Reviewerly/exHarmony huggingface dataset:
Is More Always Better? Testing the Addition Bias for German Language Statistics

Abstract This replication study aims to investigate a potential bias toward addition in the German language, building upon previous findings of Winter and colleagues who identified a similar bias in English. Our results confirm a bias in word frequencies and binomial expressions, aligning with these previous findings. However, the analysis of distributional semantics based on word vectors did not yield consistent results for German. Furthermore, our study emphasizes the crucial role of selecting appropriate translational equivalents, highlighting the significance of considering language‐specific factors when testing for such biases for languages other than English.
Detecting biased validation of predictive models in the positive-unlabeled setting: disease gene prioritization case study

Positive-unlabeled data consists of points with either positive or unknown labels. It is widespread in medical, genetic, and biological settings, creating a high demand for predictive positive-unlabeled models. The performance of such models is usually estimated using validation sets, assumed to be selected completely at random (SCAR) from known positive examples. For certain metrics, this assumption enables unbiased performance estimation when treating positive-unlabeled data as positive/negative. However, the SCAR assumption is often adopted without proper justifications, simply for the sake of convenience.We provide an algorithm that under the weak assumptions of a lower bound on the number of positive examples can test for the violation of the SCAR assumption. Applying it to the problem of gene prioritization for complex genetic traits, we illustrate that the SCAR assumption is often violated there, causing the inflation of performance estimates, which we refer to as validation bias. We estimate the potential impact of validation bias on performance estimation. Our analysis reveals that validation bias is widespread in gene prioritization data and can significantly overestimate the performance of models. This finding elucidates the discrepancy between the reported good performance of models and their limited practical applications.Python code with examples of application of the validation bias detection algorithm is available at github.com/ArtomovLab/ValidationBias.
Current research trends on cognition, integrative complexity, and decision-making: a systematic literature review using activity theory and neuroscience

This article presents a systematic literature review that follows the PRISMA and PICOS guidelines to analyze current research trends on cognition, integrative complexity (IC) (a cognitive feature focusing on information processing in a person's response rather than its quantity or quality), and decision-making from the perspectives of activity theory and neuroscience.The study examines 31 papers published between 2012 and 2022 and 19 articles specifically related to neuroscience. We performed a content analysis using six categories within activity theory: subjects, objects, rules, community, division of labor, and outcomes.The study investigates the relationship between decision-making outcomes and IC as a cognitive feature in various contexts. Additionally, content analysis on neuroscience and IC revealed significant research gaps, including understanding the nature of IC, challenges related to its measurement, and differentiation from other cognitive features. We also identify opportunities for investigating the brain's activity during decision-making in relation to IC.We address the need for a more precise categorization of IC in studies of cognition, IC, and decision-making. We discuss the implications of our analysis for understanding the cognitive nature of IC and the potential of neuroscience methods for studying this attribute.
An Updatable and Revocable Decentralized Identity Management Scheme Based on Blockchain

We study the decentralized identity management mechanism based on blockchain. Finally, we propose an updatable and revocable decentralized identity management scheme DIURS. In the scheme, we construct the DID management tree, which is a dynamic chameleon authentication tree essentially by using the chameleon hash function. We design algorithms in detail from four stages: system initialization, identity creation, identity update and revocation, and identity verification. We make the DID documents on the blockchain editable successfully and realize the update and revocation of DIDs. Then, we observe that DIURS can meet the structural stability and irreversibility requirements. The time of identity search and update is milliseconds. The length of the identity authentication path is short. There is no need to save the historical version of DID documents. These results indicate that DIURS is not only safe and reliable but also performs well and achieves functional optimization.
Testing and Improvement of OCR Recognition Technology in Export-Oriented Chinese Dictionary APP

With the increasing number of Chinese learners each year, the international influence of the Chinese language has also grown. Consequently, Chinese auxiliary learning tools should align more closely with the needs of present-day learners. This paper focuses on a typical Chinese dictionary app and conducts tests and comparisons of the optical character recognition (OCR) function. Specifically, it compares the OCR function of a widely used Chinese learning dictionary with the more advanced and sophisticated mobile built-in OCR recognition technology available today. By identifying the technical gap, the paper proposes a design that incorporates deep learning techniques to enhance the built-in OCR function of the dictionary. This improvement aims to significantly enhance recognition accuracy in natural scenarios and ultimately enhance the user experience of the Chinese learning dictionary app.
GPDCCL: Cross-Domain Named Entity Recognition with Span-Based Domain Confusion Contrastive Learning

The goal of cross-domain named entity recognition is to transfer models learned from labelled source domain data to unlabelled or lightly labelled target domain datasets. This paper discusses how to adapt a cross-domain sentiment analysis model to the field of named entity recognition, as the sentiment analysis model is more relevant to the tasks and data characteristics of named entity recognition. Most previous classification methods were based on a tokenwise approach, and this paper introduces entity boundary information to prevent the model from being affected by a large number of nonentity labels. Specifically, adversarial training is used to enable the model to learn domain-confusing knowledge, and contrastive learning is used to reduce domain shift problems. The entity boundary information is transformed into a global boundary matrix representing sentence-level target labels, enabling the model to learn explicit span boundary information. Experimental results demonstrate that this method achieves good performance compared to multiple cross-domain named entity recognition models on the SciTech dataset. Ablation experiments reveal that the method of introducing entity boundary information significantly improves KL divergence and contrastive learning.
Stability of Explainable Recommendation

Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reasoning why a suggestion is provided and how well an item aligns with a user’s personalized preferences. Hence, explanations can play a huge role in influencing users to purchase products. However, the reliability of the explanations under varying scenarios has not been strictly verified from an empirical perspective. Unreliable explanations can bear strong consequences such as attackers leveraging explanations for manipulating and tempting users to purchase target items that the attackers would want to promote. In this paper, we study the vulnerability of existent feature-oriented explainable recommenders, particularly analyzing their performance under different levels of external noises added into model parameters. We conducted experiments by analyzing three important state-of-the-art (SOTA) explainable recommenders when trained on two widely used e-commerce based recommendation datasets of different scales. We observe that all the explainable models are vulnerable to increased noise levels. Experimental results verify our hypothesis that the ability to explain recommendations does decrease along with increasing noise levels and particularly adversarial noise does contribute to a much stronger decrease. Our study presents an empirical verification on the topic of robust explanations in recommender systems which can be extended to different types of explainable recommenders in RS.
Lightweight and Efficient Attention-Based Superresolution Generative Adversarial Networks

To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial networks, this paper optimizes the generator and discriminator based on the SRGAN model. First, the residual dense block is used as the basic structural unit of the generator to improve the network's feature extraction capability. Second, enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information, thereby allowing the generator to produce more realistic image reconstruction results. Then, we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of deriving finer-grained multiscale spatial information and creating long-term dependencies between multiscale channel attentions, thus enhancing the discriminative ability of the network. Finally, a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image's texture structure and enhance the model's stability. The findings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio (PSNR) by 1.59 dB and the structural similarity index (SSIM) by 0.045 when compared to SRGAN on the three test sets. Compared with the state-of-the-art methods, the reconstructed images have a clearer texture structure, richer high-frequency details, and better visual effects.
Multitask Graph Neural Network for Knowledge Graph Link Prediction

Predicting entities in knowledge graphs is a crucial research area, and convolutional neural networks (CNNs) have exhibited significant performance due to their ability to generate expressive feature embeddings. However, several existing methods in this field tend to disrupt entities and relational embeddings, disregarding the original translation characteristics in triples, leading to incomplete feature extraction.To address this issue and preserve the translation characteristics of triples, the present study introduces a novel representation technique, termed MultiGNN. The suggested approach uses a graph convolutional neural network for encoding and implements a parameter sharing technique. It employs a convolutional neural network and a translation model as decoders. The model's parameter space is expanded to effectively integrate translation characteristics into the convolutional neural network, which allows it to capture these characteristics and enhance the model's performance. The proposed method in this paper has demonstrated significant enhancements in several metrics on the public benchmark dataset when compared to the baseline method.
Research on Dance Evaluation Technology Based on Human Posture Recognition

In view of the increase in the number of people participating in dance rating assessments, this paper proposes a dance assessment technology based on human body posture recognition. This technique adopts the human target detection of the dance video, extracts bone key points, and then uses the video data set collected by professional dancers to conduct PoseC3D model training, enabling the model to classify the basic movements of the dance; then, the dynamic time normalization algorithm is used to evaluate the classified movements. The experimental results show that this technology can accurately identify the basic movements of various dances and accurately give the evaluation score of the corresponding movements, thus reducing the work intensity of the assessment staff.
Security Compressed Sensing Image Encryption Algorithm Based on Elliptic Curve

Aiming to address the problems of high costs associated with the storage and transmission of environmental monitoring images, as well as potential security risks, this paper proposes a security compressed sensing image encryption algorithm based on elliptic curve cryptography. The algorithm introduces elliptic curve encryption technology within a compressed sensing framework, using elliptic curve cryptography to encrypt the matrix during the compression perception acquisition process. This enables secure acquisition of environmental monitoring encrypted images. Therefore, this paper presents a security compressed sensing framework to address security gaps in the compression perception reconstruction process and improve the security of environmental monitoring images. Experimental results show that the proposed algorithm effectively encrypts and reconstructs environmental monitoring images, with high security and resistance to cracking.
Dimension Reduction Based on Sampling

Dimension reduction provides a powerful means of reducing the number of random variables under consideration. However, there were many similar tuples in large datasets, and before reducing the dimension of the dataset, we removed some similar tuples to retain the main information of the dataset while accelerating the dimension reduction. Accordingly, we propose a dimension reduction technique based on biased sampling, a new procedure that incorporates features of both dimensional reduction and biased sampling to obtain a computationally efficient means of reducing the number of random variables under consideration. In this paper, we choose Principal Components Analysis(PCA) as the main dimensional reduction algorithm to study, and we show how this approach works.
A Method for Extracting Electronic Medical Record Entities by Fusing Multichannel Self-Attention Mechanism with Location Relationship Features

With the implementation of the "Internet+" strategy, electronic medical records are generally applied in the medical field. Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients' states, but the existing methods for extracting entities from electronic medical records have problems of redundant information, overlapping entities, and low accuracy rates. Therefore, this paper proposes an entity extraction method for electronic medical records based on the network framework of BERT-BiLSTM, which incorporates a multichannel self-attention mechanism and location relationship features. First, the text input sequence was encoded using the BERT-BiLSTM network framework, and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mechanism. Then, the position relation characteristic was used to extract the local semantic message of the text, and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained. Next, the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix. Finally, the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus, and the experimental results indicate that the proposed method surpasses existing methods in terms of precision, recall, F1 value and training time.
Personalized Category Frequency prediction for Buy It Again recommendations

Buy It Again (BIA) recommendations are crucial to retailers to help improve user experience and site engagement by suggesting items that customers are likely to buy again based on their own repeat purchasing patterns. Most existing BIA studies analyze guests’ personalized behaviour at item granularity. This finer level of granularity might be appropriate for small businesses or small datasets for search purposes. However, this approach can be infeasible for big retailers which have hundreds of millions of guests and tens of millions of items. For such data sets, it is more practical to have a coarse-grained model that captures customer behaviour at the item category level. In addition, customers commonly explore variants of items within the same categories, e.g., trying different brands or flavors of yogurt. A category-based model may be more appropriate in such scenarios. We propose a recommendation system called a hierarchical PCIC model that consists of a personalized category model (PC model) and a personalized item model within categories (IC model). PC model generates a personalized list of categories that customers are likely to purchase again. IC model ranks items within categories that guests are likely to reconsume within a category. The hierarchical PCIC model captures the general consumption rate of products using survival models. Trends in consumption are captured using time series models. Features derived from these models are used in training a category-grained neural network. We compare PCIC to twelve existing baselines on four standard open datasets. PCIC improves NDCG up to 16% while improving recall by around 2%. We were able to scale and train (over 8 hours) PCIC on a large dataset of 100M guests and 3M items where repeat categories of a guest outnumber repeat items. PCIC was deployed and A/B tested on the site of a major retailer, leading to significant gains in guest engagement.
Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives

Providing effective suggestions is of predominant importance for successful Recommender Systems (RSs). Nonetheless, the need of accounting for additional multiple objectives has become prominent, from both the final users’ and the item providers’ points of view. This need has led to a new class of RSs, called Multi-Objective Recommender Systems (MORSs). These systems are designed to provide suggestions by considering multiple (conflicting) objectives simultaneously, such as diverse, novel, and fairness-aware recommendations. In this work, we reproduce a state-of-the-art study on MORSs that exploits a reinforcement learning agent to satisfy three objectives, i.e., accuracy, diversity, and novelty of recommendations. The selected study is one of the few MORSs where the source code and datasets are released to ensure the reproducibility of the proposed approach. Interestingly, we find that some challenges arise when replicating the results of the original work, due to the nature of multiple-objective problems. We also extend the evaluation of the approach to analyze the impact of improving user-centered objectives of recommendations (i.e., diversity and novelty) in terms of algorithmic bias. To this end, we take into consideration both popularity and category of the items. We discover some interesting trends in the recommendation performance according to different evaluation metrics. In addition, we see that the multi-objective reinforcement learning approach is responsible for increasing the bias disparity in the output of the recommendation algorithm for those items belonging to positively/negatively biased categories. We publicly release datasets and codes in the following GitHub repository: https://github.com/sisinflab/MORS_reproducibility.
Type-Augmented Link Prediction Based on Bayesian Formula

Knowledge graphs (KGs) play a pivotal role in various real-world applications, but they are frequently plagued by incomplete information, which manifests in the form of missing entities. Link prediction, which aims to infer missing entities given existing facts, has been mostly addressed by maximizing the likelihood of observed triplets at the instance level. However, they ignore the semantic information most KGs contain and the prior knowledge implied by the semantic information. To address this limitation, we propose a Type-Augmented Link Prediction (TALP) approach, which builds a hierarchical feature model, computes type feature weights, trains them to be specific to different relations, encodes weights into prior probabilities and convolutional encodes instance-level information into likelihood probabilities; finally, combining them via Bayes rule to compute the posterior probabilities of entity prediction. Our proposed TALP approach achieves significantly better performance than existing methods on link prediction benchmark datasets.
Research on Path Planning of Mobile Robots Based on Dyna-RQ

The mobile robot path planning problem is one of the main contents of reinforcement learning research. In traditional reinforcement learning, the agent obtains the cumulative reward value in the process of interacting with the environment and finally converges to the optimal strategy. The Dyna learning framework in reinforcement learning obtains an estimation model in the real environment. The virtual samples generated by the estimation model are updated together with the empirical samples obtained in the real environment to update the value function or strategy function to improve the convergence efficiency. At present, when reinforcement learning is used for path planning tasks, continuous motion cannot be solved in a large-scale continuous environment, and the convergence is poor. In this paper, we use RBFNN to approximate the Q-value table in the Dyna-Q algorithm to solve the drawbacks in traditional algorithms. The experimental results show that the convergence speed of the improved Dyna-RQ algorithm is significantly faster, which improves the efficiency of mobile robot path planning.
Complex Time Series Analysis Based on Conditional Random Fields

A fundamental problem with complex time series analysis involves data prediction and repair. However, existing methods are not accurate enough for complex and multidimensional time series data. In this paper, we propose a novel approach, a complex time series prediction model, which is based on the conditional random field (CRF) and recurrent neural network (RNN). This model can be used as an upper-level predictor in the stacking process or be trained using deep learning methods. Our approach is more accurate than existing methods in some suitable scenarios, as shown in the experimental results.
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders

An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user’s gender or age should not influence the model. Personalized recommender systems are particularly prone to violating this definition through their explicit user focus and user modelling. Explicit user modelling is also an aspect that makes many recommender systems incapable of providing hitherto unseen users with recommendations. We propose novel approaches for mitigating discrimination in Variational Autoencoder-based recommender systems by limiting the encoding of demographic information. The approaches are capable of, and evaluated on, providing users that are not represented in the training data with fair recommendations.