Reviewerly/exHarmony huggingface dataset:
Logistic Regression Based on Faure Sequence Initialized Fruit Fly Optimization Algorithm

As a regression algorithm, logistic regression can be used for not only regression prediction, but also classification. By employing Sigmod function of nonlinear transformation to obtain a value within (0, 1), it can be given the characteristic of probability in logistic regression. According to the rule “value is 1 when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p &gt; 0.5$</tex> , and value is 0 when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p &lt; 0.5$</tex> ”, logistic regression can be recognized as a 2-class classification problem and is highly explanatory. However, in the process of solving the parameters of the loss function of traditional logistic regression, gradient descent (GD) method is often used, which means that there is a requirement for the convexity of the function, and it is necessary to reasonably control the size of the hyperparameter in GD, otherwise it is easy to fall into the local optimum value and cannot obtain the global optimal solution. As new methods need to be found to solve the problem of loss function optimization, in this paper, a Faure sequence initialized fruit fly optimization algorithm (FSFFOA) method has been proposed to replace GD method to complete the optimization process. As a swarm intelligence algorithm, FSFFOA has many parameters that can be used to adjust the algorithm accuracy and speed, and has good performance in solving nonlinear optimization problems. Via numerical experiment, the effectiveness of FSFFA has been proved.
Research On Overload Monitoring Technology For Bridges Based On Fast DIC And Deep Learning

Combined with a deep learning-based overloaded vehicle information capture and recognition method, the captured information from bridge surface images is used to establish a mapping relationship between real-time monitoring data of mid-span deflection and spatiotemporal data of passing vehicles, realizing the collection and analysis of overloaded vehicle data during bridge operation. Experimental results show that the bridge overloading monitoring method based on fast DIC and deep learning has achieved ideal results in the bridge overloading monitoring scene, and can provide data basis for road and bridge maintenance management work, with certain engineering application value.
Establishing trust in ‘Digital Cinema Initiative’ Compliant Devices using Permissioned Blockchain

The paper addresses the challenges associated with Key Delivery Message (KDM) generation in the digital cinema industry, which aims to enhance security standards and reduce piracy. Generating KDM involved verifying whether digital cinema devices are compliant with the Digital Cinema Initiative (DCI). This paper explains the concepts of encrypted Digital Cinema Packages (DCPs), DCI, Trusted Device List (TDL), digital certificates, KDMs, and Distributor Key Delivery Messages (DKDMs). The proposed platform leverages the advantages of decentralized databases, transparency, and immutability to ensure the authenticity of device certificates on the blockchain network. This approach simplified the process of generating a TDL for the distributor, improved flexibility, and efficiently managed time constraints while maintaining the same high-security standards as traditional trust models. The proposed model creates a traceable device history for buyers, enhancing operational efficiency, traceability, and security while reducing piracy. The proposed platform is a practical and efficient solution that can be implemented in real-world scenarios and can serve as a reference for other industries facing similar challenges.
Pengaruh Kompetensi Dosen, Motivasi Berprestasi, dan Penerapan Kurikulum terhadap Prestasi Belajar Mahasiswa (Suatu Kajian Studi Literatur Manajemen Pendidikan)

Previous research and relevant research are very useful for a research or literature review of a scientific work both examining the influence between variables and other factors that also influence a variable itself. This article discusses a literature review of factors that have an impact on student learning achievement, namely lecturer competence, achievement motivation, and application of curriculum. this review intends to determine hypotheses that have an impact on each variable so that it can be useful for further research. The results of this writing make it clear if lecturer competence has a positive or crucial impact on student learning achievement; achievement motivation has a positive and crucial impact on student learning achievement; implementation of the curriculum has a positive and crucial impact on student learning achievement; lecturer competence, achievement motivation, and curriculum Implementation simultaneously have a positive and meaningful impact on student learning achievement.
Web-Based Optimization and Expansion of Multimodal High School Entrance Examination Data Visualization

The reform of the new college entrance examination system makes students and their parents pay more attention to the feasibility and accuracy of the college entrance examination. The college entrance examination informatization can help the local admissions departments to mine and analyze the data rules from the massive college entrance examination data, and provide help and guidance for each link of volunteer application. Therefore, this paper, in order to comply with the development trend of the new college entrance examination information construction, proposes the optimization method of data visualization chart rendering and innovative combination of ideas to improve the Web rendering performance in order to enhance the interactive experience of users and the platform.
Knowledge Graph Construction Method for Mobile IOT Management Platform Device Anomaly

Mobile IOT management platform equipment abnormal information contains a large amount of data knowledge, and it provides decision support for intelligent operation and maintenance of power communication equipment. However, such data are mostly stored as unstructured data and cannot be directly extracted for inferential decision making. In this paper, with the advantage of knowledge graph in data storage and knowledge extraction, we propose Multi-feature entity relationship joint extraction methods (MFERJE), which firstly generates word vectors by using ALBERT word embedding model, and then realizes word vectors by using BiLSTM structure and attention mechanism to achieve multi-feature entity recognition, and finally discriminate multi-type entity relationship based on graphical matrix to obtain the entities and relationships contained in abnormal data, using location, entity label and lexicality as feature inputs to greatly reduce the loss of semantic information features, achieve effective utilization of equipment abnormal data, and achieve equipment abnormal information based on the knowledge of equipment abnormal fault alarm visualization of equipment abnormal information based on equipment abnormal fault warning knowledge. The experimental results show that the F1 value of the model reaches 0.78, the accuracy is improved by 4% compared with other traditional methods, and the knowledge extraction effect is better, which can effectively improve the efficiency of equipment maintenance, realize the accurate positioning of equipment abnormalities and the rapid retrieval of corresponding maintenance strategies, and promote the intelligent operation and maintenance of mobile IOT management platform.
Face Mask Recognition and Normative Detection Algorithm Based on Attention Mechanism Fusion and Image Filtering

Under the current normalized management of the epidemic situation, due to the dense population in public places and small detection targets, it is difficult for manual supervision to play a certain role. At the same time, the existing recognition algorithms still have certain deficiencies in whether masks are worn or not. Face mask recognition based on YOLOv5s and fusion of multi-scale attention features and wearing standard detection algorithm using HSV and YCrCb image filtering. The experimental results show that after the introduction of the CBAM attention module, the mAP reaches 73.76%, which is an increase of 1.69%. The skin color of the mouth and nose area of the face is extracted, and the accuracy of mask wearing standard detection reaches 84.93%, which meets the needs of practical applications.
Improved K-Prototype Clustering Algorithm for Plant-Side of Power Monitoring System

On the plant side of the power monitoring system, in order to achieve non-destructive and lightweight vulnerability inventory thus proposing cluster analysis, the target assets are clustered, and the impact on the plant-side hosts during vulnerability inventory is greatly reduced through the optimization of the scanning strategy. The asset information collected by the scan is processed to obtain a hybrid dataset. Using the improved k-prototypes clustering algorithm to cluster the target assets, and according to the clusters of hosts with different functions obtained by clustering, an optimized scanning strategy is adopted to change the scanning behavior and reduce the impact caused to the target network. The improvement of the clustering algorithm is mainly reflected in the initial category center selection and distance calculation. For the initial category center selection, the phase dissimilarity measure is adopted for the initial category center selection; in the distance calculation, the entropy weight value is introduced to change the traditional method of randomly determining the attribute weights, which avoids the local optimum and improves the stability of the algorithm.
A New Multimodal Video Detection Model and Dataset

In this work, we discuss the issue of multimodal detection of long and short video violations on Internet environment. We collect and annotate a dataset consisting of 6000 videos from different platforms, and offer a joint model of video frame sequences and text sequences to analyze video and text information for violation detection. We also do a comparison with the unimodal model. As far as we know, this is the first multimodal dataset proposed for the detection of violations involving “pornography and violence”. And the model adapts to the conversion of video unimodal data to multimodal data. Experiments found that although text unimodal recognition results are limited, the complementarity of the modalities in the multimodal case gives a large improvement in the results. We discuss the reasons for the results and open the codes and dataset in this domain for further study. The address is https://github.com/weijingxuan/multivideo.
Comparison of Term Weighting Methods in Sentiment Analysis of the New State Capital of Indonesia with the SVM Method

The relocation of the State Capital to â€œNusantaraâ€, which was inaugurated with the enactment of UU No. 3 of 2022, is a significant project that has sparked polemics among Indonesian citizens. Many people expressed their opinions and thoughts regarding the relocation of the State Capital on Twitter. This tendency of public opinion needs to be identified with sentiment analysis. In sentiment analysis, term weighting is an essential component to obtain optimal accuracy. Various people are trying to modify the existing term weighting to increase the performance and accuracy of the model. One of them is icf-based or tf-bin.icf, which combines inverse category frequency (ICF) and relevance frequency (RF). This study compares the tf-idf, tf-rf, and tf-bin.icf term weighting with the SVM classification method on the new State Capital of Indonesia topic. The tf-idf weighting results are still the best compared to the tf-bin.icf and tf-rf term weights, with an accuracy score of 88.0% a 1,3% difference with tf-bin.icf term weighting.
Steganography Tools and Their Analysis Concerning Distortion in Stego Image

In the research work, the examination of concealing algorithms, extraction algorithms, carrier multimedia file, and other security parameters has been done for two steganography tools. From the experimental results, we strongly recommend that many of the open-source tools are not safe for embedding personal information as it can be detected with the help of the steganalysis tools that exist in the market. In this research work, stego file is analyzed based on distortion induced concerning the position of bit used for embedding, distortion pattern using histogram, spectrum analysis, similarity index, and entropy. Quantitative values of mentioned parameters are calculated in Python and emphasize the unfortunate present state of the art of realistic steganography tools. At present, there is a lack of publically available secure steganography tools.
An Analysis of Random Non-deterministic Signals Using Recursive Methods and Stochastic Control System

In order to simplify the analysis of processes as well as the design of controllers, discrete domain analysis is a powerful tool since it reduces characteristic roots from infinite to finite numbers. Our work focuses on the process of introducing discretization to the domain conversion, as well as considering a recursive method for analyzing random non-deterministic data. With the proper discretization of tuned stable systems, nonlinear modeling of a system can be designed. Several simulations are used in this paper to demonstrate the application of non-deterministic, stochastic control theory. A method for controlling random non-deterministic signals based on sampled data has also been developed, and the time complexity has been analyzed.
Object Detection Under Low-Lighting Conditions Using Deep Learning Architectures: A Comparative Study

Detecting objects present in low-lighting conditions have always been a challenging task. Reviewing enhancement techniques for low-light object detection is crucial in research applications like surveillance and automated driving. The first task is to choose a benchmark dataset unlike other datasets with a low number of images in dark lighting. The ExDark dataset is one such dataset that exclusively contains instances in low light. Equisampling followed by comparing the results using cutting-edge object detection pipelines with enhancement techniques is the primary task of the present work. The baseline results for the complete dataset are available, but the dataset is skewed. In this paper, we analyze the methodologies that lead to the improvement in the performance of the deep learning-based object detection algorithms. The methodology is based on the enhancement of the input images with low-light intensity before performing object detection. We are also formulating a new method to obtain better bounding boxes predictions for object detection using RCNN. The present work aids the research community in low-light object detection tasks.
Automatic Speech Recognition Using Acoustic Modeling

We propose to implement traditional ASR to speech recognition within the framework of the deep speech model. The ASR model should be able to detect Indian English speakers and transform their speech into text and compare the accuracy of speech to text between the pre-trained model and our trained model. We extract features from audio signals, and by using an acoustic model, we produce words. The accent is the basic pattern of acoustic features and pronunciation. It can tell you about a person’s social and linguistic history. It is a significant source of inter- and intra-speaker variation. To improve speech recognition accuracy, an accent-dependent vocabulary or model might be utilized and can be used to improve the accuracy of speech recognition systems. We present an experimental approach of acoustic. As a result, we may transfer the pre-trained model’s learnings to different accents, allowing the model to learn diverse accents on its own.
A Transfer-Learning-Based Deep Network for Detecting Violence in Real-Time Videos

The detection of crowd behavior is critical for public safety and overall monitoring. A smart AI-based surveillance system is therefore necessary to monitor and detect crowd behavior, which is gaining increased usage in both government and private organizations. In this chapter, we proposed a deep learning-based technique for detecting agitated and nonagitated scenarios in real-time videos. A “transfer-learning” based Inception-V3 model is employed here as deep network. To fine tune Inception-V3 for the categorization of agitated and nonagitated videos, we have added a fully connected layer with “softmax” activation function to the model having two neurons. The proposed model’s performance was assessed on three standard databases, namely Hockey Fight, Violent-Flows and Movie, concerning average accuracy measure. The proposed model achieves 97.7%, 95.43% and 98.51% classification accuracy for the aforementioned datasets, which is significantly better than other related state-of-the-art methods.
A Survey on LSB Replacement-Based Statistical Image Steganalysis Techniques

Image steganalysis is comparatively newer concept than steganography. The latter hides and former unfold. Image steganography leaves hidden trails in the stego image like the change in the first or higher order statistical properties. Statistical steganalysis discovers such features whose alteration can be computed and is significant to unfold or break the steganography behind. This paper studies four widely accepted statistical steganalytic methods—Chi-square, RS analysis, sample pair analysis (SPA) and least square (LS) to attack LSB replacement steganography and compares their performances as found in the literature.
Equality Does Not Make You Happy: Effects of Differentiated Leader-Member Exchange and Team-Member Exchange on Developer Satisfaction in Agile Development Teams

Prior work on leadership in information systems development (ISD) teams has assumed that all developers are treated equally by their team leader and ignored the possibility that differentiated leader-member exchange (LMX) may be an important instrument for team leaders to influence self-organizing, agile ISD teams. We conducted a concurrent mixed methods inquiry to understand how LMX differentiation is associated with developer satisfaction in agile ISD teams and through which team processes agile ISD teams address LMX differentiation. We ran a multilevel, multistage survey of 1,894 software developers in 217 teams and an embedded case study of five ISD teams drawing on qualitative data from 40 interviews of developers and team leaders. Two focus groups (one with 10 developers and one with 10 team leaders) helped to substantiate the meta-inferences from the quantitative and qualitative studies. The results showed that LMX differentiation was positively associated with developer satisfaction, especially in teams with high-quality team-member exchange (TMX). We identify three team processes (i.e., collectivization of resources, visible appreciation of privileges, and freeing up leader capacities) that are enacted through agile ISD practices and allow ISD teams to leverage benefits from LMX differentiation for all their members.
The Positive Politeness Strategies Used in “English for Nusantara” Textbook

&lt;em&gt;The integration of pragmatic competence and politeness is an essential focus in the development of English learning materials like textbooks. Using appropriate textbooks can help students develop communicative competence as the primary goal of English language teaching as a foreign language. This study discusses the positive politeness strategy in the textbook "English for Nusantara". The present study used a qualitative approach with a documentary analysis design. The data were collected by analyzing the dialogues contained in the textbook. The theory of positive politeness strategy, developed by Brown and Levinson (1987), was used to examine the data. The findings revealed that there are eight positive politeness strategies used in the dialogues of the English for Nusantara textbook, namely (1) exaggerating one’s interest in, approval of, and sympathy of the listener, (2) employing in-group markers of identity, (3) requesting agreement, (4) requesting &amp;amp; promising, (5) involving the speaker and the listener in action and (6) offering or asking for an explanation, (7) using inclusive “we” form, and (8) giving (or asking for) reasons. The most dominant positive politeness strategy was ‘exaggerating interest in, approval of or sympathy with the hearers.’ EFL learners’ pragmatic competence will be increased when pragmatic contents, like politeness, are integrated into the textbook as English learning materials used by teachers in the classroom.&lt;/em&gt;
A Radiotherapy Positioning Method for Both Coarse Guidance and Precise Verification Based on Integration of AR and Optical Surface Imaging

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. We proposed a noninvasive radiotherapy positioning method integrating augmented reality (AR) and optical surface, and evaluated its feasibility in clinical workflow.AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, i) the AR-based coarse guidance. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. ii) optical surface-based precise verification. The point clouds were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in 6 degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, i) quality assurance workflow, ii) errors of both steps and correlation analysis, and iii) receiver operating characteristic (ROC).The maximum errors in phantom evaluation were 3.4±2.5 mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9 mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.3 vs. 1.7±1.1, p<0.01), Lng (2.4±1.3 vs. 1.4±0.1, p<0.01) and Rtn (0.8±0.5 vs. 0.6±0.4, p = 0.03) while relatively similar in Lat (1.7±1.0 vs. 1.9±1.1, p = 0.13).The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.
Research and Application Based on CNN-LSTM

In this paper, a deep learning network structure based on convolutional neural network and long short-term memory (LSTM) neural network is proposed. Combining 18 independent variables of known datasets, correlation analysis is performed, and after variable analysis, feature fusion method is used to extract shallow features and deep features and connect them using convolutional network, and the obtained vector information is input into LSTM units by convolutional fusion of features, and the results of each network are weighted and fused. The CNN-LSTM model established in this paper performs prediction and finally obtains a goodness-of-fit of 0.9381, which is better than the other models compared, and the experimental results show that the model can effectively improve recognition accuracy.