Reviewerly/exHarmony huggingface dataset:
Evaluation of a Representative Selection of SPARQL Query Engines Using Wikidata

In this paper, we present an evaluation of the performance of five representative RDF triplestores, including GraphDB, Jena Fuseki, Neptune, RDFox, and Stardog, and one experimental SPARQL query engine, QLever. We compare importing time, loading time, and exporting time using a complete version of the knowledge graph Wikidata, and we also evaluate query performances using 328 queries defined by Wikidata users. To put this evaluation into context with respect to previous evaluations, we also analyze the query performances of these systems using a prominent synthetic benchmark: SP $$^2$$ Bench. We observed that most of the systems we considered for the evaluation were able to complete the execution of almost all the queries defined by Wikidata users before the timeout we established. We noticed, however, that the time needed by most systems to import and export Wikidata might be longer than required in some industrial and academic projects, where information is represented, enriched, and stored using different representation means.
Hypothesizing an algorithm from one example: the role of specificity

Statistical machine learning usually achieves high-accuracy models by employing tens of thousands of examples. By contrast, both children and adult humans typically learn new concepts from either one or a small number of instances. The high data efficiency of human learning is not easily explained in terms of standard formal frameworks for machine learning, including Gold's learning-in-the-limit framework and Valiant's probably approximately correct (PAC) model. This paper explores ways in which this apparent disparity between human and machine learning can be reconciled by considering algorithms involving a preference for specificity combined with program minimality. It is shown how this can be efficiently enacted using hierarchical search based on identification of certificates and push-down automata to support hypothesizing compactly expressed maximal efficiency algorithms. Early results of a new system called DeepLog indicate that such approaches can support efficient top-down construction of relatively complex logic programs from a single example. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
Argument and explanation

In this paper, we bring together two closely related, but distinct, notions: argument and explanation. We clarify their relationship. We then provide an integrative review of relevant research on these notions, drawn both from the cognitive science and the artificial intelligence (AI) literatures. We then use this material to identify key directions for future research, indicating areas where bringing together cognitive science and AI perspectives would be mutually beneficial. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
Self-supervised Depth Completion with Adaptive Online Adaptation

Although depth completion has achieved remarkable performance relying on deep learning in recent years, these models tend to suffer a performance degradation when exposed to new environments. Online adaptation, where the model is trained in a self-supervised manner during testing, seems a promising technique to alleviate the drop. However, continuous online adaptation may cause the model to over-adapt and miss the optimal parameters, resulting in oscillation or even degradation of the model performance, in addition to wasting computational resources. Therefore, this paper proposes an adaptive online adaptation framework to make model adaptively trigger online adaptation when encountering novel environments and stop adaptation when model has adapted to the current environment. In detail, we design a trigger to detect the familiarity of model to the current scenario based on image similarity and then launch online adaptation when the scenario is novel. Besides, we elaborate a stopper to monitor the error between prediction and depth input and convert online adaptation to inference when online adaptation does not bring improvement for model. Experimental results demonstrate that our method improves the accuracy of model prediction and increases average running speed of the model on each frame in online adaptation.
A Rule-Free Approach for Cardiological Registry Filling from Italian Clinical Notes with Question Answering Transformers

The huge volume of textual information generated in hospitals constitutes an essential but underused asset that could be exploited to improve patient care and management. The encoding of raw medical texts into fixed data structures is traditionally addressed with knowledge-based models and complex hand-crafted rules, but the rigidity of this approach poses limitations to the generalizability and transferability of the solutions, in particular for a non-English setting under data scarcity conditions. This paper shows that transformer-based language representation models have the right characteristics to be employed as a more flexible but equally high-performing clinical information retrieval system for this scenario, without relying upon a knowledge-driven component. We demonstrate it pragmatically on the extraction of clinical entities from Italian cardiology reports for patients with inherited arrhythmias, outperforming the previous ontology-based work with our proposed transformer pipeline under the same setting and exploring a new rule-free approach based on question answering to automate cardiological registry filling.
Computational Evaluation of Model-Agnostic Explainable AI Using Local Feature Importance in Healthcare

Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed decision support from AI and comply with evidence-based medical practice. In the XAI field, effective evaluation methods are still being developed. The straightforward way is to evaluate via user feedback. However, this needs big efforts (applying on high number of users and test cases) and can still include various biases inside. A computational evaluation of explanation methods is also not easy since there is not yet a standard output of XAI models and the unsupervised learning behavior of XAI models. In this paper, we propose a computational evaluation method for XAI models which generate local feature importance as explanations. We use the output of XAI model (local feature importances) as features and the output of the prediction problem (labels) again as labels. We evaluate the method based a real-world tabular electronic health records dataset. At the end, we answer the research question: "How can we computationally evaluate XAI Models for a specific prediction model and dataset?".
Use of Augmented Reality in the Education Setting

The implementation of augmented Reality (AR) in educational settings has become popular due to AR's beneficial effects on the learning process. Because of how readily and successfully conceptual context may be shown as digital components, this technique is one of the most popular learning fads of the present day. In conclusion, this study aims to investigate how augmented reality technology is employed in Malaysian educational institutions from the viewpoints of Malaysian educators. A significant conclusion from the study indicated that teachers were interested in augmented reality technology due to its unique qualities.
MFUnetr: A transformer-based multi-task learning network for multi-organ segmentation from partially labeled datasets

As multi-organ segmentation of CT images is crucial for clinical applications, most state-of-the-art models rely on a fully annotated dataset with strong supervision to achieve high accuracy for particular organs. However, these models have weak generalization when applied to various CT images due to the small scale and single source of the training data. To utilize existing partially labeled datasets to obtain segmentation containing more organs and with higher accuracy and robustness, we create a multi-task learning network called MFUnetr. By directly feeding a union of datasets, MFUnetr trains an encoder-decoder network on two tasks in parallel. The main task is to produce full organ segmentation using a specific training strategy. The auxiliary task is to segment labeled organs of each dataset using label priors. Additionally, we offer a new weighted combined loss function to optimize the model. Compared to the base model UNETR trained on the fully annotated dataset BTCV, our network model, utilizing a combination of three partially labeled datasets, achieved mean Dice on overlapping organs: spleen + 0.35 %, esophagus + 15.28 %, and aorta + 8.31 %. Importantly, without fine-tuning, the mean Dice calculated on 13 organs of BTCV remained + 1.91 % when all 15 organs were segmented. The experimental results show that our proposed method can effectively use existing large partially annotated datasets to alleviate the problem of data scarcity in multi-organ segmentation.
The Criminal Selfie: Conveying Grievance While Recording and Live Streaming Antisocial Behavior

Abstract Despite attempts to regulate content, social media platforms continue to host images of antisocial behavior and crime. These images include dashboard videos of road rage and CCTV footage of shoplifting, as well as more extreme recordings of torture, sexual assault, suicide, and mass shootings. These images are often produced by offenders of their own volition using smartphone cameras and wearable recording devices. We understand criminal selfies as media content of antisocial behavior or crime produced by or with the awareness of an offender. By producing a criminal selfie, an offender renders themselves vulnerable to public scrutiny, legal punishment, and other negative outcomes. Yet criminal selfies remain a popular form of toxic online communication. This manuscript theorizes that one of the previously underappreciated explanations for criminal selfies is a desire to broadcast personal grievances. In such cases, they allow an offender to publicize their motivating politics and to offer them to an online audience for consideration and discussion. Antisocial content often evokes an unfavorable ratio of denunciation versus supportive responses. We claim that some offenders wager that a criminal selfie nonetheless earns their grievances a degree of awareness and, potentially, consequence. Some criminal selfies reflect a willingness to self-incriminate by documenting antisocial behavior in hopes that their images will contribute to public discourse. This article contributes to studies of criminal visibility by addressing how it can be intended as political expression. We first outline the concept of the criminal selfie and how it reflects a changing relationship between visibility and criminality in contemporary digital society. We then review literature on the motives of criminals who film themselves. We seek to compliment this literature by considering socially and politically aggrieved individuals producing antisocial content. This includes livestreams of white nationalist mass shootings (Christchurch, Halle) as well as a series of Reddit groups that solicit and (counter-)denounce antisocial grievances by digital media users (r/iamverybadass, r/publicfreakout, r/iamatotalpieceofshit).
Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model

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GETT-QA: Graph Embedding Based T2T Transformer for Knowledge Graph Question Answering

In this work, we present an end-to-end Knowledge Graph Question Answering (KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text pre-trained language model. The model takes a question in natural language as input and produces a simpler form of the intended SPARQL query. In the simpler form, the model does not directly produce entity and relation IDs. Instead, it produces corresponding entity and relation labels. The labels are grounded to KG entity and relation IDs in a subsequent step. To further improve the results, we instruct the model to produce a truncated version of the KG embedding for each entity. The truncated KG embedding enables a finer search for disambiguation purposes. We find that T5 is able to learn the truncated KG embeddings without any change of loss function, improving KGQA performance. As a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata datasets on end-to-end KGQA over Wikidata.
FlexKA: A Flexible Karatsuba Multiplier Hardware Architecture for Variable-Sized Large Integers

The Karatsuba algorithm is an effective way to accelerate large integer multiplications through recursive function calls. However, existing hardware implementations of Karatsuba multipliers are limited to fixed operand sizes. To enable their application in diverse domains, including homomorphic encryption with varying multiplicative depths, it is necessary to support variable operand sizes. In this paper, we propose a novel Karatsuba multiplier design, named FlexKA, which supports variable operand sizes through a state machine that manages the dynamic call states of the operation. We evaluate FlexKA on the Xilinx ZynqMP FPGA and demonstrate that it supports variable operand sizes up to 256K bits, achieving a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.2\times$ </tex-math></inline-formula> speedup compared to a highly-optimized software library running on a CPU. Our results show that FlexKA is an efficient and effective solution for large integer multiplications with flexible operand sizes in hardware.
Hierarchical supervisions with two-stream network for Deepfake detection

Recently, the quality of face generation and manipulation has reached impressive levels, making it difficult even for humans to distinguish real and fake faces. At the same time, methods to distinguish fake faces from reals came out, such as Deepfake detection. However, the task of Deepfake detection remains challenging, especially the low-quality fake images circulating on the Internet and the diversity of face generation methods. In this work, we propose a new Deepfake detection network that could effectively distinguish both high-quality and low-quality faces generated by various generation methods. First, we design a two-stream framework that incorporates a regular spatial stream and a frequency stream to handle the low-quality problem since we find that the frequency domain artifacts of low-quality images will be preserved. Second, we introduce hierarchical supervisions in a coarse-to-fine manner, which consists of a coarse binary classification branch to classify reals and fakes and a five-category classification branch to classify reals and four different types of fakes. Extensive experiments have proved the effectiveness of our framework on several widely used datasets.
Opening research data contributes to the citations of related research articles: Evidence from <i>Data in Brief</i>

Abstract This paper explores the effect of publishing a data paper in the Open Access journal Data in Brief ( DIB ) on the citation counts of the related research paper. Using regression analysis, citation content analysis and a survey method, we investigate whether research papers with a related data paper have higher citation counts and the potential reasons. After controlling variables that correlate with the citation counts, research papers with a related data paper were found to have higher citation counts than those published in the same issue of the same journal. Next, we explored the causal relationship between the two variables by surveying the corresponding authors of 618 papers who shared datasets in DIB from 2014 to 2021. The results show that the authors acknowledge the benefits of sharing data in DIB , including citation increase and career reputation enhancement. We further explored how the data papers in DIB increase the citations of the related research papers by using citation content analysis. We found that scientists co‐cite the data papers and their related research papers for the purpose of reusing the underlying data or portraying a better understanding of the underlying data and related research articles.
Evaluation of Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

Every day the modern world is moving towards digitalization and cashless transactions are becoming more common, credit cards are rapidly becoming more popular. Online and offline purchases using credit cards have become increasingly popular, which results in more fraudulent transactions every day. A large number of credit card fraud incidents occur every year and lead to huge financial losses. accordingly, it could be important to choose the best fraud detection method is essential so that it can detect fraud before criminal consumers a stolen card. To detect fraud, one method is to evaluate historical transaction data, as well as both normal and fraudulent transactions, to obtain usual and fraudulent behavior features by using machine learning techniques. we can use machine learning algorithms to solve this problem if we have access to enough data. In this study, our goal is to compare three algorithms for detecting credit card fraud (Decision Tree, Regression Logistic and Random Forest). we want to use a model that is new and based on a hybrid approach for detecting credit card fraud. According to this study, the proposed model is more capable of identifying fraudulent transactions than previous studies.
Farsi CAPTCHA Recognition Using Attention-Based Convolutional Neural Network

Getting around CAPTCHAs is essential for stopping fraudulent online activity. The creation of efficient CAPTCHA-breaking algorithms in the context of Persian can help safeguard Farsi-speaking users from a variety of online dangers and enhance their overall online experience. This study offers a novel method for recognizing Persian CAPTCHAs, which was developed and tested on a large and distinctive dataset. Our approach to Farsi CAPTCHA recognition leverages deep learning models, specifically a combination of the TPS-Resnet-BiLSTM-ATTN model, which surpasses other approaches and breaks Farsi CAPTCHAs with the highest possible accuracy. We have achieved amazing results with promising implications for boosting the security and usability of many online services that depend on CAPTCHA authentication by delving deeply into the impact of attention modules on CAPTCHA recognition.
Using Supervised Machine Learning Approaches To Detect Fraud In The Banking Transaction Network

Nowadays, criminal frauds occur in an organized manner in the banking sector. This issue is challenging since the number of organized frauds associated with such areas is estimated to range from 2% to 5% of the global gross domestic product (GDP). The people committing organized fraud use Internet-based financial services and conventional financial services. Accordingly, they use more complex plans and maps to avoid being recognized through organized fraud fighting systems. Due to the complexity and variety of fraud methods, the transaction may not seem suspicious initially. Hence, it is crucial to consider the interactions between the cards. For this purpose, the use of network theory is recommended. The current paper aims to classify each transaction as illegal or legal correctly. Therefore, extensive data analysis is used to organized fraud in the bank transaction network. Besides, a comparison between supervised learning algorithms is presented on a dataset with 46,316 transactions related to customers' card activities to distinguish between illegal and legal transactions. According to the Accuracy, Precision, Recall, and F1-Score criteria values, random forest and XGBoost could be considered suitable predictive models for fraud detection.
A Sentiment Analysis Dataset Preparation Framework: A Case Study on User Reviews Dataset on Iranian Tourist Destinations

The tourism industry has undergone a significant shift towards data-driven strategies in recent years. As a means of improving the quality of their service and performance, service providers are analyzing feedback from their customers to increase the number of tourists they attract. Negative feedback also provides valuable insights into the factors that detract from a location's appeal. Datasets that gather information on people's experiences and opinions of tourist destinations can be analyzed to extract valuable information. However, there are currently few existing datasets that specifically capture user reviews about historical and tourist attractions in Iran. To fill this gap, users have shared their travel experiences on various websites, and sentiment analysis can be employed to extract insights from this data. Effective sentiment analysis requires a suitable approach for data extraction, pre-processing, and storage. This study provides a framework for the user review dataset preparation, including data collection, ETL, data storage, and evaluation phases. A rich dataset containing user reviews about 178 Iran's historical and tourist attractions was prepared through the proposed framework in which automated crawlers were developed to collect data from Tripadvisor platforms. Data labelling was achieved using the DistilBERT-base-uncased language model for sentiment analysis and human evaluators for final annotations. A total of approximately 25 thousand samples were included in the dataset, and positive user comments outnumbered negative user comments by a wide margin. This high percentage of positive comments suggests that the locations were of a satisfactory standard, making it likely that users would return in the future. The findings of this study can help providers to improve the overall quality of their services by analyzing user reviews. The proposed framework and achieved dataset can also guide future efforts to leverage data for improved performance and customer satisfaction in the tourism industry by identifying areas that need improvement.
Fuzzy-ID3 Algorithm for Breast Cancer Disease Classification

In terms of death rates, breast cancer comes in second, among women with cancer. Despite the fact that cancer cells grow in a multistep process involving a number of different types of cells, prevention of breast cancer stays a challenge inside the modern world. As a method of breast cancer detection, this paper proposes ENTROPYMOC strategy, a fuzzy decision tree with a new formula of Entropy. It aims to improve the classification accuracy, precision, recall and F1-Measure of the decision tree by overcoming the limitations of the ID3 algorithm, which is not able to classify continuous-valued data. In the field of machine learning, fuzzy decision trees are becoming increasingly popular. This algorithm reduces the complexity of the logarithmic entropy formula by simplifying the Shannon entropy principle. WBCD (Original), WDBC (Diagnostic) and Coimbra datasets are used to test the improved algorithm. Based on the experimental results, the improved fuzzy-ID3 algorithm outperforms the other four classification algorithms (SVM, Na誰ve Bayes, Random forest and FId3) in terms of accuracy. In Coimbra dataset, accuracy increased by 3.448%.