper proposes a task-progressive PCM with two policy-aware pre-training tasks. The model is pre-trained through three stages where TOD-related tasks are progressively employed according to the task logic of the TOD system. A global policy consistency task is designed to capture the multi-turn dialog policy sequential relation, and an act-based contrastive learning task is designed to capture similarities among samples with the same dialog policy. Our model achieves better results on both MultiWOZ and In-Car end-to-end dialog modeling benchmarks with only 18% parameters and 25% pre-training data compared to the previous state-of-the-art PCM, GALAXY. We make our code and data publicly available ( https://github.com/lucenzhong/TPLD ).
TCRec: A novel paper recommendation method based on ternary coauthor interaction

With the explosion in the number of papers being generated, it has become a common practice to recommend papers related to research interests of authors. Existing methods mainly focus on using binary author–paper interactions to mine the research interests of authors. However, sparse binary interactions make it extremely difficult to distinguish the authors’ research interests. This results in an inability to find papers that authors need. Generally, the research interests of coauthors are highly correlated and this is useful in broadening the distinctions between authors’ research interests. Therefore, we propose a new ternary coauthor recommendation (TCRec) method for paper recommendation. Specifically, we construct a coauthor ternary to describe the coauthorship between authors to explore potential ternary interactions. First, an attention-based bidirectional long short-term memory (Bi-LSTM) is built to learn title and abstract information to represent the research area of a paper and accordingly initialize the research interests for each author. Then, an academic heterogeneous graph with a dual attention mechanism is designed to aggregate the semantic information of neighboring nodes and metapaths. This mechanism can be employed to aggregate the relationships between authors and papers in the academic network to describe the research interests for authors. Finally, after constructing the ternary, a multilayer perceptron (MLP) is applied to mine potential associations between the ternary to enrich author–paper interactions for paper recommendation. Extensive experimental results from two real academic datasets not only show the superior performance of our model over state-of-the-art approaches but also demonstrate its potential value for paper recommendation.
CCPC: A Hierarchical Chinese Corpus for Patronizing and Condescending Language Detection

Patronizing and Condescending Language (PCL) is a form of implicitly toxic speech aimed at vulnerable groups with the potential to cause them long-term harm. As an emerging field of toxicity detection, it still lacks high-quality annotated corpora (especially in the Chinese field). Existing PCL datasets lack fine-grained annotation of toxicity level, resulting in a loss of edge information. In this paper, we make the first attempt at fine-grained condescending detection in Chinese. First, we propose CondescendCN Frame, a hierarchical framework for fine-grained condescending detection. On this basis, we introduce CCPC, a hierarchical Chinese corpus for PCL, with 11k structured annotations of social media comments from Sina Weibo and Zhihu. We find that adding toxicity strength (TS) can effectively improve the detection ability of PCL and demonstrate that the trained model still retains decent detection capabilities after being migrated to a larger variety of media data (over 120k).Due to the subjective ambiguity of PCL, more contextual information and subject knowledge expansion are critically required for this field.
Peran dan Tantangan Penggunaan E-Learning sebagai Pendukung Proses Pembelajaran di Perguruan Tinggi: Sebuah Tinjauan Literatur

The use of E-Learning as a support for the learning process in higher education has become a significant research focus. This article employs a literature review method to analyze the benefits of E-Learning, the various formats of learning that can be delivered, online collaboration and discussion, as well as the challenges that may arise in its implementation. Based on the findings from the literature, the use of E-Learning enhances the accessibility of learning in higher education, provides customizable learning formats to cater to students' learning styles, and facilitates interaction and collaboration between students and instructors. However, challenges such as limited internet access and the paradigm shift in teaching need to be addressed. This article provides recommendations to improve infrastructure, train instructors, meet students' needs, and overcome challenges. With appropriate implementation, E-Learning can have a positive impact on higher education. Keywords: E-Learning, higher education, accessibility, learning formats, online collaboration.&#x0D; &#x0D; Abstrak&#x0D; Penggunaan E-Learning sebagai pendukung proses pembelajaran di perguruan tinggi telah menjadi fokus penelitian yang signifikan. Artikel ini menggunakan metode riset literatur untuk menganalisis manfaat penggunaan E-Learning, format pembelajaran yang dapat disajikan, kolaborasi dan diskusi daring, serta tantangan yang mungkin muncul dalam implementasinya. Berdasarkan temuan literatur, penggunaan E-Learning meningkatkan aksesibilitas pembelajaran di perguruan tinggi, memberikan format pembelajaran yang dapat disesuaikan dengan gaya belajar mahasiswa, dan memfasilitasi interaksi dan kolaborasi antara mahasiswa dan dosen. Namun, tantangan seperti keterbatasan akses internet dan perubahan paradigma pengajaran perlu diatasi. Artikel ini memberikan saran untuk meningkatkan infrastruktur, pelatihan dosen, mendukung kebutuhan mahasiswa, dan mengatasi tantangan yang muncul. Dengan implementasi yang tepat, penggunaan E-Learning dapat memberikan dampak positif dalam pendidikan tinggi.&#x0D;
First Steps to Improve Cybersecurity Behaviour – a Virtual Reality Experience

Internet is completely integrated and absorbed in our life. Facilitating transfer of files across the world or wiring money from the couch, we could not imagine a world without it anymore. With these benefits, as with any new technology, there is also the introduction of risks and threats, for internet primarily in the form of cybercrime and online fraud. To reduce victimisation of this cybercrime, interventions are used to teach people to not perform risky behaviour. To overcome criticisms of current training materials, such as being tedious and boring, we created an Immersive Virtual Reality experience. By using a 4-step design process (i.e. ideation, specification, realisation, and evaluation), we designed a playful VR environment with simplistic non player characters to train the user to perform basic cybersecurity tasks in the right way. In the simulation, the participants are exposed to the challenge of creating a new password and a potential ransomware attack using USB storage device. The program allows for monitoring the user’s cybersecurity knowledge and behaviour and provides feedback. An evaluation of the VR environment among 16 respondents using a pretest-posttest evaluation with the Human Aspect Information Security Questionnaire (HAIS-Q) showed a statistically significant increase in scores after exposure to the VR environment. The system showed an above average SUS score. These initial findings indicate that a VR environment can be an alternative to consider for future development of cybersecurity interventions. Future research could expand our social VR environment with additional cybersecurity challenges, real-time actors, and running simulations among a broader audience to also investigate the retention of knowledge and skills.
SPAW-SMOTE: Space Partitioning Adaptive Weighted Synthetic Minority Oversampling Technique For Imbalanced Data Set Learning

Abstract The problem of data imbalance is common in reality, which greatly affects the performance of classifiers. Most of the solutions are to balance the data set by generating new minority class samples, which are faced with the problems of selecting the appropriate area for generating samples, fuzzy classification boundary and uneven distribution of samples. To solve these problems, we propose a novel oversampling algorithm named space partitioning adaptive weighted synthetic minority oversampling technique (SPAW-SMOTE). We first divide the data space into boundary space and non-boundary space based on spatial partitioning techniques. The number of samples to be generated is assigned to different spaces by the designed adaptive weighting algorithm, which is used to solve the problems of uneven distribution of samples and easy to blur the classification boundary. Finally, we also endeavor to develop a new generation algorithm to reduce the probability of overlapping samples generated when synthesizing new samples and to ensure the diversity of new samples. Experimental results on 18 real-world data sets show that the average performance (G-mean, F1-measure and Area Under Curve) of SPAW-SMOTE is significantly better than other existing oversampling techniques.
Development of Marketing in the ecosystem of the University: economic and socio-Psychological aspects

The relevance of the chosen research topic is due to the fact that the market economy, increased competition and increased requirements for efficiency have made universities need to improve all aspects of their activities, ensuring competitiveness in order to develop and obtain additional resources. A well-built marketing strategy plays a key role in this process, which should be based on the most modern promotion tools and must take account of the characteristics of the target audience, for the university — these are the applicants, students and their parents. The article deals with the study of the role of marketing in the development of the university ecosystem. The definition of marketing in the field of higher education is given, the main subjects of marketing in educational activities, the tasks of marketing in the development of the university ecosystem, the principles of marketing educational services are considered, and effective tools for promoting the university are identified. A sociocultural and psychological portrait of a modern applicant and student is also presented, the main aspects of the motivation of the younger generation are identified, recommendations are given for building marketing communications, taking special account the characteristics of modern youth. On the basis of the study, the authors identify effective tools for promoting the university, describe the stages of developing a strategy, and present key aspects of building marketing for an educational organization.
Social Competencies in the development of the Professional Potential of Young People

The article is devoted to assessing the need of the labor market for personnel with certain flexible skills, the so-called “soft-skills”. A literary review of scientific papers devoted to similar research issues is carried out, based on the data obtained, an expanded concept of the term “social competence” is presented. A detailed analysis of existing employers’ requests for the possession of a particular social competence by potential employees in the current conditions has revealed a number of “basic” and “composite” social competencies, which will allow further research to study the peculiarities of the combination of certain positions when considering employers’ requirements for candidates for vacant positions in various industries and spheres.
On the significance of Modern social and behavioral ontologies

As challenges and uncertainties and risks are forming, the competition of countries for a better future is intensifying, and the number of studies and publications of multimillion global scientific community is growing, the role and importance of adequate description, accessibility and practical use of global knowledge is continuously increasing. Ontologies are becoming a tool for organizing both scientific and practical activities. As the world develops and becomes more complex, the importance and role of human beings grows — accordingly, the significance of ontologies in the social and behavioral sciences and the possibilities of their application for the purposeful creation of a better future grows significantly. Mastering modern digital approaches to creating dynamic ontologies and their application becomes the key to the development of a globally significant unique national project to create a better future for Russia and competitive strategic management of its sustainability.
Advertising opportunities in the Metaverse

Successful cases of using the metaverse as a promotion channel are discussed in the article. However, States and governments have seen serious risks that need to be addressed to ensure security. In the course of the work, these threats were identified and described in detail. The successful practice of using the metaverse by companies and the presence of serious problems of its development create contradictions that require study. Thus, the purpose of this work is to answer the question whether the metaverse is a promising channel of promotion for business. In order to achieve it, an analysis of the report of the largest bank JPMorgan in 2022 with forecasts of the prospects of metaverses for business promotion was carried out. In the future, the next step will be to investigate whether the major and largest media- outlets are using the metaverse to promote their content. Perhaps it will become a new communication platform, just like once innovative messengers, marketplaces and ecosystems. To test this hypothesis, it is planned to conduct expert interviews with the marketing director of Sensorium Galaxy, Yandex, Sber and VKontakte.