Reviewerly/exHarmony huggingface dataset:
The impact of the characteristics of the smart organization in promoting competitive superiority An applied study of a sample of private banks in Baghdad

Based on what the science of business administration is witnessing from the developments of information in the concepts and new and organized ideas of the most important concepts, which is one of the most important pillars of competition and the emergency photo gallery, slaughter the information. The goal is (52) the information through the questionnaire(77), and the data was analyzed according to the (spss) program. The research reached a set of results. The existence of a merit culture that focuses on creative behavior and the adoption of a culture of excellence will contribute and affect effectively the achievement of competitive advantage. In conclusion, he presented a set of recommendations, the most prominent of which was the necessity of adopting the research sample banks a cost leadership strategy by providing high quality services at a low cost compared to competitors in order to achieve customer satisfaction and to contribute to achieving competitive advantage.
Impact of Agent Language on Student Language in the Structures of Language Connections

This study explores the impact of conversational agent language (formal vs. informal) on student language in an intelligent tutoring system (ITS). Unlike previous studies that analyzed language features in isolation, we utilized the epistemic network analysis (ENA) approach to investigate the structure of language connections in five dimensions, including non-narrativity, word abstractness, syntactic complexity, referential cohesion, and deep cohesion, in summaries written before and after the intervention. Visualizations of the ENA networks revealed differences in language connection structures between pretest and posttest in both formal and informal groups. Specifically, in the informal group, the connection between non-narrativity and word abstractness was stronger in the posttest network. In the formal group, the connection between non-narrativity and referential cohesion was stronger in the posttest network. Additionally, the connection between deep cohesion and syntactic complexity shifted from significant on pretest networks to insignificant on posttest networks. This study has implications for the design of conversational agent language and sheds light on the potential of combining ENA and Coh-Metrix components to analyze the differences in the structure of language connections, rather than individual language features in isolation.
A Lightweight Interactive Regular Expression Generator for Qualitative Coding in Quantitative Ethnography

Quantitative ethnography approaches are often used to analyze large scale qualitative data. Manually coding such data is expensive and time consuming, if not impractical or impossible. In contrast, machine learning algorithms can code virtually unlimited amounts of data once a model has been created. However, machine learning approaches lack transparency and rely on large amount of training data. An alternative automated coding approach using regular expressions has the advantage of minimizing required training data while providing transparency. However, manually creating regular expressions during the coding process can be a very challenging task for many researchers. One potential solution to this challenge is automatic regular expression generation. Unfortunately, existing algorithms are all based on large pre-coded training data which is often unavailable in quantitative ethnography tasks. In this paper, we present a lightweight and interactive algorithm that actively constructs regular expression-based coding classifiers with the researcher. We use a simulation on an education data to show that the proposed algorithm is promising.
Automated Code Discovery via Graph Neural Networks and Generative AI

Quantitative Ethnographic researchers sometimes use machine learning to help them discover codes in discourse. Commonly used techniques, such as topic modelling via Latent Dirichlet Allocation (LDA), are computationally limited and may produce results that are difficult to interpret. We present a novel approach for automated code discovery using graph neural networks (GNNs) and generative artificial intelligence (GAI) that identifies and interprets topics in discourse. Using data collected from a collaborative sensemaking task, we compare the results of our method to the outputs of a similar approach using LDA, as well as manually derived codes. We found that for these data (a) GAI can produce reasonable interpretations of topics; (b) the LDA-based approach discovered content-related codes; (c) the GNN-based approach discovered process-related codes; and (d) the outputs of both approaches had little overlap with manually derived codes.
Patient Medical Records: Implementation of a Security Solution Based on the Hyperledger Fabric Blockchain

In this paper, we have developed a solution for securing patient medical records based on blockchain. In our approach, we first carried out a comparative study of different blockchains. This comparative study, based on the Ethereum, Corda and Hyperledger Fabric blockchains, enabled us to select Hyperledger Fabric as the development framework for our blockchain. The criteria justifying our choice are essentially: the modularity of the architecture, the variety of programming languages for smarts contracts, the possibility of creating private channels between members of a network, high access control and data confidentiality, and a flexible consensus model. These criteria are crucial as they guarantee both the robustness and flexibility of the network in a shared medical record context. The proposed solution is a decentralized application that exchanges data in a consortium-type blockchain network, with three different organizations in a healthcare pathway: a hospital, a pharmacy and a laboratory. Other organizations can be added to the network taking into account the need to share and secure healthcare information. Our solution uses the IPFS (Interplanetary File System) protocol for distributed document storage, increasing data security and availability. To facilitate exchanges between network nodes, particular emphasis was also placed on the choice of consensus algorithm. First we chose the Solo Orderer algorithm, which uses a single Ordering Service node to process transactions and add them to blocks, and then we used the Kafka orderer algorithm, which offers high scalability and robust resilience in production environments. The choice of these two consensus algorithms enabled us to set up and deploy a blockchain network that stores and secures sensitive data from medical analyses or examinations in a patient's care pathway.
Mobile assisted language learning: investigating English speaking performance and satisfaction

With their growing popularity, mobile technologies can be seen as having the capacity to enhance language learning by offering learners a flexible and dynamic learning environment that is not limited by time or location. While the integration of technology in foreign language education has demonstrated overall success, including enhanced language learning outcomes through mobile technologies, it is important to acknowledge that there is also research highlighting potential drawbacks. Therefore, conducting further studies in this area remains crucial for a comprehensive understanding. This is more significant when speaking is considered as a skill of paramount importance compared to others and student satisfaction which can lead to overall success of the course. Therefore, the primary goal of this descriptive study is to investigate the potential advantages and consequences of integrating mobile technologies into blended learning language classes. Within this framework, the study focuses on examining the utilization of a recognized Web 2.0 tool, specifically Voki, to assess its effects on the English speaking proficiency and satisfaction perceptions of preparatory school students in blended learning settings. The results of the study reveal that the implementation of Voki leads to a significant enhancement in students' speaking skills, accompanied by a noticeable increase in their satisfaction levels. These findings suggest that intentional integration of mobile technologies into language education, particularly through the utilization of a Web 2.0 tool like Voki with its multimedia capabilities, holds the potential to serve as a valuable educational asset.
VECTORS OF THE DEVELOPMENT OF SCIENCE AND EDUCATION IN THE MODERN WORLD

Interdisciplinary research of modern trends in science and its practical application
GAIT: A Game-Theoretic Defense Against Intellectual Property Theft

Months may pass before the victim of IP theft even knows they have been compromised. During this time, the attacker can exfiltrate large amounts of data. Recent work has proposed the idea of injecting a set of believable fake versions of a real document into a network so that the attacker has to expend time and effort to identify the real document from a sea of similar documents. In this paper, we consider the problem of an attacker who is smart and breaks a technical document down into small, bit-sized “units” and inspects them one by one so as to defeat the fake document defense. If a unit in a document is determined to be fake, the adversary does not need to look further at the same document. He can also immediately identify as fake, any other document that contains the same unit. In this paper, we consider the problem of a smart attacker using this strategy. Our proposed defensive algorithm, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf GAIT}$</tex-math></inline-formula> , is shown to be successful in mitigating such attacks. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf GAIT}$</tex-math></inline-formula> can work in conjunction with any NLP-based generative method to create fake technical documents.
HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion

Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained information about its associated triple. Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers. Most existing approaches to HKGC exploit a global-level graph structure to encode hyper-relational knowledge into the graph convolution message passing process. However, the addition of multi-hop information might bring noise into the triple prediction process. To address this problem, we propose HyperFormer, a model that considers local-level sequential information, which encodes the content of the entities, relations and qualifiers of a triple. More precisely, HyperFormer is composed of three different modules: an entity neighbor aggregator module allowing to integrate the information of the neighbors of an entity to capture different perspectives of it; a relation qualifier aggregator module to integrate hyper-relational knowledge into the corresponding relation to refine the representation of relational content; a convolution-based bidirectional interaction module based on a convolutional operation, capturing pairwise bidirectional interactions of entity-relation, entity-qualifier, and relation-qualifier. Furthermore, we introduce a Mixture-of-Experts strategy into the feed-forward layers of HyperFormer to strengthen its representation capabilities while reducing the amount of model parameters and computation. Extensive experiments on three well-known datasets with four different conditions demonstrate HyperFormer's effectiveness. Datasets and code are available at https://github.com/zhiweihu1103/HKGC-HyperFormer.
TPUF: Enhancing Cross-domain Sequential Recommendation via Transferring Pre-trained User Features

Sequential recommendation has long been challenged by data sparsity issues. Most recently, cross-domain sequential recommendation (CDSR) techniques have been proposed to leverage sequential interaction data from other domains. However, accessing raw data from source domains is often restricted due to privacy concerns. To tackle this issue, we introduce TPUF, a novel CDSR model that transfers pre-trained latent user features from the source domain (UFS) instead of the original interaction data. By doing so, TPUF improves recommendation effectiveness while maintaining practicality. TPUF has three functional characteristics: (1) It is a feature mapping-and-aggregation framework that does not impose specific constraints on the nature of pre-trained UFS. (2) It incorporates a temporal feature mapping unit to effectively extract domain-shared information from UFS with temporal information recovered. (3) It additionally employs an adversarial feature alignment unit to align features across domains to combat feature transfer bias. Experimental results on real-world datasets demonstrate that TPUF outperforms other state-of-the-art cross-domain recommendation models and is compatible with multiple UFS types.
KuaiSAR: A Unified Search And Recommendation Dataset

The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in joint optimization using user behavior data from both S&R services. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 350 million daily active users. Previous research in this field has predominantly employed publicly available semi-synthetic datasets, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR contains genuine user behaviors, including the occurrence of each interaction within either search or recommendation service, and the users' transitions between the two services. This work aids in joint modeling of S&R, and utilizing search data for recommender systems (and recommendation data for search engines). Furthermore, due to the various feedback labels associated with user-video interactions, KuaiSAR also supports a broad range of tasks, including intent recommendation, multi-task learning, and modeling of long sequential multi-behavioral patterns. We believe this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the S&R services in practical, real-world applications. The dataset is available at https://ethan00si.github.io/KuaiSAR/. The dataset is also shared at https://zenodo.org/record/8181109.
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems

The field of generating recommendations within the framework of causal inference has seen a recent surge.This approach enhances insights into the influence of recommendations on user behavior and helps in identifying the underlying factors. Existing research has often leveraged propensity scores to mitigate bias, albeit at the risk of introducing additional variance. Others have explored the use of unbiased data from randomized controlled trials, although this comes with assumptions that may prove challenging in practice. In this paper, we first present the causality-aware interpretation of recommendations and reveal how the underlying exposure mechanism can bias the maximum likelihood estimation (MLE) of observational feedback. Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set. Building on this foundation, we present a novel contrastive counterfactual learning method (CCL) that incorporates three unique positive sampling strategies grounded in estimated exposure probability or random counterfactual samples. Through extensive experiments on two real-world datasets, we demonstrate that our CCL outperforms the state-of-the-art methods.
Towards Automatic ICD Coding via Knowledge Enhanced Multi-Task Learning

The aim of ICD coding is to assign International Classification of Diseases (ICD) codes to unstructured clinical notes or discharge summaries. Numerous methods have been proposed for automatic ICD coding in an effort to reduce human labor and errors. However, existing works disregard the data imbalance problem of clinical notes. In addition, the noisy clinical note issue has not been thoroughly investigated. To address such issues, we propose a knowledge enhanced Graph Attention Network (GAT) under multi-task learning setting. Specifically, multi-level information transitions and interactions have been implemented. On the one hand, a large heterogeneous text graph is constructed to capture both intra- and inter-note correlations between various semantic concepts, thereby alleviating the data imbalance issue. On the other hand, two auxiliary healthcare tasks have been proposed to facilitate the sharing of information across tasks. Moreover, to tackle the issue of noisy clinical notes, we propose to utilize the rich structured knowledge facts and information provided by medical domain knowledge, thereby encouraging the model to focus on the clinical notes' noteworthy portion and valuable information. The experimental results on the widely-used medical dataset, MIMIC-III, demonstrate the advantages of our proposed framework.
Latent Aspect Detection via Backtranslation Augmentation

Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers identify shortcomings and prioritize customers' needs. Existing methods focus on detecting the surface form of an aspect falling short when aspects are latent in reviews, especially in an informal context like in social posts. In this paper, we propose data augmentation via natural language backtranslation to extract latent occurrences of aspects. We presume that backtranslation (1) can reveal latent aspects because they may not be commonly known in the target language and can be generated through backtranslation; (2) augments context-aware synonymous aspects from a target language to the original language, hence addressing the out-of-vocabulary issue; and (3) helps with the semantic disambiguation of polysemous words and collocations. Through our experiments on well-known aspect detection methods across semeval datasets of restaurant and laptop reviews, we demonstrate that review augmentation via backtranslation yields a steady performance boost in baselines. We further contribute LADy at https://github.com/fani-lab/LADy, a benchmark library to support the reproducibility of our research.
Phenomenology of Emotions and Algorithms in Cases of Early Rehospitalizations

This paper is going to focus on the problem of emotions in technology, in particular in reference to the case of algorithms developed to track early rehospitalizations. In this paper I am going to discuss how phenomenology can support the integration of emotions in technology and how this integration can improve our chances for that “decent survival” that the founder of bioethics, Potter, has envisioned as the main goal of this discipline (Natl Cancer Inst Monogr 13:111–116, 1964; Bioethics, bridge to the future. Prentice Hall, Inc., Englewood Cliffs, p 24, 1971; Ann N Y Acad Sci 196(Art. 4):200–205, 1972; Cancer Res 35:2297–2306, 1975; Global bioethics. Michigan University, 1988). I believe that descriptive phenomenology can be a useful philosophical and psychological approach to improve the moral and emotional intelligence behind machine learning programming (Dreyfus, What computers can’t do: the limits of artificial intelligence. HarperCollins, 1978; The Socratic and platonic basis of cognitivism. AI & Society, Springer, Berlin, 1988) and overcome the risk of ethical opacity implied in programming (Casacuberta, Ethical and technical aspects of emotions to create empathy in medical machines. In Van Rysewik SP, Pontier M (eds) Machine in medical ethics. Springer, New York/Dordrecht, 2015). On the one hand, the ethical aspect of phenomenology would allow us to grasp the predicative and pre-predicative facie of the lived-experiences and their emotional contents. On the other hand, the phenomenological method of research applied in psychology would offer a way to transcribe and interpret data in a responsible and contextualized way.
MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation

Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks. However, the exact GED computation is known to be NP-complete. For instance, the widely used A* algorithms explore the entire search space to find the optimal solution which inevitably suffers scalability issues. Learning-based methods apply graph representation techniques to learn the GED by formulating a regression task, which can not recover the edit path and lead to inaccurate GED approximation (i.e., the predicted GED is smaller than the exact). To this end, in this work, we present a data-driven hybrid approach MATA* for approximate GED computation based on Graph Neural Networks (GNNs) and A* algorithms, which models from the perspective of learning to match nodes instead of directly regressing GED. Specifically, aware of the structure-dominant operations (i.e., node and edge insertion/deletion) property in GED computation, a structure-enhanced GNN is firstly designed to jointly learn local and high-order structural information for node embeddings for node matchings. Second, top-k candidate nodes are produced via a differentiable top-k operation to enable the training for node matchings, which is adhering to another property of GED, i.e., multiple optimal node matchings. Third, benefiting from the candidate nodes, MATA* only performs on the promising search directions, reaching the solution efficiently. Finally, extensive experiments show the superiority of MATA* as it significantly outperforms the combinatorial search-based, learning-based and hybrid methods and scales well to large-size graphs.
Asymptotic Equipartition Property for a Markov Source Having Ambiguous Alphabet

We propose a generalization of the asymptotic equipartition property to discrete sources with an ambiguous alphabet, and prove that it holds for irreducible stationary Markov sources with an arbitrary distinguishability relation. Our definition is based on the limiting behavior of graph parameters appearing in a recent dual characterization of the Shannon capacity of graphs, evaluated at subgraphs of strong powers of the confusability graph induced on high-probability subsets. As a special case, our results give an information-theoretic interpretation of the graph entropy rate of such sources.