Reviewerly/exHarmony huggingface dataset:
Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data

Collaborative filtering recommendation algorithms generate suggestions based on similar interactions between users. Although it provides accurate recommendations, the approach has two limitations: the popularity bias, which frequently suggests a small set of the most interacted items, and the systems’ black box functioning, as they are grounded on complex mathematical models. To improve such aspects in collaborative filtering algorithms, this paper introduces a multi-domain item reordering system based on the best explanation for an item, which are the best ranked paths extracted from a Linked Open Data knowledge graph connecting recommended and interacted items. To order paths, the algorithm assigns a value to the node attributes connecting two items by calculating the popularity of the property between interacted items that are rare in the full set of items. Results from two datasets of the movie and music domains comparing the proposed reordering system with six baselines of different collaborative filtering families showed that our easy-to-explain approach improved diversity and/or accuracy metrics.
Linking artificial intelligence to service sabotage

The widespread use of artificial intelligence (AI) technology in the service industry has made the conflict between service robots and frontline employees a hot topic. While research shows that the adoption of service robots may have a negative impact on employees' psychology and behavior, little is known about its effects on frontline employee' service sabotage. The current study explores the influencing mechanism of service sabotage in the context of AI introduction based on the STARA theory and conservation of resource theory. The results reveal that: (1) Frontline employee's AI awareness direclty affects service sabotage; (2) Organization-based self-esteem plays a partial mediating role between AI awareness and service sabotage; (3) Perceived organizational support weakens the effect of AI awareness on service sabotage.
SoK: Context and Risk Aware Access Control for Zero Trust Systems

Evolving computing technologies such as cloud, edge computing, and the Internet of Things (IoT) are creating a more complex, dispersed, and dynamic enterprise operational environment. New security enterprise architectures such as those based on the concept of Zero Trust (ZT) are emerging to meet the challenges posed by these changes. ZT systems treat internal and external networks as untrusted and subject both to the same security checking and control to prevent data breaches and limit internal lateral movement. Context awareness is a notion from the field of ubiquitous computing that is used to capture and react to the situation of an entity, based on the dynamics of a particular application or system context. The idea has been incorporated into several access control models. However, the overlap between context-aware access control and zero-trust security has not been fully explored. In this SoK, we conduct a systematic examination of ZT, context awareness, and risk-based access control to explore the critical elements of each and to identify areas of overlap and synergy to enhance the operation and deployment of ZT systems.
Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China

Photovoltaic (PV) power generation will become one of the main energy sources to achieve goals of carbon peak and carbon-neutral in China in the future. However, serious haze events have occurred frequently due to massive anthropogenic emissions in autumn and winter in China and resulted in a significant reduction of solar radiation reaching the Earth's surface. In this study, we conducted modifications to modelled solar radiation with the Weather Research and Forecasting (WRF)-Solar model by implementing the satellite-based aerosol optical depth (AOD) data into the model. The satellite-based AOD data have a higher spatial resolution and better accuracy. The studies were performed during the heavy haze period in the winter of 2016/2017 over North China (NC). Comparison with observations shows that the temporal-spatial variations of satellite-based AOD are consistent with those of AERONET and surface PM2.5 concentration, and satellite-based AOD at different sites or some months is slightly underestimated. Satellite-based AOD and surface PM2.5 concentrations in most of the southern areas of NC are significantly larger than in the northern parts. Simulated errors of Global horizontal irradiance (GHI) are reduced remarkably compared with those excluding the AOD data, especially in regions of the BTH (Beijing, Tianjin, and Hebei province), west of Shandong province, and north of Henan province. All RMSE and MAE of simulated GHI under three types of weather conditions declined by more than 20%, and 37%, respectively. Modelled errors of GHI on all-sky and cloudy-sky days are larger than those on clear-sky days due to the complicated aerosol-cloud-radiation interaction and simulated bias of cloud. Reductions of GHI caused by aerosols fall in between -140～-20 W m−2, and the spatial distribution is more refined and perform well compared to observations. Our results show that direct application of the satellite-based AOD data into the WRF-Solar model is very effective for improving the simulation of solar radiation and investigating the attenuation effect of solar radiation caused by aerosol in winter.
Execution Survey and State of the Art of Different ML-Based Ensemble Classifiers Approach Contextual Analysis of Spam Remark Location

The digital podium is proving as an increasingly important area for the contemporary development of civilization. However, it additionally engenders a rudimentary conundrum. Spamming is one of the most solemn quandaries that puts state-of-the-art security to the test. Spam wires, which send offensive messages to an immensely voluminous number of recipients, conventionally have become an apperceived security peril. There are various ways spam security issues can be addressed, including utilizing a machine learning (ML) complement system. Ensemble classifier is one of the most commonly used ML approximations. Ensemble methods use different models to amend execution. In various examination fields, like computational erudition, stats, and machine learning uses ensemble classifiers. This paper surveys traditional and verbally express-of-the-art ensemble approaches, accommodating a comprehensive overview for both practitioners and newcomers. In customary outfit strategies like Ada boost, Bagging classifier, extra trees sorts the ensemble techniques; gradient boost; logit boost; random forest; real Ada boost. This investigation is fixated on the ensemble frameworks to slant toward the spam (channel spam or ham remarks) security issue. Remark datasets are utilized for a fascinating judgment of over 41k comments and not for spam. We can split the experimental dataset into two parts. The first uses 30k for training, and the second utilizes the remaining 10k for testing. End-of-heuristics evaluation utilizing accuracy, precision, recall, f1 score, AUC score, model preparation time, and mean squared error reveals that Extra Trees outperforms numerous models in various exhibit metrics.
Development of a Clear‐Sky 3D Sub‐Grid Terrain Solar Radiative Effect Parameterization Scheme Based on the Mountain Radiation Theory

Abstract Terrains strongly affect the surface solar radiation (SSR) and energy balance, and further greatly modulate the weather and climate in rugged areas. In this study, we have developed a clear‐sky 3‐dimensional sub‐grid terrain solar radiative effect (3DSTSRE) parameterization scheme based on the mountain radiation theory with full consideration of the influences of 3‐dimensional configuration of terrains. Results show that the 3DSTSRE scheme achieves the equivalent effect of the downward SSR flux at the model grids derived from those explicitly calculated at the sub‐grids without reducing the calculating efficiency of numerical models. It performs well at model grids with different horizontal resolutions. The instant downward SSR flux calculated by the 3DSTSRE scheme at 76.8%, 84.8%, 88.7%, 91.6%, 93.0%, and 87.1% model grids with the horizontal resolution of 0.025°, 0.05°, 0.1°, 0.2°, 0.4°, and 0.8° in the areas featured by complex terrains shows relative errors within ±1.0% against those derived from the explicit calculations at sub‐grids, respectively. The normalized mean absolute errors of the instant downward SSR flux calculated by the 3DSTSRE scheme are below 1% (2%) throughout the day and the year for the model grids with resolutions ranging from 0.05° to 0.8° (of 0.025°). Although the performance of 3DSTSRE scheme decreases slightly under the conditions with much lower solar zenith angle and finer model horizontal resolution, the 3DSTSRE scheme developed in current study shows broad application prospects in various numerical models with the advantages of a solid physical foundation, high accuracy, strong portability and flexibility.
Towards a Secured IoT Communication: A Blockchain Implementation Through APIs

Years ago, intercommunication among systems was not at all possible, but with the development of the application programming interface (APIs-traditional) in the 1960s, this was possible. With the evolution from the 1960s, these APIs have advanced, and the birth of APIs (Modern) took place in the initial 2000s. Many conventional techniques and tools that possess diverse security issues and are operating in a fully functional manner can be solved to a greater extent with the help of APIs if integrated with modern and SMART technologies. One such example is a sub-group of artificial intelligence (AI) i.e., IoT, that possesses diverse issues and challenges and can be solved by the peer-to-peer technology blockchain. Here, blockchain is regarded as the SMART solution. The diverse attacks on IoT spectrum are illustrated in this paper to attain a better scope and security for IoT devices. Moreover, the implementation of blockchain is also illustrated in this paper to depict a convenient approach for solving the issues of IoT with decentralization attribute. The reason is that IoT illustrates a centralized architecture.
Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks

Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.
Online mathematics education as bio-eco-techno process: bibliometric analysis using co-authorship and bibliographic coupling

Under the COVID-19 pandemic, mathematics education has moved completely online. To tackle this new norm based on bio-eco-techno theories, this study aims to provide educators an overview of the research landscape for envisioning educational practices through bibliometric analysis of 319 articles and reviews published in peer-reviewed journals from 1993 to 2020. Country and institutional co-authorship depicts the social network structure of the field to identify top productive contributors. Bibliographic coupling of publications forms the conceptual structure, revealing research themes. Together, the results are mapped according to the bio-eco-techno perspective. The bioecological system highlights student achievement as the central concerns. The microsystem emphasizes techno-subsystems for supporting flipped learning. The exosystem and mesosystem require institution support for teacher pedagogical design, digital competencies, and collaboration. The macrosystem raises the issue of distribution or centralization in the strengths of online mathematics education and calls for greater cross-national boundary digital use and collaboration. The chronosystem asks: Does Covid-19 force the popularity of blended or flipped learning into online education? Based on the bio-eco-techno perspective, further recommendations are provided.The online version contains supplementary material available at 10.1007/s11192-022-04441-3.
Spline-Based Optimal Trajectory Generation for Autonomous Excavator

In this paper, we propose a novel trajectory generation method for autonomous excavator teach-and-plan applications. Rather than controlling the excavator to precisely follow the teaching path, the proposed method transforms the arbitrary slow and jerky trajectory of human excavation into a topologically equivalent path that is guaranteed to be fast, smooth and dynamically feasible. This method optimizes trajectories in both time and jerk aspects. A spline is used to connect these waypoints, which are topologically equivalent to the human teaching path. Then the trajectory is reparametrized to obtain the minimum time-jerk trajectory with the kinodynamic constraints. The optimal time-jerk trajectory generation method is both formulated using nonlinear programming and conducted iteratively. The framework proposed in this paper was integrated into a complete autonomous excavation platform and was validated to achieve aggressive excavation in a field environment.
Multi-Objective Multi-Learner Robot Trajectory Prediction Method for IoT Mobile Robot Systems

Robot trajectory prediction is an essential part of building digital twin systems and ensuring the high-performance navigation of IoT mobile robots. In the study, a novel two-stage multi-objective multi-learner model is proposed for robot trajectory prediction. Five machine learning models are adopted as base learners, including autoregressive moving average, multi-layer perceptron, Elman neural network, deep echo state network, and long short-term memory. A non-dominated sorting genetic algorithm III is applied to automatically combine these base learners, generating an accurate and robust ensemble model. The proposed model is tested on several actual robot trajectory datasets and evaluated by several metrics. Moreover, different existing optimization algorithms are also applied to compare with the proposed model. The results demonstrate that the proposed model can achieve satisfactory accuracy and robustness for different datasets. It is suitable for the accurate prediction of robot trajectory.
Aggressive behaviour of anti-vaxxers and their toxic replies in English and Japanese

Abstract The anti-vaccine movement has gained traction in many countries since the COVID-19 pandemic began. However, their aggressive behaviour through replies on Twitter—a form of directed messaging that can be sent beyond follow-follower relationships—is less understood, and even less is known about the language use differences of this behaviour. We conducted a comparative study of anti-vaxxers’ aggressive behaviours by analysing a longitudinal dataset of COVID-19 tweets in English and Japanese. We found two common features across these languages. First, anti-vaxxers most actively transmit targeted messages or replies to users with different beliefs, especially to neutral accounts, with significantly toxic and negative language, and these replies are often directed to posts about vaccine operations. Second, influential users with many followers and verified accounts are more likely to receive the most toxic replies from the anti-vaxxers. However, pro-vaccine accounts with a few followers receive highly toxic replies in English, which is different from the Japanese case. These results provide insights into both language-dependent and independent countermeasures against anti-vaxxers’ aggressive behaviour.
Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N=425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images (WSIs). The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P<0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N=143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.
Real-Time Efficient FPGA Implementation of the Multi-Scale Lucas-Kanade and Horn-Schunck Optical Flow Algorithms for a 4K Video Stream

The information about optical flow, i.e., the movement of pixels between two consecutive images from a video sequence, is used in many vision systems, both classical and those based on deep neural networks. In some robotic applications, e.g., in autonomous vehicles, it is necessary to calculate the flow in real time. This represents a challenging task, especially for high-resolution video streams. In this work, two gradient-based algorithms-Lucas-Kanade and Horn-Schunck-were implemented on a ZCU 104 platform with Xilinx Zynq UltraScale+ MPSoC FPGA. A vector data format was used to enable flow calculation for a 4K (Ultra HD, 3840 × 2160 pixels) video stream at 60 fps. In order to detect larger pixel displacements, a multi-scale approach was used in both algorithms. Depending on the scale, the calculations were performed for different data formats, allowing for more efficient processing by reducing resource utilisation. The presented solution allows real-time optical flow determination in multiple scales for a 4K resolution with estimated energy consumption below 6 W. The algorithms realised in this work can be a component of a larger vision system in advanced surveillance systems or autonomous vehicles.
Rethinking Surgical Captioning: End-to-End Window-Based MLP Transformer Using Patches

Surgical captioning plays an important role in surgical instruction prediction and report generation. However, the majority of captioning models still rely on the heavy computational object detector or feature extractor to extract regional features. In addition, the detection model requires additional bounding box annotation which is costly and needs skilled annotators. These lead to inference delay and limit the captioning model to deploy in real-time robotic surgery. For this purpose, we design an end-to-end detector and feature extractor-free captioning model by utilizing the patch-based shifted window technique. We propose Shifted Window-Based Multi-Layer Perceptrons Transformer Captioning model (SwinMLP-TranCAP) with faster inference speed and less computation. SwinMLP-TranCAP replaces the multi-head attention module with window-based multi-head MLP. Such deployments primarily focus on image understanding tasks, but very few works investigate the caption generation task. SwinMLP-TranCAP is also extended into a video version for video captioning tasks using 3D patches and windows. Compared with previous detector-based or feature extractor-based models, our models greatly simplify the architecture design while maintaining performance on two surgical datasets. The code is publicly available at https://github.com/XuMengyaAmy/SwinMLP_TranCAP .
CODA: Content-aware Frame Dropping Algorithm for High Frame-rate Video Streaming

Ultra High Definition Television (UHDTV) offers a better immersive audiovisual experience than HDTV by improving the aesthetic sense of the content [1]. How-ever, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, a low-latency pre-processing algorithm named COntent-aware frame Dropping Algorithm (CODA) is proposed to predict the optimized framerate per video segment in streaming scenarios. The optimized framerate <tex>$(\hat{f})$</tex> for every video segment at each target bitrate is modelled as an exponential decay (increasing) function whose decay rate is directly proportional to the temporal characteristics <tex>$(h)$</tex> [2] [3] of the video and the target bitrate <tex>$(b)$</tex>, and inversely proportional to the spatial characteristics <tex>$(E)$</tex> of the video. The encoding is carried out with the predicted framerate, saving encoding time and improving visual quality at lower bitrates. At the decoder side, the video is upscaled in the temporal domain to the original framerate <tex>$(f_{max})$</tex> for display.
Application of artificial intelligence for university information system

We know that Artificial Intelligence is play a major role in a multiplicity of fields ranging from manufacturing industries to sale industries, to customer care in public relations. Chat bots and online Artificial Intelligence (AI) systems which are already available that help people get answer to their day to day queries. So AI based college information system were invented that can solve any college related query. This will function as a university data Intelligence machine. Exam, fees, placement information related to university courses are answered by AI Bot using algorithm. The university enquiry chat bot will be constructed that examines exact enquiries response by student and answer consequently.