Reviewerly/exHarmony huggingface dataset:
EasyStudy: Framework for Easy Deployment of User Studies on Recommender Systems

Improvements in the recommender systems (RS) domain are not possible without a thorough way to evaluate and compare newly proposed approaches. User studies represent a viable alternative to online and offline evaluation schemes, but despite their numerous benefits, they are only rarely used. One of the main reasons behind this fact is that preparing a user study from scratch involves a lot of extra work on top of a simple algorithm proposal. To simplify this task, we propose EasyStudy, a modular framework built on the credo "Make simple things fast and hard things possible". It features ready-to-use datasets, preference elicitation methods, incrementally tuned baseline algorithms, study flow plugins, and evaluation metrics. As a result, a simple study comparing several RS can be deployed with just a few clicks, while more complex study designs can still benefit from a range of reusable components, such as preference elicitation. Overall, EasyStudy dramatically decreases the gap between the laboriousness of offline evaluation vs. user studies and, therefore, may contribute towards the more reliable and insightful user-centric evaluation of next-generation RS. The project repository is available from https://bit.ly/easy-study-repo.
Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation

Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The well-established, dominating identity (ID)-based approaches completely fail to work. Cold-start recommenders, on the other hand, leverage item contents (brand, title, descriptions, etc.) to map the new items to the existing ones. However, the existing SCS recommenders explore item contents in coarse-grained manners that introduce noise or information loss. Moreover, informative data sources other than item contents, such as users' purchase sequences and review texts, are largely ignored. In this work, we explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation. Our proposed framework, ColdGPT, models item-attribute correlations into an item-attribute graph by extracting fine-grained attributes from item contents. ColdGPT then transfers knowledge into the item-attribute graph from various available data sources, i.e., item contents, historical purchase sequences, and review texts of the existing items, via multi-task learning. To facilitate the positive transfer, ColdGPT designs specific submodules according to the natural forms of the data sources and proposes to coordinate the multiple pre-training tasks via unified alignment-and-uniformity losses. Our pre-trained item-attribute graph acts as an implicit, extendable item embedding matrix, which enables the SCS item embeddings to be easily acquired by inserting these items into the item-attribute graph and propagating their attributes' embeddings. We carefully process three public datasets, i.e., Yelp, Amazon-home, and Amazon-sports, to guarantee the SCS setting for evaluation. Extensive experiments show that ColdGPT consistently outperforms the existing SCS recommenders by large margins and even surpasses models that are pre-trained on 75 - 224 times more, cross-domain data on two out of four datasets. Our code and pre-processed datasets for SCS evaluations are publicly available to help future SCS studies.
Exploring False Hard Negative Sample in Cross-Domain Recommendation

Negative Sampling in recommendation aims to capture informative negative instances for the sparse user-item interactions to improve the performance. Conventional negative sampling methods tend to select informative hard negative samples (HNS) besides the default random samples. However, these hard negative sampling methods usually struggle with false hard negative samples (FHNS), which happens when a user-item interaction has not been observed yet and is picked as a negative sample, while the user will actually interact with this item once exposed to it. Such FHNS issues may seriously confuse the model training, while most conventional hard negative sampling methods do not systematically explore and distinguish FHNS from HNS. To address this issue, we propose a novel model-agnostic Real Hard Negative Sampling (RealHNS) framework specially for cross-domain recommendation (CDR), which aims to discover the false and refine the real from all HNS via both general and cross-domain real hard negative sample selectors. For the general part, we conduct the coarse- and fine-grained real HNS selectors sequentially, armed with a dynamic item-based FHNS filter to find high-quality HNS. For the cross-domain part, we further design a new cross-domain HNS for alleviating negative transfer in CDR and discover its corresponding FHNS via a dynamic user-based FHNS filter to keep its power. We conduct experiments on four datasets based on three representative hard negative sampling methods, along with extensive model analyses, ablation studies, and universality analyses. The consistent improvements indicate the effectiveness, robustness, and universality of RealHNS, which is also easy-to-deploy in real-world systems as a plug-and-play strategy. The source code is avaliable in https://github.com/hulkima/RealHNS.
Multi-Relational Contrastive Learning for Recommendation

Personalized recommender systems play a crucial role in capturing users' evolving preferences over time to provide accurate and effective recommendations on various online platforms. However, many recommendation models rely on a single type of behavior learning, which limits their ability to represent the complex relationships between users and items in real-life scenarios. In such situations, users interact with items in multiple ways, including clicking, tagging as favorite, reviewing, and purchasing. To address this issue, we propose the Relation-aware Contrastive Learning (RCL) framework, which effectively models dynamic interaction heterogeneity. The RCL model incorporates a multi-relational graph encoder that captures short-term preference heterogeneity while preserving the dedicated relation semantics for different types of user-item interactions. Moreover, we design a dynamic cross-relational memory network that enables the RCL model to capture users' long-term multi-behavior preferences and the underlying evolving cross-type behavior dependencies over time. To obtain robust and informative user representations with both commonality and diversity across multi-behavior interactions, we introduce a multi-relational contrastive learning paradigm with heterogeneous short- and long-term interest modeling. Our extensive experimental studies on several real-world datasets demonstrate the superiority of the RCL recommender system over various state-of-the-art baselines in terms of recommendation accuracy and effectiveness. We provide the implementation codes for the RCL model at https://github.com/HKUDS/RCL.
Analysis of Failure Mechanisms of Gerber Half-Joint Specimens Through Digital Image Correlation Technique

Reinforced concrete Gerber half-joints are characterized by D-Regions in which the de Saint Venant theory is not valid. Therefore, in the capacity assessment of existing Gerber half-joints, the correct assumptions about the stress fields and the strut-and-tie models play a crucial role. Digital Image Correlation (DIC) is an optical technique for the determination of displacements and strains of specimens subjected to mechanical actions. In this work, the DIC technique is applied within an experimental campaign aimed at identifying the nonlinear behavior of Gerber half-joints according to different construction details. The results obtained using the DIC technique allow to identify the shape of the compression fields (struts) and to predict the development of the crack patterns. Therefore, the outcomes are compared with the expected shapes of the strut-and-tie models and interpreted to analyze the different failure mechanisms oh the half-joints.
Artificial intelligence technologies and applications for language learning and teaching

Abstract Artificial intelligence (AI) is changing many aspects of education and is gradually being introduced to language education. This article reviews the literature to examine main trends and common findings in relation to AI technologies and applications for second and foreign language learning and teaching. With special reference to computer-assisted language learning (CALL), the article explores natural language processing (NLP), data-driven learning (DDL), automated writing evaluation (AWE), computerized dynamic assessment (CDA), intelligent tutoring systems (ITSs), automatic speech recognition (ASR), and chatbots. It contributes to discussions on understanding and using AI-supported language learning and teaching. It suggests that AI will be continuously integrated into language education and AI technologies and applications will have a profound impact on language learning and teaching. Language educators need to ensure that AI is effectively used to support language learning and teaching in AI-powered contexts. More rigorous research on AI-supported language learning and teaching is recommended to maximise second and foreign language learning and teaching with AI.
Complementary Proof Nets for Classical Logic

Abstract A complementary system for a given logic is a proof system whose theorems are exactly the formulas that are not valid according to the logic in question. This article is a contribution to the complementary proof theory of classical propositional logic. In particular, we present a complementary proof-net system, $$\textsf{CPN}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CPN</mml:mi> </mml:math> , that is sound and complete with respect to the set of all classically invalid (one-side) sequents. We also show that cut elimination in $$\textsf{CPN}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CPN</mml:mi> </mml:math> enjoys strong normalization along with strong confluence (and, hence, uniqueness of normal forms).
Green Electronic Auditing and Accounting Information Reliability in the Jordanian Social Security Corporation: The Mediating Role of Cloud Computing

The purpose of this research is to examine the impact of green electronic auditing on accounting information reliability and the mediating role of cloud computing in the Jordanian Social Security Corporation. A survey of 500 employees in the Jordanian Social Security Corporation was used to gather data, with a response rate of 31.4% (157 employees). The researcher used structural equation modeling to investigate the connections between cloud computing, auditing on data processing processes, auditing the inputs, auditing the outputs, prior auditing on inputs, and accounting information reliability. The findings revealed that auditing data processing activities, auditing outputs, cloud computing, and earlier auditing on inputs all have a substantial impact on accounting information reliability. However, auditing the inputs and the link between cloud computing and accounting information reliability were not significant. This study’s conclusions have ramifications for policymakers and auditing and accounting practitioners. The Jordanian Social Security Corporation must consider the significance of adequate auditing methods to assure correct accounting information, particularly in the context of cloud computing. This report also highlights the need for more research on the influence of cloud computing on accounting and auditing processes in underdeveloped countries.
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques

The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource optimization. Machine Learning (ML), as the most advanced branch of AI in the past few years, has shown encouraging results and applications in the edge environment. Nevertheless, edge-powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements. Despite the fact that a great deal of the attention garnered by Edge ML is gained in both the academic and industrial communities, we noticed the lack of a complete survey on existing Edge ML technologies to provide a common understanding of this concept. To tackle this, this paper aims at providing a comprehensive taxonomy and a systematic review of Edge ML techniques, focusing on the soft computing aspects of existing paradigms and techniques. We start by identifying the Edge ML requirements driven by the joint constraints. We then extensively survey more than twenty paradigms and techniques along with their representative work, covering two main parts: edge inference, and edge learning. In particular, we analyze how each technique fits into Edge ML by meeting a subset of the identified requirements. We also summarize Edge ML frameworks and open issues to shed light on future directions for Edge ML.
An soft-sensor method for the biochemical reaction process based on LSTM and transfer learning

Due to significant differences in data distribution under different working conditions during Pichia pastoris biochemical reaction process, traditional soft-sensor model suffer from the model failure and deterioration, this paper propose a soft-sensor modeling method combing long short-term memory network (LSTM) and balanced distribution adaptation method (BDA). Firstly, the source domain data is used to establish an accurate source domain LSTM prediction model, and the structure and parameters of the first layer of LSTM are fixed to migrate to the target domain prediction model. Then use the balanced distribution adaptation method to shrink the distribution differences between different domains of data. Finally, data that has been modeled with balanced and adaptive distribution assist the real-time data to train the remaining layer of the network, and the accurate target domain prediction model is finally obtained. The simulation results show that the mentioned method has the preponderance of timely prediction and high prediction accuracy, which validates the effectiveness and practicality of the method. This method solves the problem of soft-sensor modeling under unknown modes of multiple operating conditions in Pichia pastoris biochemical reaction process, achieving the prediction of key parameters under different operating conditions, which can be widely applied, and also providing a new method for soft-sensor modeling of other non system systems.
A differential evolution-based algorithm with maturity extension for feature selection in intrusion detection system

Feature Selection (FS) is critically important for optimising the performance of Intrusion Detection Systems (IDSs) used in Wireless Sensor Networks (WSNs). However, selecting an optimal number of relevant features from massive IDS data sets has been an ongoing FS research problem. Many approaches use Machine Learning (ML), such as nature-inspired population-based and Differential Evolution (DE)-based algorithms for FS. However, the main drawback of DE is that it has a premature convergence issue, which leads to false convergence. In this paper, a novel mutation strategy is proposed to alleviate premature convergence and to develop a modified DE-based FS algorithm called DE with maturity extension (DE-ME). The novel mutation is achieved by changing the mutation factor in the DE\rnd\1 based on K-Nearest Neighbour (KNN) as the most efficient implementation without adding further complexity to the design process. The proposed DE-ME algorithm improves the FS operation with significant performance improvements in terms of overcoming premature convergence and increasing accuracy for selection. The DE-ME algorithm was used to select the most effective features from 41 features in the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset. The accuracy of DE-ME is high at 99.66 %, its False Positive Rate (FPR) is low at 0.464 %, and the true positive rate (TPR) achieved is 99.80 %. In addition, the number of features selected by DE-ME is 6 out of 41 features in the dataset.
Machine learning emulator for physics-based prediction of ionospheric potential response to solar wind variations

Abstract Physics-based simulations are important for elucidating the fundamental mechanisms behind the time-varying complex ionospheric conditions, such as ionospheric potential, against unprecedented solar wind variations incident on the Earth’s magnetosphere. However, carrying out an extensive parameter survey for comprehending the nonlinear solar wind density dependence of the ionospheric potential, for example, requires state-of-the-art global magnetohydrodynamic (MHD) simulations, which cannot be executed efficiently even on large-scale cluster computers. Here, we report the performance of a machine-learning based surrogate model for estimating the ionospheric potential outputs of a global MHD simulation, using the reservoir computing technique called echo state network (ESN). The trained ESN-based emulator demonstrates exceptional speed in conducting the parameter survey, which can lead to the identification of a solar wind density dependence of the ionospheric polar cap potential. Finally, we discuss future directions including the promising application for space weather forecasting. Graphical Abstract
Evaluation of heavy metal contamination using cokriging geostatistical method (case study of Abteymour oilfield in southern Iran)

Abstract One of the most significant sources of soil contamination is the industries involved in the discovery, extraction, and use of petroleum and gas resources. One of the largest oil fields in Iran is the Abteymour oilfield, which is situated in the agricultural areas and Karun river flood plain. This study was carried out to analyze and monitor soil contamination by heavy metals and to create a map of the spread of contamination in the vicinity of the Abteymour oilfield, taking into account the significance of soil pollution in such a region. Thirty-three samples from the local surface soils were used in this study, and in addition to testing the ICP-MASS device's ability to detect the presence of heavy metals and other key elements, some of the soil's physical and chemical characteristics were also determined. After drawing the variograms and determining the appropriate fitting model, the cokriging method was used to study the spatial distribution of Al, Ti, Sr, Cr, Ni, V, Cu, Li, and Pb heavy metals and Na and S elements. The results of the heavy metals distribution in the studied area showed that the distribution of the studied elements is affected by geological conditions and human activities, including the petroleum industry and agriculture activities.
An integrated approach of <scp>IoT</scp> and <scp>WSN</scp> using wavelet transform and machine learning for the solid waste image classification in smart cities

Abstract Solid waste management is one of the utmost vital issues allied with the smart city, which harms our society's health and the environment. Solid waste management has been a significant hurdle in developing smart cities, impacting the community's living standards. Sustainability is the circumstances that help to achieve natural and social development simultaneously in a prolific accord. We can allege that sustainable growth brings out stability in the necessities of the environment. It helps to preserve resources available for the coming generations. Waste management is also a concern for many authorities; researchers have started focusing on this area. This research article presents a unique method for waste management by employing internet of things for waste monitoring, a wireless sensor network for data communication and discrete wavelet transforms, image processing, and machine learning for image categorization. Implementing machine learning for image categorization has gained attention in the research area of artificial intelligence. The paper presents a way to classify waste images into different Classes such as domestic waste, e‐waste, and medical waste. The input image is down‐sampled, and channels are split to extract the color moments for minimal processing time. We have created a convolution neural network (CNN) trained using pre‐processed data for feature extraction and classification. We have used K‐nearest neighbor classifier (KNN) as another classifier to compare the results obtained by the proposed methodology. The findings demonstrate that, compared to the KNN, the suggested model shortens the processing time and achieves a high accuracy rate. The paper presents a novel approach for Solid waste classification by using pre‐processed image data for image processing and CNN for waste image classification, for the sake of proving the efficiency of the proposed method we have compared the results with the KNN classifier and found that the accuracy of classification is better in proposed method than the KNN classifier.
Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.