Reviewerly/exHarmony huggingface dataset:
AutoDenoise: Automatic Data Instance Denoising for Recommendations

Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences. However, the arbitrary user behaviors in most recommendation scenarios lead to a large volume of noisy data instances being recorded, which cannot fully represent their true interests. While a large number of denoising studies are emerging in the recommender system community, all of them suffer from highly dynamic data distributions. In this paper, we propose a Deep Reinforcement Learning (DRL) based framework, AutoDenoise, with an Instance Denoising Policy Network, for denoising data instances with an instance selection manner in deep recommender systems. To be specific, AutoDenoise serves as an agent in DRL to adaptively select noise-free and predictive data instances, which can then be utilized directly in training representative recommendation models. In addition, we design an alternate two-phase optimization strategy to train and validate the AutoDenoise properly. In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability. We conduct extensive experiments to validate the effectiveness of AutoDenoise combined with multiple representative recommender system models.
Anomaly detection using spatial and temporal information in multivariate time series

Real-world industrial systems contain a large number of interconnected sensors that generate a significant amount of time series data during system operation. Performing anomaly detection on these multivariate time series data can timely find faults, prevent malicious attacks, and ensure these systems safe and reliable operation. However, the rarity of abnormal instances leads to a lack of labeled data, so the supervised machine learning methods are not applicable. Furthermore, most current techniques do not take full advantage of the spatial and temporal dependencies implied among multiple variables to detect anomalies. Hence, we propose STADN, a novel Anomaly Detection Network Using Spatial and Temporal Information. STADN models the relationship graph between variables for a graph attention network to capture the spatial dependency between variables and utilizes a long short-term memory network to mine the temporal dependency of time series to fully use the spatial and temporal information of multivariate time series. STADN predicts the future behavior of each sensor by combining the historical behavior of the sensor and its neighbors, then detects and locates anomalies according to the prediction error. Furthermore, we improve the proposed model's ability to discriminate anomaly and regularity and expand the prediction error gap between normal and abnormal instances by reconstructing the prediction errors. We conduct experiments on two real-world datasets, and the experimental results suggested that STADN achieves state-of-the-art outperformance.
Trends and opportunities in computable clinical phenotyping: A scoping review

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.
Exact Probability Distribution for the ROC Area under Curve

The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus the exactness in its probability distribution is crucial toward accurate statistical inference. We show the exact probability distribution of the ROC AUC-value, hence exact critical values and p-values are readily obtained. Because the exact calculations are computationally intense, we demonstrate a method of geometric interpolation, which is exact in a special case but generally an approximation, vastly increasing computational speeds. The method is illustrated through open access data, demonstrating superiority of 26 composite biomarkers relative to a predicate device. Especially under correction for testing of multiple hypotheses, traditional asymptotic approximations are encumbered by considerable imprecision, adversely affecting IVD device development. The ability to obtain exact p-values will allow more efficient IVD device development.
Learned Text Representation for Amharic Information Retrieval and Natural Language Processing

Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applications rely on pre-trained text representations, leading to the development of a number of neural network language models for various languages. However, this is not the case for Amharic, which is known to be a morphologically complex and under-resourced language. Usable pre-trained models for automatic Amharic text processing are not available. This paper presents an investigation on the essence of learned text representation for information retrieval and NLP tasks using word embeddings and BERT language models. We explored the most commonly used methods for word embeddings, including word2vec, GloVe, and fastText, as well as the BERT model. We investigated the performance of query expansion using word embeddings. We also analyzed the use of a pre-trained Amharic BERT model for masked language modeling, next sentence prediction, and text classification tasks. Amharic ad hoc information retrieval test collections that contain word-based, stem-based, and root-based text representations were used for evaluation. We conducted a detailed empirical analysis on the usability of word embeddings and BERT models on word-based, stem-based, and root-based corpora. Experimental results show that word-based query expansion and language modeling perform better than stem-based and root-based text representations, and fastText outperforms other word embeddings on word-based corpus.
Bibliometric analysis of articles on digital educational environments

This study’s main objective is to present bibliometric data on articles related to digital educational environments (DEEs) published in the journals indexed in Scopus database between 2003 and February 2023. The data collected from 61 articles published within the study’s scope were subjected to bibliometric analysis based on six categories: number of articles and citations, most cited articles, most used keywords, most influential countries, most-important institutions, and the most important journals. This study was designed as a descriptive study and offers bibliometric network maps of the most popular papers, keywords and countries, institutions, and journals. VOS viewer was used to create network diagrams and bibliometric analyses. The bibliometric analysis showed that the most frequently used keywords were digital learning environment(s), digital educational environment. The most prolific authors related to DEE are Antón-Sancho, Vergara, Barana, and Marchisio. The results showed that DEE studies were started in 2003 and the most articles were published in 2021. This study presents a global perspective on DEEs and proposes vision for future research.
The effects of digital transformation, digital leadership, and entrepreneurial motivation on business decision making and business process performance: Evidence from greater Amman municipality

The purpose of this research was to examine the effects of digital transformation, digital leadership, and entrepreneurial motivation on business decision making and business process performance in the Greater Amman Municipality. The study's hypotheses were put to the test and proven through a variety of quantitative analysis and data processing techniques. The research hypotheses were evaluated using a Structural Equation Model. Participants in this research are managers from the middle and high echelons of the Greater Amman Municipality, who were responsible for making decisions in their respective divisions. One hundred and eighty middle and upper-level managers with at least eight years' experience in public service were recruited for this study from the Greater Amman Municipality. Distributed questionnaires and the deliberate sampling technique were used to compile this data. Decision making had a positive effect on the business process performance, and the results of hypothesis testing data processing using Structural Equation Model indicated that digital transformation, digital leadership, and the motivation of the business environment all had positive and significant effects on business decision making and business process performance in the Greater Amman Municipality. To conduct their research, the authors opted to focus on digital transformation, which includes four primary components: process transformation, business model change, domain transformation, and cultural transformation. The following dimensions commander, communicator, collaborator, and co-creator were all indicative of digital leadership. Entrepreneurial motivation could be seen in the word’s communication, hassle-free work environment, mastering the art of constructive criticism, and trust among others. The novel aspect of this study is the model developed to explain the interplay between digital transformation, digital leadership, and entrepreneurial motivation, and how these factors influence business decision making and business process performance at Greater Amman Municipality.
GPT-4 vs. GPT-3.5: A Concise Showdown

As the echoes of ChatGPT's remarkable success continue to permeate the AI community, its formidable successor, GPT-4, has emerged, showing off a wealth of novel features. In this concise paper, we elucidate the capabilities of GPT-4 and conduct a comparative analysis with its predecessor, ChatGPT, offering insights into their relative strengths and advancements in the rapidly evolving field of generative AI. We also present a comprehensive summary of the major performance results reported by OpenAI on GPT-4 across various Natural Language Processing (NLP) tasks. We place great emphasis on the innovative advancements offered by GPT-4 in comparison to its predecessors. Our focus is on highlighting its remarkable performance while also mentioning its limitations. The purpose of this paper is to deliver a succinct understanding of the new features and performance benchmarks of GPT-4.
FedTCR: communication-efficient federated learning via taming computing resources

Abstract Federated learning (FL) enables clients learning a shared global model from multiple distributed devices while keeping training data locally. Due to the synchronous update mode between server and devices, the straggler problem has become a significant bottleneck for efficient FL. Existing approaches attempt to tackle this issue by using asynchronous-based model aggregation. However, these researches are only from the perspective of changing global model updating manner to mitigate straggler effect. They do not investigate the intrinsic reasons for the generation of the straggler effect, which could not fundamentally solve this problem. Furthermore, asynchronous-based approaches usually ignore those slow-responding but important local updates while frequently aggregating fast-responding ones during the whole training process, which may come with degradation in model accuracy. Thus, we propose FedTCR, a novel Federated learning approach via Taming Computing Resources. FedTCR includes a coarse-grained logical computing cluster construction algorithm (LCC) and a fine-grained intra-cluster collaborative training mechanism (ICT) as part of the FL process. The computing resource heterogeneity among devices and the communication frequency between devices and the server are indirectly tamed during this process, which substantially resolves the straggler problem and significantly improves the communication efficiency for FL. Experimental results show that FedTCR achieves much faster training performance, reducing the communication cost by up to $$8.59\,\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>8.59</mml:mn> <mml:mspace /> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> while improving $$13.85\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>13.85</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> model accuracy, compared to state-of-the-art FL methods.
Using Open API for the Development of Hybrid Classical-Quantum Services

Quantum Computing has started to demonstrate its first practical applications. As the technology develops to a point of maturity that allows quantum computers to expand commercially, large companies such as Google, Microsoft, IBM and Amazon are making a considerably effort to make them accessible through the cloud so that research and industry initiatives can test their capabilities. The characteristics of this paradigm and the lack of mature tools still make the process of defining, implementing, and running quantum, or hybrid classical-quantum software systems difficult compared with the procedures used for pure classical ones. To address this lack, we present a demonstration of a method for defining quantum services and the automatic generation of the corresponding source code through an extension of the Open API Specification. In this demo we present an extension that enables developers to define quantum services with a high abstraction level, link them with quantum circuits, and generate the source code of the service to be deployed in a quantum computer in the same way they do for classical services.
Quantum error correction with dissipatively stabilized squeezed-cat qubits

Noise-biased qubits are a promising route toward significantly reducing the hardware overhead associated with quantum error correction. The squeezed-cat code, a nonlocal encoding in phase space based on squeezed coherent states, is an example of a noise-biased (bosonic) qubit with exponential error bias. Here we propose and analyze the error correction performance of a dissipatively stabilized squeezed-cat qubit. We find that for moderate squeezing the bit-flip error rate gets significantly reduced in comparison with the ordinary cat qubit while leaving the phase-flip rate unchanged. Additionally, we find that the squeezing enables faster and higher-fidelity gates.
Fine-Tuning BERT-Based Pre-Trained Models for Arabic Dependency Parsing

With the advent of pre-trained language models, many natural language processing tasks in various languages have achieved great success. Although some research has been conducted on fine-tuning BERT-based models for syntactic parsing, and several Arabic pre-trained models have been developed, no attention has been paid to Arabic dependency parsing. In this study, we attempt to fill this gap and compare nine Arabic models, fine-tuning strategies, and encoding methods for dependency parsing. We evaluated three treebanks to highlight the best options and methods for fine-tuning Arabic BERT-based models to capture syntactic dependencies in the data. Our exploratory results show that the AraBERTv2 model provides the best scores for all treebanks and confirm that fine-tuning to the higher layers of pre-trained models is required. However, adding additional neural network layers to those models drops the accuracy. Additionally, we found that the treebanks have differences in the encoding techniques that give the highest scores. The analysis of the errors obtained by the test examples highlights four issues that have an important effect on the results: parse tree post-processing, contextualized embeddings, erroneous tokenization, and erroneous annotation. This study reveals a direction for future research to achieve enhanced Arabic BERT-based syntactic parsing.
Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously. Considering large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks, with language serving as a generic interface to empower this. Based on this philosophy, we present HuggingGPT, an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence.
Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection

Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to end-users for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experiment results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests.
Toward Hidden Data Detection via Local Features Optimization in Spatial Domain Images

Technology advancements made machine learning algorithms crucial to solving complex problems. Deep learning, a machine learning paradigm to design convolutional neural networks (CNNs), achieves promising performance in detecting confidential data, known as steganalysis. However, the existing steganalysis CNNs have not achieved optimal performance detecting accuracy and network stability. In this research, we propose a new approach within CNN to improve the secret data detection accuracy by optimizing the local features in the feature extraction stage of the spatial domain images. The performance is evaluated using the Break Our Steganographic System Base (BOSSBase) dataset with two standard adaptive steganography algorithms employing low payload capacities of 0.2 and 0.4 bits per pixel. The experimental results outperform the results of the previously published works in accuracy and network stability. The detection accuracy is improved in a range between 2.1% to 3.6%.