Reviewerly/exHarmony huggingface dataset:
Trading Aggregate Statistics Over Private Internet of Things Data

The exponential growth of Internet of Things (IoT) data has pushed the boundaries of data analysis, but it has also raised concerns about the increasing commoditization of personal privacy. The intriguing problem of trading private IoT data is the focal point of this paper. We delve into three fundamental questions: Firstly, we determine the minimum privacy that a broker must purchase from data owners to achieve aggregate statistics with a fixed accuracy goal. Secondly, we propose an innovative arbitrage-free pricing framework that empowers brokers to set prices for aggregate statistics, maximizing utility while ensuring fairness. Lastly, we address the challenge of fairly compensating data owners for their privacy losses while providing economic guarantees. To achieve these objectives, we propose a data trading framework in this paper. Our framework considers ubiquitous data correlations and the potential involvement of untrusted brokers, ensuring that the total compensations remain within the brokers budget; each owner is guaranteed to receive non-negative revenues, and cunning data owners cannot exploit misreported privacy demands to gain extra compensations. Experiments on the MovieLens dataset demonstrated the robustness of our framework.
Smart hospital definition: Academic and industrial perspective

Healthcare is a social and economic challenge in many countries, exacerbated by today's increasing demand. Many studies demonstrate that hospitals that move towards smartness, and some of their processes are smart, can provide more appropriate treatments and deal with problems more flexibly. It is axiomatic that implementing smart hospitals and healthcare tools requires a clear objective. However, the concept of a smart hospital lacks a comprehensive and broadly accepted definition, leading to varied interpretations and misconceptions. Many developments touted as 'smart' merely digitize existing hospital environments without truly embracing the full potential of smart technology. Furthermore, research studies have neglected to consider industrial perspectives, which will soon cause a gap between industry and academics in this concept. This research aims to explore the attributes of a smart hospital and use them to propose a definition for it, considering both scholarly and industrial viewpoints. The PRISMA method is employed to select academic and practical papers providing definitions and insights into smart hospitals or healthcare. 17 studies are analyzed, and a total, 83 characteristics are identified to describe the smart hospital. These features are categorized into three primary categories: "technologies", "services", and "goals". The most important features are determined by analyzing the frequencies of these characteristics across all the studies. In the results section, these data are presented in graphical form, highlighting both academic and industrial perspectives separately, as well as a combined analysis. Furthermore, an attempt is made to uncover trends in smart hospitals from 2015 to 2023. A comprehensive definition of the smart hospital, encompassing both academic and industrial perspectives, is proposed using the investigated characteristics. This study also presents research opportunities and discusses the existing gap between academia and industry concerning smart hospitals.
Forecasting Models

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Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems

Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have achieved better performance in terms of user engagement metrics such as clicks and browsing time. The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption. A natural following question is whether current recommendation algorithms are manipulating user preferences. If so, can we measure the manipulation level? In this article, we present a general framework for benchmarking the degree of manipulations of recommendation algorithms, in both slate recommendation and sequential recommendation scenarios. The framework consists of four stages, initial preference calculation, training data collection, algorithm training and interaction, and metrics calculation that involves two proposed metrics, Manipulation Score and Preference Shift. We benchmark some representative recommendation algorithms in both synthetic and real-world datasets under the proposed framework. We have observed that a high online click-through rate does not necessarily mean a better understanding of user initial preference, but ends in prompting users to choose more documents they initially did not favor. Moreover, we find that the training data have notable impacts on the manipulation degrees, and algorithms with more powerful modeling abilities are more sensitive to such impacts. The experiments also verified the usefulness of the proposed metrics for measuring the degree of manipulations. We advocate that future recommendation algorithm studies should be treated as an optimization problem with constrained user preference manipulations.
Robust partial face recognition using multi-label attributes

Partial face recognition (PFR) is challenging as the appearance of the face changes significantly with occlusion. In particular, these occlusions can be due to any item and may appear in any position that seriously hinders the extraction of discriminative features. Existing methods deal with PFR either by training a deep model with existing face databases containing limited occlusion types or by extracting un-occluded features directly from face regions without occlusions. Limited training data (i.e., occlusion type and diversity) can not cover the real-occlusion situations, and thus training-based methods can not learn occlusion robust discriminative features. The performance of occlusion region-based method is bounded by occlusion detection. Different from limited training data and occlusion region-based methods, we propose to use multi-label attributes for Partial Face Recognition (Attr4PFR). A novel data augmentation is proposed to solve limited training data and generate occlusion attributes. Apart from occlusion attributes, we also include soft biometric attributes and semantic attributes to explore more rich attributes to combat the loss caused by occlusions. To train our Attr4PFR, we propose an implicit attributes loss combined with a softmax loss to enforce Attr4PFR to learn discriminative features. As multi-label attributes are our auxiliary signal in the training phase, we do not need them in the inference. Extensive experiments on public benchmark AR and IJB-C databases show our method is 3% and 2.3% improvement compared to the state-of-the-art.
Video reversible data hiding: A systematic review

Reversible data hiding (RDH), also known as lossless data hiding, ensures the losslessly recovery of both the cover medium and the secret message at the decoder. For the video medium, RDH can also provide the effective protection and has attracted a lot of attention due to its unique reversibility. This paper aims to present a systematic review about the existing video RDH methods and the corresponding practical applications. We summarize the basic frameworks and the current optimization aspects in video RDH methods, including (1) the determination for the suitable embedding position, (2) the drift distortion minimization, (3) the extension for security improvement. Then, the fundamental trade-off for the evaluation metrics is quantified, and the performance comparison of the classical methods is given. Finally, the future research directions are discussed to exploit the further idea for this area.
Does e-service for research and community service boost the performance of university lecturers?

Indonesian universities are implementing various strategies to improve the quality and quantity of scientific publications. Sriwijaya University has restructured its research and community service grant services by implementing e-services through the new generation management information system of the institute of research and community service (SIM LPPM NG). Numerous studies have been performed to determine the factors that influence lecturer performance, particularly in scientific journals. The purpose of this research is to utilize a model to investigate the impact of system quality, information quality, and service quality on user satisfaction and performance. For partial least squares structural equation modelling (PLS-SEM), 280 respondents comprised the dataset. The results demonstrated that system quality, information quality, and service quality influence user satisfaction. Individual performance is influenced by the level of user satisfaction. According to the findings of this study, the DeLone and McLean information system success model can be used to evaluate obligatory e-services that are integrated.
The digital-mediated extensive reading on English Language learning of agriculture students

Reading is a skill that is initially taught in schools. Students are introduced to the alphabet and later learn to read texts. Reading Foreign languages is also introduced when students learn a foreign language. The student requires reading activities that include vocabulary learning. Extensive reading is one of the practical activities of reading. Nowadays, extensive reading activities include reading printed and digital text in e-books, journals, and internet and social media articles. In addition, digital extensive reading research on university students of agriculture is still rare. Thus, research on digital-mediated extensive reading in agricultural students’ needs to be conducted to investigate whether it improves their reading comprehension. This study involved 84 agricultural students majoring Agroecotechnology study program. The participants take an English course. The data for this study was taken from the pre-test and post-test scores and questionnaire. The data will be analyzed using descriptive and inferential statistics. The results of the study shows that the digital-mediated extensive reading effectively enhances the student's reading comprehension in English as academic purposes (EAP) setting, and the students express that the digital-mediated extensive reading provides flexibility, technology utilization, and reading.
Fishing risky behavior recognition based on adaptive transformer, reinforcement learning and stochastic configuration networks

The fishing behavior in a high-voltage environment may lead to electric shock accidents. This paper proposes a fishing risky behavior recognition method based on adaptive Transformer, reinforcement learning and stochastic configuration networks (SCNs). Firstly, the fishing behavior image sample training set is evaluated using the actor network based on linear entropy in the reinforcement learning module to select suitable training samples that possess abundant straight-line elements. Subsequently, the selected training samples are fed into a multi-scale spatial deep convolution to extract continuous spatial features of elongated objects. The deformable Transformer network, enhanced with adaptive encoding and decoding layers through SCNs, is used to obtain the position and detection results of the fishing rod. The trained Transformer model is evaluated by a defined credibility evaluation metric for acquiring rewards to update the training sample set iteratively. Then, an adaptive adjustment mechanism is constructed for the encoding and decoding layers of the adaptive Transformer network to establish a library of adaptive Transformer models with different encoding and decoding layer levels to accommodate the feature requirements of training samples in various scenarios. Finally, the blending learning method for combining the detection results from the model library is integrated with human body object detection and pose estimation methods, and a predefined expert system is utilized for logic reasoning on fishing risky behavior. Experimental results demonstrate the effectiveness of the proposed method in this paper.
An Enhanced Group Abnormity Detection Model in Social Networks Through Multi-Scale Knowledge Graph-Based Deep Learning

In area of group abnormity detection, deep learning-based methods have received more attention in recent years. But existing research works mostly failed to well-integrate complex graph-level characteristics of social network context into feature representation. To deal with this issue, this paper proposes an enhanced group abnormity detection model in social networks through multi-scale knowledge graph-based deep learning. First, multi-scale knowledge graph is formulated to extract and field knowledge as the fundamental support. Then, a recurrent neural network structure is utilized to express graph-level features, so as to aggregate the information of the surrounding nodes. For output of group abnormity detection results, the interaction information among users is further considered, and the correlation analysis of abnormal behaviors is conducted based on the probability expression. On this basis, probabilistic soft logic is used to express the relationship between abnormal association users and their behavior occurrence. Finally, a set of identification rules are developed to discover abnormal association users, and those who have the same association probability value are identified as abnormal user groups. Experiments are conducted on social network dataset to evaluate performance of the proposed model. The results reveal that it can make group abnormity identification with higher accuracy compared with baseline methods.
A Latency-Aware End-Edge Cooperative Insulator Detection Method with High Energy Efficiency and Accuracy

A prerequisite to ensure the stability of the power supply system is suitable functioning of transmission line equipment. However, the increasing deployment of transmission lines in modern power systems has introduced significant challenges to line inspection. While deep learning-based image detection techniques have shown promise in improving the efficiency and accuracy of insulator detection, they often require substantial computational resources and energy. This limitation hinders the consistent guarantee of accuracy and real-time performance on resource-constrained drones. To address this issue, this paper investigates the co-optimization problem of energy consumption and analytic accuracy in insulator image detection on unmanned aerial vehicles (UAVs). We propose a latency-aware end-edge cooperative insulator detection task offloading scheme with high energy efficiency and accuracy that aims to achieve optimal performance. Initially, we conducted an experimental analysis to examine the influence of input image resolution on the accuracy and latency of the CNN-based insulation detection model. Subsequently, we develop a model that takes into account the latency, analytic accuracy and energy consumption for image detection task offloading. Finally, we formalized a nonlinear integer optimization problem and designed a particle swarm optimization (PSO)-based task offloading scheme to optimize task accuracy and energy consumption while adhering to latency constraints. Extensive experiments validated the effectiveness of the proposed end-edge cooperative insulator detection method in optimizing accuracy and energy consumption.
Composition of feature selection techniques for improving the global horizontal irradiance estimation via machine learning models

The rise in photovoltaic capacity installed globally and the sporadic nature of solar resources underline the significance of solar radiation forecasting for grid integration. The choice of relevant climatic variables for estimating global horizontal irradiance (GHI) is crucial because it influences estimation accuracy. To keep this in mind, different feature selection techniques such as filter, wrapper, and embedded are computed and compared to identify a pertinent input variable for the prediction of GHI. In order to reduce the dimensionality of the input features, it is pertinent to remove unnecessary variables using feature selection techniques. This comprehensive study explores the use of tree-based ensemble machine learning (ML) models in the modeling of global solar radiation. Eighteen statistical and ML regressor algorithms, along with ten feature selection methods are evaluated based on statistical comparisons of their computational effectiveness and estimation errors. The lowest mean absolute error (MAE) and root mean squared error (RMSE) of 0.9938 and 2.5327 achieved by the extra-tree regressor algorithm reveals that it outperforms the other models on the test dataset. The integration of feature selection techniques improves predictive capabilities, reducing MAE and RMSE by 13.15% and 5.24%, respectively, for the Mutual Information method. This study also identifies influential features critical to accurate GHI estimation, such as solar zenith angle, diffuse horizontal irradiance, and direct normal irradiance, which provide valuable insights into the dynamics governing solar radiation estimation.
Asymmetric transition of electrical resistance in an all-solid-state redox device with Fe<sub>3</sub>O<sub>4</sub> and Li-ion electrolyte thin films for physical reservoir computing

Abstract In recent years, ion-gating devices have been used in artificial neuromorphic computing and achieved high performance for time-series data processing. However, the origin of this performance still needs to be clarified. In this study, we fabricated an all-solid-state redox device with functional material Fe 3 O 4 and Li-ion conducting solid electrolytes, and the transient response of the electrical resistance of the Fe 3 O 4 thin film to time-series data input was investigated. The transition between high and low electrical resistance states was asymmetric, and residual Li-ion in the thin film led to a hysteresis effect. These unique features, which are induced by ion-electron dynamics coupling, contributes to the high performance of physical reservoir computing utilizing an ion-gating device.
“De” – The <i>Xin</i> of TCVM Practice with an Emphasis on <i>Tui-na</i>

In the primordial Chinese classic, Dao De Ching, opposites are a consistent and universal theme – Yin and Yang permeate the Cosmos. Even the title exemplifies this – in the word “De” 德. As Western acculturated individuals our experience with “de” comes from the French word for “of”… A little word with a little meaning. In contrast, one little word in Chinese has one big meaning – the illusionary and polar state of nature summed up in the word “De”. For the Chinese, “De” connotes a profound concept, the concept of Virtue. Dao De Ching literally means “The Way (or Path) of Virtue Classic”. “De” is the embodiment of Virtue in the individual. Virtue includes both the values of honest living and the resulting power derived from it – particularly the power of healing. The character, or pictogram (德), representing “De” is composed of three parts. On the left is a person walking (progress or action), on the top right is a symbol meaning “something observed from all angles or directions shows no deviation” (being perfectly right), and a symbol on the bottom right representing the character for “Xin” (the Heart). The imagery can be interpreted as a virtuous person’s behavior perfectly reflects their heart. For the practitioner the incorporation of “Xin” underscores the importance of utilizing both the heart and mind in daily practice. The ability to combine one’s mind (thoughts) with one’s heart (intuition) will refine one’s being as a healer. Further, it implies “the ability to directly bypass one’s mind and know spontaneously with one’s heart, the heart of all things.”
A survey on deep learning-based spatio-temporal action detection

Spatio-temporal action detection (STAD) aims to classify the actions present in a video and localize them in space and time. It has become a particularly active area of research in computer vision because of its explosively emerging real-world applications, such as autonomous driving, visual surveillance and entertainment. Many efforts have been devoted in recent years to build a robust and effective framework for STAD. This paper provides a comprehensive review of the state-of-the-art deep learning-based methods for STAD. First, a taxonomy is developed to organize these methods. Next, the linking algorithms, which aim to associate the frame- or clip-level detection results together to form action tubes, are reviewed. Then, the commonly used benchmark datasets and evaluation metrics are introduced, and the performance of state-of-the-art models is compared. At last, this paper is concluded, and a set of potential research directions of STAD are discussed.
Ontology learning from object-relational mapping metadata and relational database

Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using object-relational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. During the initial phase, we analyzed the ORM metadata to map it to an resource description framework schema (RDF(S))-OWL representation of the ontology. In the subsequent phase, we applied mapping rules to convert the relational database (RDB) data into ontological instances, which are then represented as RDF triples. Using ORM metadata, we enhance the accuracy of the resulting ontology, particularly in terms of extracting concepts and hierarchical relationships. This study contributes to the field of ontology learning by showcasing a novel approach that leverages ORM metadata to create ontologies from relational databases.
Benchmarking a neutral-atom quantum computer

In this study, we simulated the algorithmic performance of a small neutral atom quantum computer and compared its performance when operating with all-to-all versus nearest-neighbor connectivity. This comparison was made using a suite of algorithmic benchmarks developed by the Quantum Economic Development Consortium. Circuits were simulated with a noise model consistent with experimental data from [Nature 604, 457 (2022)]. We find that all-to-all connectivity improves simulated circuit fidelity by [Formula: see text]–[Formula: see text], compared to nearest-neighbor connectivity.