Reviewerly/exHarmony huggingface dataset:
AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM

Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.
Performance Comparison of Predictive Methodologies for Carbon Emission Credit Price in the Korea Emission Trading System

Research related to the carbon-emission credit-price prediction model has only considered the effects of specific indicators, such as coal and oil prices, and only long-term prediction studies have been conducted. Recently, carbon emission credits have been recognized as investment assets, such as stocks and real estate. Accordingly, a carbon-emission credit prediction method is needed to establish an industrial strategy with low risk. In this study, an attempt was made to model the behavior of market participants in the time series model by analyzing the correlation between the search query volume data and the Korean Allowance Unit (KAU). Multiple Linear Regression Analysis (MRA) and Auto-Regressive Integrated Moving Average models were developed. In all price prediction models, the error of the prediction model at the 4th time was low. In the case of MRA, the error in the predicted near future price was small, but the error rate increased with increasing analysis period and prediction time. The error rate of ARIMA was lower than that of MRA, but it did not show a rapid change. These research findings will be beneficial to investigating and finding more rigid and reliable methodologies that can be used to predict various important values in similar fields in the future.
Prediction of the Development Scale of Vocational Education Using Markov Algorithm and Countermeasures

HVE (Higher Vocal Education) in China is developing rapidly. With the expansion of HVE enrollment, the scale has also expanded rapidly. However, with the great development of HVE, HVE also faces many problems. In this paper, the M_BPNN (Markov neural network-BP) model is constructed for the prediction of the development scale of vocational education. Using BPNN’s powerful nonlinear mapping ability and error correction thought, the data information of the future development scale of vocational education is predicted. The results show that the prediction accuracy of the M_BPNN model is the best, and MSE (mean squared error) and MRE (mean relative error) are 10.184 and 5.017, respectively, which are lower than the other two prediction models. It shows that the prediction effect of the M_BPNN model is better than that of the pure Markov model. The forecast results show that the population of school age in H province will decrease from 3.36 million in 2020 at an average annual rate of nearly 700,000 to the lowest value of 2.06 million in 2022. After that, the population of school age will increase steadily. The result shows that there is a relative shortage of regional students, and the enrollment scale is developing well, but it is still not optimistic. It is necessary to coordinate the cross-regional development.
Contributions to reducing online gender harassment: Social re-norming and appealing to empathy as tried-and-failed techniques

Inspired by similar methods shown to be effective in reducing online racist harassment, we designed two tweets aimed at reducing online gender harassment. Our interventions were based on the principles of social re-norming and appealing to harassers’ empathy. In a sample of 666 Twitter users engaging in sexist or misogynist tweeting, we found that our intervention tweets did not reduce the number of sexist slurs or sexist users, either 7 days or 31 days after being sent. Our attempts also affected neither the valence nor the arousal of subsequent tweets posted by our sample of Twitter users. We discuss the conceptual, methodological, and ethical challenges associated with activist research aimed at reducing online gender harassment and discuss some of the implications of our attempts to do so.
Teaching Methods and Practices of University English Classroom Based on Big Data

Traditional university English classroom teaching has undergone radical changes in the context of big data, with new trends in the teaching mode of teachers, the learning mode of students, and the interaction mode of teachers and students. In order to keep up with the development of society, university English cannot remain stagnant and cannot continue to adopt traditional teaching and learning methods. In order to keep up with the development of society and meet its requirements, university English courses should be combined with big data technology to improve the quality of university English. Classroom engagement can attract students’ attention and motivate them to learn, thus improving the quality of university English teaching. This paper elaborates on the changes in university English teaching methods in the context of big data from the changes and role of the English classroom and investigates and analyzes 228 students’ evaluations of the English classroom in our university to further propose ways to improve the teaching methods in the university English classroom. Strategies. This paper discusses how the application of big data technology can assist university English classroom teaching, providing an effective reference for the more efficient use of information technology to help university English classroom teaching in the future and enriching the theoretical basis for university English classroom teaching.
Automatic False Alarm Detection Based on XAI and Reliability Analysis

Many studies attempt to apply artificial intelligence (AI) to cyber security to effectively cope with the increasing number of cyber threats. However, there is a black box problem such that it is difficult to understand the basis for AI prediction. False alarms for malware or cyberattacks can cause serious side effects. Due to this limitation, all AI predictions must be confirmed by an expert, which is a considerable obstacle to AI expansion. Compared to the increasing number of cyberattack alerts, the number of alerts that can be analyzed by experts is limited. This paper provides explainability through an interpretation of AI prediction results and a reliability analysis of AI predictions based on explainable artificial intelligence (XAI). In addition, we propose a method for screening high-quality data that can efficiently detect false predictions based on reliability indicators. Through this, even a small security team can quickly respond to false predictions. To validate the proposed method, experiments were conducted using the IDS dataset and the malware dataset. AI errors were detected better than they could be by the existing AI models, with about 262% in the IDS dataset and 127% in the malware dataset from the top 10% of analysis targets. Therefore, the ability to respond to cyberattacks can be improved using the proposed method.
Significance of Prosody Modification in Privacy Preservation on speaker verification

Privacy is the major concern that comes to the user's mind before sharing their data. There are various methods proposed in literature for providing privacy to speech data. Previous works that have been done to protect the speaker identity were done for speech applications like automatic speech recognition (ASR), speech analysis, etc. For these applications the presence of speaker identity is not essential while processing. The objective of this work is to provide privacy to the task in which presence of speaker identity is essential at the time of processing. In this work, privacy is provided to the speaker identity information present in speech signals while performing automatic speaker verification (ASV) tasks. In order to achieve the same, this work proposes a prosody modification based approach. The proposed approach is able to conceal the speaker identity from human perception by changing the pitch of the speech utterances with a pitch modification factor of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha\geq 1$</tex> But at the same time the ASV system provides consistent performance irrespective of the change in pitch (i.e. for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha\geq 1)$</tex> . The same evidence has been shown through experiments in TIMIT and IITG-MV databases. A subjective study has also performed to verify the extent of speaker anonymization with respect to humans. The subjective study evaluates the performance in terms of mean opinion score (MOS). The observed MOS signifies the ability of the proposed approach to conceal the speaker's identity.
Emulation as a Service (EaaS): A Plug-n-Play Framework for Benchmarking Network Analytics

Real-time data generation and collection to analyse the network performance is difficult for large-scale networks having limited accessibility. In this paper we propose a framework which can provide realistic si/e-mulations, and generate synthetic data closer to real-time data that replaces the traditionally used deterministic and probabilistic models. This framework uses an emulation based platform to replicate real network scenarios. The emulator acts as a base layer with necessary APIs to enable customized inclusion of analytics services in a plug-and-play manner through the framework. This framework can be used to acquire data required for different Machine Learning (ML) models in order to reduce costly and time-consuming data collection effort in network analytics.
Visualizing Source-Code Evolution for Understanding Class-Wide Programming Processes

The COVID-19 pandemic has led to an increase in online classes, and programming classes are no exception. In such a learning environment, understanding every student’s programming process is mostly impractical for teachers, despite its significance in supporting students. Giving teachers feedback on programming processes is a typical approach to the problem. However, few studies have focused on visual representations of the evolution process of source-code contents; it remains unclear what visual representation would be effective to this end and how teachers value such feedback. We propose two feedback tools for teachers. These tools visualize the temporal evolution of source-code contents at different granularities. An experiment was conducted in which several university teachers performed a user evaluation of the tools, particularly with regard to their usefulness for reviewing past programming classes taught by another teacher. Questionnaire results showed that these tools are helpful for understanding programming processes. The tools were also found to be complementary, with different aspects being highly evaluated. We successfully presented concrete visual representations of programming processes as well as their relative strengths and weaknesses for reviewing classes; this contribution may serve as a basis for future real-time use of these tools in class.
Methodological trauma in the socio-humanitarian sciences

The article raises the issue of the state of logic and methodology of socio-humanitarian researches in modern Ukraine. It is diversely argued the canonical requirement to return the preparation of scientific research projects and subsequent publication of their results and conclusions to generation in them such a conceptual structure (writing an introduction), which necessarily contains a normative formulation of the scientific problem in the form of a theoretical question concerning cognitive contradictions resulting from the existing practices of obtaining new rational knowledge in accordance with the three types of epistemological matrix of sociological cognition, which determine one of the research positions – to be an observer, interpreter or activist. Emphasis is placed on the tendency of reducing the demands concerning mandatory presentation of the methodological basis of the study in its program, reports, publications on the results obtained, in particular concerning the normative design of articles published in Ukraine’s professional journals in sociology. Examples of violations of this norm are given in articles published during 2015 and 2016 in two leading domestic periodicals of sociological orientation. It is noted that the claims of some authors to the theoretical significance of their publications are not justified in the absence of a scientific problem statement, the formulation of which must include a number of issues of theoretical content. In addition, it is underlined that the current situation in Ukrainian sociology can be explained by the so-called methodological trauma, which is also typical for researchers from other post-Soviet countries. The conclusion is formulated that the development of sociological science in its theoretical and empirical contexts is possible only if the methodological trauma experienced by sociologists, psychologists and other representatives of socionomic research role is overcome.
PLSSVM—Parallel Least Squares Support Vector Machine

<h2>Abstract</h2> Support Vector Machines are used in supervised learning. For large dense data sets, however, even optimized implementations like LIBSVM or ThunderSVM do not scale well on massively parallel hardware: They are algorithmically based on Sequential Minimal Optimization, and we are not aware of a performance portable implementation supporting CPUs and GPUs from different vendors. Our Parallel Least Squares Support Vector Machine (PLSSVM) solves both of these issues. First, PLSSVM resorts to the least squares formulation, and thus to an algorithm that is well-suited for massive parallelism. Second, PLSSVM provides a hardware-independent efficient implementation using OpenMP, CUDA, HIP, OpenCL, and SYCL.
Context-aware Trust Metrics for non critical IoT Applications: an Intrinsic Data Quality approach

Trust mechanisms are a promising solution to the security and reliability dilemma of city scale (smart city) IoT sensor deployments in non-critical scenarios. These deployments often employ low-cost sensor networks, where strict security policies are difficult to enforce, and whose IoT devices may exhibit incoherent sensing behavior. Because of the diverse disturbances that may affect the sensing data path, the integrity of the measurements has to be assessed by the data-consuming application, so that possibly misbehaving sensors and/or biased data can be identified. This paper proposes a trust model which assesses the trustworthiness of data collected from IoT sensors deployed at a city scale. The model is based on a set of context-aware trust metrics that leverage proximity information and context signatures. This intrinsic data-quality approach has the potentials to overcome the limitations of consensus-based metrics, which cannot easily detect anomalies affecting several neighbor sensors. Our new metrics are being validated over a real data-set coming from a wide LoRaWAN-based noise sensor deployment in a urban area.
CACP: Classification Algorithms Comparison Pipeline

<h2>Abstract</h2> This paper presents a Classification Algorithms Comparison Pipeline (CACP) for comparing newly developed classification algorithms in Python with other commonly used classifiers to evaluate classification performance, reproducibility, and statistical reliability. CACP simplifies the entire classifier evaluation process. Code examples and output results are explained and detailed to provide the basics of the module and give an overview of its capabilities. The code is available on GitHub under the MIT license.
Analysis of the Rare Earth Mineral Resources Reserve System and Model Construction Based on Regional Development

China is a large rare earth country that has pushed for related rare earth research, development, and application in the global development and progress of rare earths. The rare earth resource reserve strategy must be implemented by China due to the situation of rare earth resources at home and abroad, national security, and the need to strengthen the right to speak in the international market. This article builds the rare earth mineral resources reserve system and model from the perspective of regional development and uses the improved SURF algorithm to solve the problems of inaccurate mine location, mine location deviation, dislocation, overlap, and other issues, resulting in more accurate mineral resources reserve management data. The results show that the maximum relative error between the parallel profile method and the traditional method is 2.6%, which meets the requirement for mineral reserve calculation accuracy and can be used to calculate reserves. China's peak ionic rare earth output will be 46,797.06 tonnes in 2024, and then, it will decline at a 4% annual rate thereafter. This demonstrates how a graded reserve and orderly promotion can improve the workflow and efficiency of the rare earth mineral resources reserve.
Robust Watermarking Method to Secure Medical Images Applied to Ehealth

This paper propose a new robust watermarking scheme for medical images using a hybrid combination of the three methods, HD (hessenberg Decomposition) and SVD (Singular value decomposition) and an original crypto system based on the Arnold Transform (AT) of Singular Value Decomposition (SVD). In order to spread the robustness of our algorithm and provide extra security, an improved SVD-AT embedding and extraction procedure has been used to scramble the watermark logo in the preprocessing step of the proposed method. In the process of watermark embedding, an R-level discrete wavelet transform was applied to the host image. The low frequency wavelet coefficients are selected to carry this scrambled-watermark. The proposed watermarking method endures entirety attacks and rightly extracts the hidden watermark without significant degradation in the image quality, thus, when the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) performance of the proposed algorithm is performed.
Risk assessment of ICU patients through deep learning technique: A big data approach

Intensive Care Unit (ICU) patients are exposed to various medications, especially during infusion, and the amount of infusion drugs and the rate of their application may negatively affect their health status. A deep learning model can monitor a patient's continuous reaction to tranquillizer therapy, analyze the treatment plans of experts to avoid severe situations such as reverse medication associations, work with a convenient mediator, and change the treatment plans of specialists as needed.Generally, patients' treatment histories are linked together via a period grouping connection, which is usually burdened by missing information. Displaying time-succession via Repetitive Neural Organization (RNO) is the best available solution. However, it's possible that a patient's treatment may be prolonged, which RNN may not be able to demonstrate in this manner.We propose the use of the LSTM-RNN driven by heterogeneous medicine events to predict the patient's outcome, as well as the Regular Language Handling and Gaussian Cycle, which can handle boisterous, deficient, inadequate, heterogeneous, and unevenly tested prescription records of patients while addressing the missing value issue using a piece-based Gaussian cycle.We emphasize the semantic relevance of every medication event and the grouping of drug events on patients in our study. We will focus specifically on LSTM-RNN and Phased LSTM-RNN for showing treatment results and information attribution using bit-based Gaussian cycles. We worked on Staged LSTM-RNN.
Boosting semi-supervised face recognition with raw faces

Deep facial recognition benefits significantly from large-scale training data; however, the bottleneck of high labeling costs persists. Therefore, to reduce the labeling costs, it is desirable to train a model using limited labeled data and abundant unlabeled data (i.e., semi-supervised learning). However, existing semi-supervised learning methods present two primary challenges: (1) The possibility of identity overlaps between the unlabeled and labeled data. These overlaps can affect the correctness of pseudo-labels of the unlabeled set. (2) Different pseudo-labels generated by the clustering algorithm may belong to the same individual (i.e., over-decomposition problem). Thus, in this study, instead of experimenting with non-overlapping conditions, we apply smooth labels to exploit the potential of those samples that are similar to the identities in the labeled set. For samples that are not similar to the labeled set, we introduce a dual clustering strategy to remedy the over-decomposition problem caused by single clustering. With the upgraded semi-supervised framework, we recycle the discarded samples during purification of MS-Celeb-1 M (MS1M) to further scale up the training set, which offers a considerable performance boost of 94.39% on the IJB-C dataset.
Binary-Coded Ternary Number Representation in Natural Language Text Compression

A lossless data compression code based on a binary-coded ternary number representation is investigated. The code is synchronizable and supports a direct search in a compressed file. A simple monotonous encoding and very fast decoding algorithms are constructed owing to code properties. Experiments show that in natural language text compression the new code outperforms byte-aligned codes SCDC and RPBC either in compression ratio or in decoding speed.
DCN-ECPE: Dual-Channel Network for Emotion-Cause Pair Extraction

It is a challenge task to extract the potential pairs of emotion clause and corresponding cause clause from the documents. The existing state-of-the-art ECPE method formulates the task in an end-to-end model, which processes the interactions of emotion-cause pairs based on joint two-dimensional. The model has two shortcomings: 1) the potential semantic particularity of the causal relation between the emotion-cause pair is not fully considered; 2) it falls short of capturing various regional features of contextualized representation. In this work, we propose an end-to-end model named DCN-ECPE. The model generates the representation of emotion-cause pairs with dual-channel, which takes both potential causal features and contextualized interactions of the clause pairs into consideration. One channel extracts potential semantic feature of the causal relation from constructed statements, and the other channel processes the representation of clause pairs with CNN to capture various regional features. Our method outperforms existing state-of-the-art end-to-end ECPE method in all aspects.