Reviewerly/exHarmony huggingface dataset:
Semorph: A Morphology Semantic Enhanced Pre-trained Model for Chinese Spam Text Detection

Chinese spam text detection is essential for social media since these texts affect the user experience of Chinese speakers and pollute the community. The underlying text classification method is employed to explore the unique combinations of characters that represent clues of spam information from annotated or further augmented data. However, based on the diversity of Chinese characters in glyphs, the spammers frequently wrap the spam content in another visually close text to fool the model but make sure people understand. This paper proposes to adopt the essence of human cognition of these adversarial texts into spam text detection models, by designing a pre-trained model to learn the morphology semantics of Chinese characters and represent their contextual meanings from scratch. The model pre-trains on self-supervised Chinese corpus and fine-tunes on spam-annotated community texts. Besides, cooperating with the pre-trained model that can capture the morphological features of Chinese, a new data perturbation method is introduced to guide the optimization towards the direction of recognizing the actual meaning of a text after spammers tamper with partial characters by visually close ones. The experimental results have shown that our proposed methodology can notably improve the performance of spam text detection as well as maintain robustness against adversarial samples.
IoT-Based Intelligent Monitoring System Applying RNN

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources.
Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis-Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.
Acceleration of Deep Neural Network Training Using Field Programmable Gate Arrays

Convolutional neural network (CNN) training often necessitates a considerable amount of computational resources. In recent years, several studies have proposed for CNN inference and training accelerators in which the FPGAs have previously demonstrated good performance and energy efficiency. To speed up the processing, CNN requires additional computational resources such as memory bandwidth, a FPGA platform resource usage, time, power consumption, and large datasets for training. They are constrained by the requirement for improved hardware acceleration to support scalability beyond existing data and model sizes. This paper proposes a procedure for energy efficient CNN training in collaboration with an FPGA-based accelerator. We employed optimizations such as quantization, which is a common model compression technique, to speed up the CNN training process. Additionally, a gradient accumulation buffer is used to ensure maximum operating efficiency while maintaining gradient descent of the learning algorithm. To validate the design, we implemented the AlexNet and VGG-16 models on an FPGA board and laptop CPU along side GPU. It achieves 203.75 GOPS on Terasic DE1 SoC with the AlexNet model and 196.50 GOPS with the VGG-16 model on Terasic DE-SoC. Our result also exhibits that the FPGA accelerators are more energy efficient than other platforms.
Weibull-Open-World (WOW) Multi-Type Novelty Detection in CartPole3D

Algorithms for automated novelty detection and management are of growing interest but must address the inherent uncertainty from variations in non-novel environments while detecting the changes from the novelty. This paper expands on a recent unified framework to develop an operational theory for novelty that includes multiple (sub)types of novelty. As an example, this paper explores the problem of multi-type novelty detection in a 3D version of CartPole, wherein the cart Weibull-Open-World control-agent (WOW-agent) is confronted by different sub-types/levels of novelty from multiple independent agents moving in the environment. The WOW-agent must balance the pole and detect and characterize the novelties while adapting to maintain that balance. The approach develops static, dynamic, and prediction-error measures of dissimilarity to address different signals/sources of novelty. The WOW-agent uses the Extreme Value Theory, applied per dimension of the dissimilarity measures, to detect outliers and combines different dimensions to characterize the novelty. In blind/sequestered testing, the system detects nearly 100% of the non-nuisance novelties, detects many nuisance novelties, and shows it is better than novelty detection using a Gaussian-based approach. We also show the WOW-agent’s lookahead collision avoiding control is significantly better than a baseline Deep-Q-learning Networktrained controller.
Answering regular path queries mediated by unrestricted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">SQ</mml:mi></mml:math> ontologies

A prime application of description logics is ontology-mediated query answering, with the query language often reaching far beyond instance queries. Here, we investigate this task for positive existential two-way regular path queries and ontologies formulated in the expressive description logic SQu, where SQu denotes the extension of the basic description logic ALC with transitive roles (S) and qualified number restrictions (Q) which can be unrestrictedly applied to both non-transitive and transitive roles (⋅u). Notably, the latter is usually forbidden in expressive description logics. As the main contribution, we show decidability of ontology-mediated query answering in that setting and establish tight complexity bounds, namely 2ExpTime-completeness in combined complexity and coNP-completeness in data complexity. Since the lower bounds are inherited from the fragment ALC, we concentrate on providing upper bounds. As main technical tools we establish a tree-like countermodel property and a characterization of when a query is not satisfied in a tree-like interpretation. Together, these results allow us to use an automata-based approach to query answering.
Secure Medical Data Model Using Integrated Transformed Paillier and KLEIN Algorithm Encryption Technique with Elephant Herd Optimization for Healthcare Applications

In the healthcare industry, where concerns are frequently and appropriately focused on saving someone’s life, access to interfaces and computer systems storing sensitive data, such as medical records, is crucial to take into account. Medical information has to be secretive and protected by the laws of privacy with restrictions on its access. E-health security is a holistic notion that encompasses available medical data’s integrities and confidentiality which ensures that data are not accessed by unauthorized people and allow doctors to offer proper treatment. The patients’ data need to be secured on servers holding medical data. This work adds new features for ensuring storage and access safety through ITPKLEIN-EHO (integrated transformed Paillier and KLEIN algorithms) that use EHOs (elephant herd optimizations) to provide lightweight features. The key space affects lightweight encryption techniques in general. The EHOs (elephant herd optimizations) optimize key spaces by adjusting iteration rounds. The main goal is to encrypt EEGs (electroencephalographic signals) in healthcare and send it to end users using the proposed ITPKLEIN-EHO approach. This suggested technique utilizes MATLAB for its tests on various EEG data sets for implementation. The simulations of the proposed IRPKLEIN-EHO technique are evaluated with other existing techniques in terms of MSEs, PSNRs, SSIMs, PRDs, and encryption/decryption times.
The Use of Data Science in a National Statistical Office

Objective statistical information is vital to an open and democratic society.It provides a solid foundation so that informed decisions can be made by our elected representatives, businesses, unions, and non-profit organizations, as well as individual citizens.There is a great shift towards a more virtual and digital economy and society.The traditional official statistical systems are centered on surveys, and must be adapted to this new digital reality.National statistical offices have been increasingly embracing non-survey data sources along with data science methods to better serve society.This paper provides a blueprint for the application of data science in a government organization.It describes how data science enables innovation and the delivery of new high-value, high-quality, relevant, and trusted products that reflect the ever-evolving needs of our society and economy.We discuss practical operational considerations and impactful data science applications that supported the work of Statistics Canada's analysts and front-line health agencies during the pandemic.We also discuss the innovative use of scanner data in lieu of survey data for large business respondents in the retail industry.We will describe computer vision methodologies, including machine learning models used to detect the start of buildings construction from satellite imagery, greenhouse area and greenhouse production, as well as crop types detection.Data science and machine learning methods have tremendous potential, and their ethical use is of primary importance.We conclude the paper with a forward-facing view of responsible data science use in statistical production.
Quantum positioning and ranging via a distributed sensor network

A quantum sensor network with multipartite entanglement offers a sensitivity advantage in optical phase estimation over the classical scheme. To tackle richer sensing problems, we construct a distributed sensor network with four nodes via four partite entanglements, unveil the estimation of the higher order derivative of radio-frequency signal phase, and unlock the potential of quantum target ranging and space positioning. Taking phased-array radar as an example, we demonstrate the optimal quantum advantages for space positioning and target ranging missions. Without doubt, the demonstration that endows innovative physical conception opens up widespread application of quantum sensor networks.
A comprehensive review of stacking methods for semantic similarity measurement

This article presents a comprehensive review of stacking methods commonly used to address the challenge of automatic semantic similarity measurement in the literature. Since more than two decades of research have left various semantic similarity measures, scientists and practitioners often find many difficulties in choosing the best method to put into production. For this reason, a novel generation of strategies has been proposed to use basic semantic similarity measures using base estimators to achieve a better performance than could be gained from any of the semantic similarity measures. In this work, we analyze different stacking techniques, ranging from the classical algebraic methods to the most powerful ones based on hybridization, including blending, neural, fuzzy, and genetic-based stacking. Each technique excels in aspects such as simplicity, robustness, accuracy, interpretability, transferability, or a favorable combination of several of those aspects. The goal is that the reader can have an overview of the state-of-the-art in this field.
A conceptual framework for safe object initialization: a principled and mechanized soundness proof of the Celsius model

An object under initialization does not fulfill its class specification yet and can be unsafe to use as it may have uninitialized fields. It can sometimes be useful to call methods on such a partially initialized object in order to compute a complex initial value, or to let the object escape its constructor in order to create mutually recursive objects. However, inadvertent usage of uninitialized fields can lead to run-time crashes. Those subtle programming errors are not statically detected by most modern compilers. While many other features of object-oriented programming languages have been thoroughly studied over the years, object initialization lacks a simple, systematic, and principled treatment. Building on the insights of previous work, we identify a set of four core principles for safe initialization: monotonicity, authority, stackability, and scopability. We capture the essence of the principles with a minimal calculus, Celsius, and show that the principles give rise to a practical initialization system that strikes a balance between expressiveness and simplicity. The meta-theory of the system is entirely mechanized using the Coq proof assistant. We believe that our approach based on well-identified core principles sheds new light on the underlying mechanisms ensuring safety and could serve as a basis for language design when faced with similar challenges.
Evaluating the effectiveness of physical exercise in improving standardized testing performances through attention indices

Abstract Introduction In the United States, standardized tests have risen in prevalence, extending their importance from education placement to employment. Attention is crucial to improving testing performance. Past studies have established that acute, coordinative, aerobic exercise improves attention, which is measured by the D2 Attention Test, emotional analysis, reading time, and eye movement tracking. No studies have drawn connections between physical exercise's quantifiable improvement in attention to improvements in standardized tests; therefore, this study would attempt to do so. Methods This study defines attention to be positively related to reading speed and negatively related to the number of eye drifts. High school students were selected to read and answer two reading passages from an SAT (Scholastic Assessment Test) exam, before and after a short 80% intensity run. Their reading times, facial video, and test scores were recorded. Dlib plots the facial landmark and OpenCV tracks movement of the pupil. Results Through paired‐samples t ‐tests, this study found out that after exercise, subjects displayed increased reading speed and fewer eye drifts, coupled with increased mean scores. Conclusion Thus, this study demonstrated that running, as an acute, coordinative, aerobic exercise, helps increase the testing performance of the SAT reading section by measuring attention. Future research could focus on including head movement as an attention index, replicate the experiment on different standardized tests or exercises, and conduct natural experiments to better simulate real‐life conditions to increase applicability.
Smart Contracts for Households Managed by Smart Meter Equipped with Blockchain and Chain 2

Managing electricity effectively also means knowing as accurately as possible when, where and how electricity is used. Detailed metering and timely allocation of consumption can help identify specific areas where energy consumption is excessive and therefore requires action and optimization. All those interested in the measurement process (distributors, sellers, wholesalers, managers, ultimately customers and new prosumer figures - producers / consumers -) have an interest in monitoring and managing energy flows more efficiently, in real time.Smart meter plays a key role in sending data containing consumer measurements to both the producer and the consumer, thanks to chain 2. It allows you to connect consumption and production, during use and the customer’s identity, allowing billing as Time-of-Use or Real-Time Pricing, and through the new two-way channel, this information is also made available to the consumer / prosumer himself, enabling new services such as awareness of energy consumption at the very moment of energy use.This is made possible by latest generation devices that "talk" with the end user, which use chain 2 and the power line for communication.However, the implementation of smart meters and related digital technologies associated with the smart grid raises various concerns, including, privacy. This paper provides a comparative perspective on privacy policies for residential energy customers, moreover, it will be possible to improve security through the blockchain for the introduction of smart contracts.
A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients

Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan’s National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning.
The Impact of Mobile Learning on Undergraduate Students’ Cognitive Learning Outcomes: A Meta-Analytic Review

The objective of this research was to identify the overall effectiveness of mobile-based interventions on the learning performance of tertiary education students. Besides, the paper sought to contour common variables across eligible studies that might have favoured learning achievement. A meta-analysis of the impacts of ubiquitous learning interventions on academic outcomes over 12 years (2010–2021) was carried out, in which 12 experimental and quasi-experimental studies were analysed. A random-effects model yielded the overall standardised mean difference of .52 [.37 to .67] in favour of mobile learning environment, which is a moderate effect size. Common facilitators of achievement as distinguished by experimenters and participants were commendable multimedia design, diversified content, the opportunity for relevant interaction with teachers and peers, as well as flexibility and accessibility of learning activities due to handheld devices. These findings suggest the potential of mobile-assisted interventions in contributing to student educational outcomes. This study summarises the extant literature to some degree thus advancing the investigation into the effectiveness and expediency of mobile technologies in higher education.
Impact of Self-View Latency on Quality of Experience: Analysis of Natural Interaction in XR Environments

The rise of eXtended Reality (XR) has led to multiple ways of including the user’s body in interactive experiences. However, the delay limits of self-view rendering in interactive XR remain unexplored. This article presents a minimum self-view latency system and an interactive task-based experiment to study the influence of different levels of self-view delay on Quality of Experience (QoE) and task performance. During the experiment, 23 users tested 8 delay conditions (from 190 to 597 ms) while building block-based models. The results show a hard threshold in terms of involvement and overall quality around 450ms. However, the impact on adaptation and execution time was less pronounced. This indicates that although users adapted to the task in a certain way, their immersion was severely affected above a certain self-view delay value.
Role of Artificial Intelligence in Livestock and Poultry Farming

One of the technologies, artificial intelligence (AI), requires quick adoption in the livestock sector. The use of AI technology can be highly beneficial in a number of key areas in the livestock business, including monitoring, forecasting, optimizing the growth of farm animals, contend with pests, diseases, threats of biosecurity, and monitoring farm animals and farm management. Livestock farms will be helped by artificial intelligence to gather and analyses of data in order to precisely forecast consumer behavior, including purchasing patterns, top trends, etc. Operation of farm will be done by using automatic means which directly minimize the expense and increase the quality of egg, milk and meat products but this system needs some extra investment to start.