Reviewerly/exHarmony huggingface dataset:
De-biased Teacher: Rethinking IoU Matching for Semi-supervised Object Detection

Most of the recent research in semi-supervised object detection follows the pseudo-labeling paradigm evolved from the semi-supervised image classification task. However, the training paradigm of the two-stage object detector inevitably makes the pseudo-label learning process for unlabeled images full of bias. Specifically, the IoU matching scheme used for selecting and labeling candidate boxes is based on the assumption that the matching source~(ground truth) is accurate enough in terms of the number of objects, object position and object category. Obviously, pseudo-labels generated for unlabeled images cannot satisfy such a strong assumption, which makes the produced training proposals extremely unreliable and thus severely spoil the follow-up training. To de-bias the training proposals generated by the pseudo-label-based IoU matching, we propose a general framework -- De-biased Teacher, which abandons both the IoU matching and pseudo labeling processes by directly generating favorable training proposals for consistency regularization between the weak/strong augmented image pairs. Moreover, a distribution-based refinement scheme is designed to eliminate the scattered class predictions of significantly low values for higher efficiency. Extensive experiments demonstrate that the proposed De-biased Teacher consistently outperforms other state-of-the-art methods on the MS-COCO and PASCAL VOC benchmarks. Source codes are available at https://github.com/wkfdb/De-biased-Teracher.
A meta-analysis on the correlations between statistical learning, language, and reading outcomes.

The purpose of this meta-analytic review is to investigate the relation between statistical learning (SL) and language-related outcomes, and between SL and reading-related outcomes. A comprehensive search of peer-reviewed published research resulted in 42 articles with 53 independent samples and 201 reported effect sizes (Pearson's r). Results of our robust variance estimation correlated effects model revealed a significant, moderate relation between SL and language-related outcomes, r = .236, p < .001, and a significant, moderate relation between SL and reading-related outcomes, r = .239, p < .001. Moreover, age, the writing system of the language, and SL paradigm moderate the strength of the association between SL and reading. Age is the only significant moderator on the strength of the association between SL and language. The findings from this meta-analysis shed light on the contribution of multiple factors that impact how SL relates to language and reading outcomes, with important implications for developing effective instructional practices that emphasize statistical regularities of oral and written materials in the classroom. Theoretical implications of these findings for language and reading development are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
A novel framework of prescribed time/fixed time/finite time stochastic synchronization control of neural networks and its application in image encryption

In this paper, we investigate a novel framework for achieving prescribed-time (PAT), fixed-time (FXT) and finite-time (FNT) stochastic synchronization control of semi-Markov switching quaternion-valued neural networks (SMS-QVNNs), where the setting time (ST) of PAT/FXT/FNT stochastic synchronization control is effectively preassigned beforehand and estimated. Different from the existing frameworks of PAT/FXT/FNT control and PAT/FXT control (where PAT control is deeply dependent on FXT control, meaning that if the FXT control task is removed, it is impossible to implement the PAT control task), and different from the existing frameworks of PAT control (where a time-varying control gain such as μ(t)=T/(T−t) with t∈[0,T) was employed, leading to an unbounded control gain as t→T− from the initial time to prescribed time T), the investigated framework is only built on a control strategy, which can accomplish its three control tasks (PAT/FXT/FNT control), and the control gains are bounded even though time t tends to the prescribed time T. Four numerical examples and an application of image encryption/decryption are given to illustrate the feasibility of our proposed framework.
SSTSA: A Self-Supervised Topic Sentiment Analysis Using Semantic Similarity Measures and Transformers

The exponentially increasing amount of data generated by the public on social media platforms is a precious source of information. It can be used to find the topics and analyze the comments. Some researchers have extended the Latent Dirichlet Allocation (LDA) method by adding a sentiment layer to simultaneously find the topics and their related sentiments. However, most of these approaches do not achieve admirable accuracy in Topic Sentiment Analysis (TSA), particularly when there is insufficient training data or the texts are complex, ambiguous, and short. In this paper, a self-supervised novel approach called SSTSA is proposed for TSA that extracts the hidden topics and analyzes the total sentiment related to each topic. The SSTSA proposes a new method called Pseudo-label Generator. For this purpose, first, it employs semantic similarity and Word Mover’s Distance (WMD) measures. Then, the document embedding technique is employed to semantically estimate the sentiment orientation of samples and generate the pseudo-labels (positive or negative). Afterward, a hybrid classifier composed of a pre-trained Robustly Optimized BERT (RoBERTa) and a Long Short-Term Memory (LSTM) model is trained to predict the sentiment of unseen data. The evaluation results on different datasets of various domains demonstrate that the SSTSA outperforms similar unsupervised/self-supervised methods.
Triangle's Fraud Theory on Academic Fraud Behavior When online Learning

Purpose: This study aims to examine the dimensions of the fraud triangle to explain academic cheating behavior during online learning. Theoretical Framework: The theory that examines the causes of fraud is the fraud triangle theory. This theory was first put forward by Edwin Sutherland, who coined the term white-color crime, and Donald Cressey, who was a student of Sutherland's in a doctoral program in the 1940s and author of Other People Money: A Study in the Social Psychology of Embezzlement. Design/Methodology/Approach: This study uses multiple regression analysis with the number of samples of this study are 73 accounting students at the Indonesian Christian University in Maluku who have passed auditing courses and business and professional ethics. Data were collected using a survey method. Findings: The results of this study indicate that partial pressure and opportunity affect academic cheating, while rationalization does not affect academic cheating. This study also shows that simultaneously academic fraud is determined by the dimensions of the fraud triangle, namely pressure, opportunity, and rationalization. Research, Practical &amp; Social Implications: The implication of this research is to provide useful input to institutions, especially the UKIM Faculty of Economics and Business, to make online learning standards. Also, the teaching staff as input in the teaching and learning process pays attention that pressure, opportunity, and rationalization are important to determine student behavior in academic fraud. Originality/Value: The results of this research indicate that rationalization does not influence academic fraud behaviors. Rationalization is self-justification or the wrong reasons for wrong behavior. This is due to the awareness of the perpetrators of academic fraud that the fraud committed is an act that is not commendable, so guilt arises when committing academic fraud. It does not support the fraud triangle theory which states that rationalization encourages fraud.
A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital

Abstract Objective To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR). Materials and Methods Statistical classifiers were trained on feature representations derived from unstructured text in patient EHRs. We used a proxy dataset of patients with COVID-19 polymerase chain reaction (PCR) tests for training. We selected a model based on performance on our proxy dataset and applied it to instances without COVID-19 PCR tests. A physician reviewed a sample of these instances to validate the classifier. Results On the test split of the proxy dataset, our best classifier obtained 0.56 F1, 0.6 precision, and 0.52 recall scores for SARS-CoV2 positive cases. In an expert validation, the classifier correctly identified 97.6% (81/84) as COVID-19 positive and 97.8% (91/93) as not SARS-CoV2 positive. The classifier labeled an additional 960 cases as not having SARS-CoV2 lab tests in hospital, and only 177 of those cases had the ICD-10 code for COVID-19. Discussion Proxy dataset performance may be worse because these instances sometimes include discussion of pending lab tests. The most predictive features are meaningful and interpretable. The type of external test that was performed is rarely mentioned. Conclusion COVID-19 cases that had testing done outside of the hospital can be reliably detected from the text in EHRs. Training on a proxy dataset was a suitable method for developing a highly performant classifier without labor-intensive labeling efforts.
Using Experimental Models and Multitemporal Landsat-9 Images for Cadmium Concentration Mapping

Remote sensing technology and its integration with machine learning are examples of effective and low-cost acquisition, particularly in the field of environmental studies and earth sciences for optimal management. The purpose of this study is to accurately map the cadmium (Cd) concentration and introduce the most suitable regression model among different models, including support vector regression (SVR), partial least square regression (PLSR), and artificial neural networks (ANN). In this study, instead of using a single-date Landsat-9 image, multi-temporal images were used, and the extraction of suitable features to estimate the Cd concentration was selected as the input of the regression model. For this purpose, twenty Landsat-9 images and 100 soil samples were prepared from the Ust-Kamenogorsk region in northeastern Kazakhstan. The comparison of multi-temporal images and single-date images of Landsat-9 showed that multi-temporal images have a better performance for monitoring the concentration of heavy metals compared to single-date images. Among various features investigated in this study, the OB (original band) was found to be the most appropriate feature for grouping regressions. The results suggested that SVR model with OB as the input leads to the most accurate estimate of the Cd concentration in the area, which was in the range of 8 to 26 mg/kg.
A Three-Dimensional UCAV Path Planning Approach Based on Immune Plasma Algorithm

Abstract Path planning of an unmanned aerial vehicle (UAV) or its variant supported with weapon systems, also called unmanned combat aerial vehicle (UCAV), is one of the most crucial steps for the autonomous flight and requires compelling decisions being made by considering the existence of the enemy defence, obstacles, and battery or fuel capacity of the vehicle optimally before starting the flight. Immune plasma algorithm (IP algorithm or IPA), inspired by the convalescent plasma treatment which is a medical method being popularized again with the rise of new coronavirus or COVID-19, has been introduced recently as a new optimization technique and the promising performance of the mentioned algorithm validated through a set of numerical and engineering problems. In this study, the IP algorithm was specialized as a three-dimensional UCAV path planner for the first time. Its performance was investigated particularly by using three different battlefield environments and assigning various constants to the population size. Moreover, the effect of two IPA-specific control parameters that are responsible for determining the number of donors and number of receivers on the qualities of the calculated paths were analyzed in detail. The results obtained by the IPA were compared with the results of other three-dimensional path planners guiding optimization algorithms such as simulated annealing (SA), gray wolf optimizer (GWO) and symbiotic organism search (SOS). Experimental studies showed that the main idea lying behind the usage of qualified solutions as donors and transferring information directly from them to the receivers representing the poor solutions of the problem gives a positive contribution to the exploitation-dominant operations of IPA and fully complies with the challenging requirements of the path planning problem. Especially in the battlefields for which an optimal or near optimal path contains complex maneuvers, the advantages from the subtly balanced operational steps of the IPA become more apparent by setting the number of donors and receivers appropriately and IPA is found to be more stable and successful compared to the other tested algorithms.
NEURAL NETWORK SIGNAL PROCESSING: THE TASKS WITHOUT “DEEP LEARNING”

The purpose of the study is to evaluate the possibility of using artificial neural networks for electrical signals processing.&#x0D; &#x0D; Methods. The application of a multilayer perceptron for these purposes as the simplest neural feed forward network is considered. Its difference as the basis of neural network algorithms is that when analyzing dynamic processes, signal processing should be carried out in a “sliding time window”.&#x0D; &#x0D; Results. It is shown that neural network processing makes it possible to approximate the shape of the signal with high accuracy and determine its parameters in real time. Using the example of periodic signals and transients in electrical circuits, accuracy assessments are made and the features of neural network processing are analyzed. The necessary sizes of the training sample of signals and the level of errors that occur when testing a neural network are discussed. Estimates of the required signal sampling frequency, the “sliding window” duration and the variation range of signal parameters when creating a training sample are given.&#x0D; &#x0D; Conclusions. It is shown that the proposed approach does not require “deep learning” of neural networks with complex architecture. It enables to create a signals training sample based on simple analytical formulas and to control the neural network algorithm quality at intermediate stages of calculations.
Accurate Nonlinearity and Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Data Generation

Piezoresistive pressure sensors exhibit inherent nonlinearity and sensitivity to ambient temperature, requiring multidimensional compensation to achieve accurate measurements. However, recent studies on software compensation mainly focused on developing advanced and intricate algorithms while neglecting the importance of calibration data and the limitation of computing resources. This paper aims to present a novel compensation method which generates more data by learning the calibration process of pressure sensors and uses a larger dataset instead of more complex models to improve the compensation effect. This method is performed by the proposed aquila optimizer optimized mixed polynomial kernel extreme learning machine (AO-MPKELM) algorithm. We conducted a detailed calibration experiment to assess the quality of the generated data and evaluate the performance of the proposed method through ablation analysis. The results demonstrate a high level of consistency between the generated and real data, with a maximum voltage deviation of only 0.71 millivolts. When using a bilinear interpolation algorithm for compensation, extra generated data can help reduce measurement errors by 78.95%, ultimately achieving 0.03% full-scale (FS) accuracy. These findings prove the proposed method is valid for high-accuracy measurements and has superior engineering applicability.
One-Shot Unsupervised Cross-Domain Person Re-Identification

Cross-domain person re-identification is challenging due to the notorious domain shift problem. Most of the existing unsupervised cross-domain person ReID methods require a large number of unlabeled target-domain samples for adaptation. However, large scale of training data are not always available due to public privacy. Domain generalization methods have inferior adaptation ability without seeing any target domain data. Inspired by the few-shot learning capability of human vision system, we propose a novel setting, one-shot unsupervised cross-domain for person ReID and study the ability of adaptation using the minimum number of image in the target domain during training. Specifically, we first propose a novel Group Normalization (GN) based domain generalizable ReID model. We show that the GN based model could strike a better balance between model discrimination and generalization ability, compared with the Batch Normalization (BN) and Instance Normalization (IN) counterparts, and is more suitable for domain generalizable ReID baseline model. Then besides the supervised feature learning task in the source domain, we introduce two self-supervised learning tasks using the one-shot target domain data to further improve the generalization ability of the ReID model. We carefully design model architecture and perform model training to reduce overfitting to the one-shot target domain. Extensive experiments demonstrate the effectiveness of our approach for one-shot unsupervised cross-domain ReID. Our approach can be extended to few-shot setting and increasing the number of shot up to 1,000 images can steadily increase the performance, which provides practical values to the community.
A Lightweight Machine Learning-Based Email Spam Detection Model Using Word Frequency Pattern

This Spam emails have become a severe challenge that irritates and consumes recipients' time. On the one hand, existing spam detection techniques have low detection rates and cannot tolerate high-dimensional data. Moreover, due to the machine learning algorithm's effectiveness in identifying mail as solicited or unsolicited, their approaches have become common in spam detection systems. This paper proposes a lightweight machine learning-based spam detection model based on Random Forest (RF) algorithm. According to the empirical results, the proposed model achieved a 97% accuracy on the spambase dataset. Furthermore, the performance of the proposed model was evaluated using standard classification metrics such as Fscore, Recall, Precision, and Accuracy. The comparison of Our model with state-of-the-art works investigated in this paper showed the model performs better, with an improvement of 6% for all metrics.
A call for better validation of opioid overdose risk algorithms

Abstract Clinical decision support (CDS) systems powered by predictive models have the potential to improve the accuracy and efficiency of clinical decision-making. However, without sufficient validation, these systems have the potential to mislead clinicians and harm patients. This is especially true for CDS systems used by opioid prescribers and dispensers, where a flawed prediction can directly harm patients. To prevent these harms, regulators and researchers have proposed guidance for validating predictive models and CDS systems. However, this guidance is not universally followed and is not required by law. We call on CDS developers, deployers, and users to hold these systems to higher standards of clinical and technical validation. We provide a case study on two CDS systems deployed on a national scale in the United States for predicting a patient’s risk of adverse opioid-related events: the Stratification Tool for Opioid Risk Mitigation (STORM), used by the Veterans Health Administration, and NarxCare, a commercial system.
<b>faaShark</b>: an End-to-End Network Traffic Analysis System atop Serverless Computing

The prosperity of the Internet has made network traffic analysis increasingly indispensable in network operation. With the development of machine learning, more researchers and engineers are using deep learning models for network traffic analysis. However, the rapidly growing data size and model complexity make resource scheduling a serious limitation, which is why cloud computing services are typically required for network analysis. To leverage the advantages of serverless platforms, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">faaShark</b> , an end-to-end network traffic analysis system based on a serverless computing platform. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">faaShark</b> adapts distributed training to fully utilize the flexibility of serverless platforms. Additionally, we design a cold start optimization algorithm to reduce the hit rate of cold start when serving pretrained models to handle network analysis requests. Our extensive experiments evaluate the impact of several parameters of distributed training and confirm the effectiveness of our cold start optimization algorithm when building such a network analysis system atop serverless computing frameworks.
A Hybrid Approach of Dijkstra’s Algorithm and A* Search, with an Optional Adaptive Threshold Heuristic

This study is a part of the trajectory planning applied to harvest system work where mobile robots must be able to navigate safely the environment to look for palmer crops. Many constraints can be faced, such as crop selection as maturity changes over time, searching for the most mature palmer, avoiding different kinds of obstacles, robot speed control, and the cost of moving from an initial point to a goal target. After studying different trajectory planning approaches and their applications [8], we conclude that some of these methods can be combined to design a new, powerful approach based on the accurate property of Dijkstra and the heuristic function of A Star.Dijkstra is known as a powerful algorithm based on graph mapping and reducing the path cost, and A Star on the other side is one of the best guides for path searching due to the heuristic function that avoids exploring all environment nodes and only those leading to the goal. Combining Dijkstra’s algorithm and the A* (A-star) algorithm can lead to a more efficient pathfinding approach. Dijkstra’s algorithm [4] is a well-known method for finding the shortest path between two nodes in a graph, while the A* algorithm is an extension of Dijkstra’s algorithm that uses heuristic estimates to guide the search towards the goal node. By combining these two algorithms, we can use Dijkstra’s algorithm to explore the graph and generate a good initial estimate of the path cost, then use the A* algorithm to refine the estimate and guide the search towards the goal node. This paper explores the utilization of trajectory planning in a harvesting system. By employing both the Dijkstra and A* algorithms, we propose a hybrid approach to ensure optimal timing for finding a path. We conduct a comparative analysis to evaluate the performance of the new approach by comparing the application of a single algorithm versus the hybrid approach across various graph sizes.