Reviewerly/exHarmony huggingface dataset:
Connecting artificial intelligence to value creation in services: mechanism and implications

Abstract Artificial intelligence (AI) is transforming services by providing personalized solutions, enhancing customer experience, and reducing operational costs. To tackle the challenges posed by the extensive and diverse literature on AI services, a comprehensive review was conducted using text mining techniques on journal articles. Twelve key research topics were identified, and the enabler–interface–business framework was developed. In addition, a value creation mechanism for AI services consisting of 6Cs (i.e., connection, collection, and computation, communication, control, and co-creation) was proposed. The study provides a complete overview of AI services, facilitating academic discussion and industrial transformation.
CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection

Anomaly detection has gained increasing attention in recent years, but detecting anomalies in time series data remains challenging due to temporal dynamics, label scarcity, and data diversity in real-world applications. To address these challenges, we introduce a novel method for anomaly detection in time series data, called CL-TAD (Contrastive-Learning-based method for Times series Anomaly Detection), which employs a contrastive-learning-based representation learning technique. Inspired by the successes of reconstruction-based approaches and contrastive learning approaches, the proposed method seeks to leverage these approaches for time series anomaly detection. The CL-TAD method is comprised of two main components: positive sample generation and contrastive-learning-based representation learning. The former component generates positive samples by trying to reconstruct the original data from masked samples. These positive samples, in conjunction with the original data, serve as input for the contrastive-learning-based representation learning component. The representations of input original data and their masked data are used to detect anomalies later on. Experimental results have demonstrated that the CL-TAD method achieved the best performance on five datasets out of nine benchmark datasets over 10 other recent methods. By leveraging the reconstruction learning and contrastive learning techniques, our method offers a promising solution for effectively detecting anomalies in time series data by handling the issues raised by label scarcity and data diversity, delivering high performance.
Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.
Continuous-Variable Pairwise Entanglement Based on Optoelectromechanical System

We show how to generate stationary continuous-variable pairwise entanglement between microwave modes in a hybrid optoelectromechanical system, which consists of a single Fabry–Pérot cavity, a parallel-plate capacitor with a moving element as the mechanical resonator, and several pairs of microwave cavities. The optical mode and mechanical resonator are coupled via radiation pressure; meanwhile, several pairs of the microwave mode and mechanical resonator are capacitively coupled. Under an experimentally reachable parameter regime, we show the influence of different key parameters on pairwise entanglement and find that it is also robust against temperature. Our model and results are expected to provide a new perspective on quantum networks with increasingly large scales, quantum internet with multiple local users, and multiport microwave quantum illumination radar.
Secure Two-Party Decision Tree Classification Based on Function Secret Sharing

Decision tree models are widely used for classification tasks in data mining. However, privacy becomes a significant concern when training data contain sensitive information from different parties. This paper proposes a novel framework for secure two-party decision tree classification that enables collaborative training and evaluation without leaking sensitive data. The critical techniques employed include homomorphic encryption, function secret sharing (FSS), and a custom secure comparison protocol. Homomorphic encryption allows computations on ciphertexts, enabling parties to evaluate an encrypted decision tree model jointly. FSS splits functions into secret shares to hide sensitive intermediate values. The comparison protocol leverages FSS to securely compare attribute values to node thresholds for tree traversal, reducing overhead through efficient cryptographic techniques. Our framework divides computation between two servers holding private data. A privacy-preserving protocol lets them jointly construct a decision tree classifier without revealing their respective inputs. The servers encrypt their data and exchange function secret shares to traverse the tree and obtain the classification result. Rigorous security proofs demonstrate that the protocol protects data confidentiality in a semihonest model. Experiments on benchmark datasets confirm that the approach achieves high accuracy with reasonable computation and communication costs. The techniques minimize accuracy loss and latency compared to prior protocols. Overall, the paper delivers an efficient, modular framework for practical two-party secure decision tree evaluation that advances the capability of privacy-preserving machine learning.
Federated Learning for the Metaverse: A Short Survey

The Metaverse, a 3-dimensional virtual realm mirroring real-world objects, promises transformative experiences. Its potential is tempered by data collection, confidentiality, and privacy concerns. In tandem with AI-enabled technologies, edge computing can provide a practical solution to overcoming many of these challenges. This paper argues that combining AI-enabled technologies with edge computing-specifically via federated learning (FL) can address these challenges. FL, as a privacy-centric distributed machine learning (ML) approach, enables knowledge sharing among Metaverse clients without compromising user data. However, while FL posits potential resolutions for the Metaverse, a discernible lacuna remains in the comprehensive study of its overarching consequences. The literature misses an extensive study of adopting these new deployment architectures, giving it significant research impact. This paper bridges this knowledge gap, exploring the symbiosis between the Metaverse and FL. We first introduce the foundational concepts of both domains and detail enabling technologies such as digital twins, the Internet of Things, brain-computer interfaces, blockchain, and extended reality. Next, we delve into practical applications of FL within the metaverse, spanning sectors like healthcare, education, e-commerce, gaming, and the military. Finally, the paper highlights the key challenges and future directions for integrating FL within the metaverse ecosystem.
Predictive and Robust Field-of-View Selection for Virtual Reality Video Streaming

Virtual reality technology is rapidly evolving towards providing immersive user experience. By predicting user’s field-of-view (FoV) in advance, only transmitting content viewed by the user can help to meet the stringent requirements of delivering enhanced video quality. In this paper, a predictive and robust FoV selection algorithm is devised to dynamically identify a subset of video tiles, guided by the prediction error due to user’s stochastic head movement. Considering that the required data size to cover actual FoV is positively correlated with the prediction error, we construct a context space represented by the prediction error. A partition method of context space is exploited to discretize continuous context, where the prediction errors are classified effectively, and adaptive tile selection can be carried out. Then, a padding strategy is proposed by estimating the transmission gain of each tile in different prediction context, which improves the coverage of transmitted content around the true FoV at less bandwidth cost. Experimental results based on a real-world dataset demonstrate that the proposed algorithm can achieve dynamic FoV adjustment, and effectively improve user’s quality of experience.
Forecasting Hospital Readmissions with Machine Learning

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78.
Reconfigurable Hexapartite Cluster States by Four‐Wave Mixing Processes with a Spatially Structured Pump

Abstract The universal quantum computation provides a new paradigm for information processing. One feasible approach is measurement‐based one‐way quantum computation utilizing cluster states. Generally, the generation of cluster states with different structures for implementing on‐demand quantum computation needs different experimental setup, which limits its scalability. Here, the reconfigurable hexapartite cluster states created by postprocessing the quadrature information of hexapartite entangled state are demonstrated. Without altering the experimental layout, nine quantum correlated states with different structures, especially three cluster states, are implemented. In particular, such method can effectively reduce the excess noise introduced by creating cluster states under limited squeezing resources. This approach provides an avenue for realizing large‐scale reconfigurable cluster states without changing the experimental architecture.
APONS: Accelerating Federated Learning Architecture in 6G Based on Parameter Optimization and Neural Architecture Search

The Federated Edge Learning technique can successfully assist the edge deployment of 6G networks. Using a lot of user data, it can train machine learning models by communicating with many edge clients. However, in 6G-enabled mobile edge computing networks, heterogeneity and resource limitations among distributed edge clients can lower the effectiveness of Federated Edge Learning training. This study suggests a novel Federated Learning framework to expedite the training process. This paper proposes a new model based on the relationship between training loss, resource consumption, and heterogeneity. Then, to reduce latency effects brought on by client heterogeneity and resource limitations, we suggest using a search technique called APONS to generate local models of edge clients and optimal imprecision of band allocation. As a result, modifying the percentage of frequency bands and the local model of the edge client's inaccuracy can significantly increase training efficiency. The simulation outcomes demonstrate our algorithm's benefits in increasing training effectiveness.
Research on Human-centered Design in College Music Education to Improve Student Experience of Artificial Intelligence-based Information Systems

The integration of Artificial Intelligence (AI) technology with music instruction necessitates a delicate balance between technical advancement and the maintenance of humanistic teaching. This study examined how human-centered design concepts were used to optimize the integration of AI while also investigating the effects of AI technology on college-level music instruction in China. It aimed to identify potential, difficulties and make recommendations for ethical AI deployment in this particular environment. Semi-structured interviews with 20 music students and professors from Chinese higher education institutions were conducted using a qualitative study design. To condense significant themes and subthemes from the data, open coding, axial coding, and selective coding were used. The study revealed complex interactions between AI and Chinese music instruction. Themes included "Enhanced Learning with AI", emphasizing AI's role in motivating and personalizing music education; "User-Centric Design", emphasizing the importance of intuitive interfaces and aesthetic appeal; "Collaboration and Peer Learning", demonstrating AI's facilitation of collaborative projects; "Technical Challenges and Ethical Concerns", addressing technical obstacles and ethical concerns; and "Educator Support and Curriculum Alignment", emphasizing the importance of educator support and curriculum alignment. This study adds knowledge about how AI can be successfully incorporated into Chinese music teaching. It informs best practices for the adoption of AI, ensuring that technology enhances the learning experience for students while preserving cultural nuances. The study improves the conversation about innovative pedagogy and responsible technology integration. Implications include the potential for AI to change music education, cultural preservation, and global viewpoints. However, drawbacks such as sample bias and the dynamic nature of AI technology necessitate more study and development of educational techniques that use AI. Personalization and multimodal methods used in college music instruction in the future, to help increase student involvement. The importance of ethical issues, long-term effect analyses, and user-centered design will call for interdisciplinary cooperation. The future of AI-enhanced music education will also be shaped by assuring accessibility, diversity, and active engagement in policy and regulation discussions.
A hybrid multi-source data fusion for word, sentence, aspect, and document-level sentiment analysis on real-time databases

Sentiment analysis (SA)at the sentence, aspect, and document levels determines the sentiment of particular aspect phrases in a given sentence. Due to their capacity to extract sentiment information from text in aspect-level sentiment classification, neural networks (NNs) have achieved significant success. Generally speaking, sufficiently sizable training corpora are necessary for NNs to be effective. The performance of NN-based systems is reduced by the small size of the aspect-level corpora currently available. In this research, we suggest a gated bilateral recurrent neural network (G-Bi-RNN) as a foundation for multi-source data fusion, their system offers sentiment information that several sources. We develop a uniform architecture specifically to include information from sentimental lexicons, including aspect- and sentence-level corpora. To further provide aspect-specific phrase representations for SA, we use G-Bi-RNN, a deep bilateral Transformer-based pre-trained language model. We assess our methods using SemEval 2014 datasets for laptops and restaurants. According to experimental findings, our method consistently outperforms cutting-edge techniques on all datasets. We use a number of well-known aspect-level SA datasets to assess the efficacy of our model. Experiments show that when compared to baseline models, the suggested model can produce state-of-the-art results.
A Comparison of End-to-End Decision Forest Inference Pipelines

Decision forest, including RandomForest, XGBoost, and LightGBM, dominates the machine learning tasks over tabular data. Recently, several frameworks were developed for decision forest inference, such as ONNX, TreeLite from Amazon, TensorFlow Decision Forest from Google, HummingBird from Microsoft, Nvidia FIL, and lleaves. While these frameworks are fully optimized for inference computations, they are all decoupled with databases and general data management frameworks, which leads to cross-system performance overheads. We first provided a DICT model to understand the performance gaps between decoupled and in-database inference. We further identified that for in-database inference, in addition to the popular UDF-centric representation that encapsulates the ML into one User Defined Function (UDF), there also exists a relation-centric representation that breaks down the decision forest inference into several fine-grained SQL operations. The relation-centric representation can achieve significantly better performance for large models. We optimized both implementations and conducted a comprehensive benchmark to compare these two implementations to the aforementioned decoupled inference pipelines and existing in-database inference pipelines such as Spark-SQL and PostgresML. The evaluation results validated the DICT model and demonstrated the superior performance of our in-database inference design compared to the baselines.
A cost effective communication model for requirements elicitation in global software development

Requirement elicitation stands as a pivotal activity within requirement engineering, gaining even greater significance in the context of global software development. Effective communication among stakeholders assumes paramount importance in this arena. Factors such as time zone disparities, cultural variations, and language differences exert a formidable impact on communication within the sphere of global software development. These dynamics inevitably impinge upon timely coordination, potentially compromising the software's quality. In response, researchers have proffered communication models tailored for requirement elicitation within the ambit of global software development. The purpose of this study is to conduct an in-depth critical review of existing communication models for demand elicitation in global software development. Through this comprehensive review, we aim to discern prevailing publication trends, provide an introductory overview, and illuminate the strengths and limitations inherent in the existing communication models. By identifying these limitations, we seek to advance a novel, low-cost communication approach designed primarily for demand elicitation in global software development. To culminate our endeavor, we will undertake a case study-based experiment, meticulously designed to assess the efficacy and practical utility of the proposed techniques.
Feature Selection for Automated QoE Prediction

With the huge number of broadband users, automated network management becomes of huge interest to service providers. A major challenge is automated monitoring of user Quality of Experience (QoE), where Artificial Intelligence (AI) and Machine Learning (ML) models provide powerful tools to predict user QoE from basic protocol indicators such as Round Trip Time (RTT), retransmission rate, etc. In this paper, we introduce an effective feature selection method along with the corresponding classification algorithms to address this challenge. The simulation results show a prediction accuracy of 78% on the benchmark ITU ML5G-PS-012 dataset, improving 11% over the state-of-the-art result whilst reducing the model complexity at the same time. Moreover, we show that the local area network round trip time (LAN RTT) value during daytime and midweek plays the most prominent factor affecting the user QoE.
Fabricatable 90° Pop‐ups: Interactive Transformation of a 3D Model into a Pop‐up Structure

Abstract Ninety‐degree pop‐ups are a type of papercraft on which a three‐dimensional (3D) structure pops up when the angle of the base fold is 90°. They are fabricated by cutting and creasing a single sheet of paper. Traditional 90° pop‐ups are limited to 3D shapes only comprising planar shapes because they are made of paper. In this paper, we present novel pop‐ups, fabricatable 90° pop‐ups that employ the 90° pop‐up mechanism, consist of components with curved shapes, and can be fabricatable using a 3D printer. We propose a method for converting a 3D model into a fabricatable 90° pop‐up. The user first interactively designs a layout of pop‐up components, and the system automatically deforms the components using the 3D model. Because the generated pop‐ups contain necessary cuts and folds, no additional assembly process is required. To demonstrate the feasibility of the proposed method, we designed and fabricated various 90° pop‐ups using a 3D printer.
Using Pretrained Vision Transformer for Breast Cancer Binary Classification

Breast cancer is presently the most prevalent cancer diagnosed among women on a global scale. Originating in the breast tissue, it stands as a primary cause of mortality in women. Timely detection of breast cancer plays a pivotal role in successful treatment, as early-stage diagnosis offers curative possibilities. Breast cancer is classified into two types: malignant and benign tumors. Malignant tumors pose a greater threat due to their accelerated growth rate, necessitating accurate identification of tumor type to determine the appropriate course of treatment for breast cancer patients. In this paper, we introduce an innovative approach employing Transformers for the classification of breast cancer images. Our proposed methodology leverages pretrained ViT Transformer, previously trained on an auxiliary domain, which are then integrated with an additional network comprising fully connected layers. Subsequently, this network is trained using breast cancer images in isolation. Our findings, based on the analysis of a benchmark dataset, demonstrate the remarkable classification accuracy of 97.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> achieved by our proposed model.