Reviewerly/exHarmony huggingface dataset:
Evaluation of Graduate Employment Ability Based on Ahp Entropy Weight Model

In order to achieve a scientific and reasonable evaluation of graduate employability and promote high-quality employment, this article combines the AHP method and entropy weight method to construct an evaluation model for graduate employability. Firstly, a three-layer indicator model for evaluating graduate students' employability is constructed through six dimensions: scientific research ability, learning ability, cognitive ability, practical ability, workplace ability, and job application ability. Then, based on the AHP method and entropy weight method, the weights of various indicators in the criterion layer and indicator layer were analyzed, and the final weights were calculated by combining weights. Finally, it was demonstrated through empirical analysis that the model can provide a scientific and reasonable evaluation of graduate students' employability. Research shows that (1) graduate students' employability is influenced by multiple abilities, among which research ability has a relatively significant impact on employability; (2) Graduate students as a whole exhibit different characteristics under different indicators, and indicators with relatively scattered data have guiding significance for graduate management and employment; (3) The overall score of graduate students reflects their employability, which is beneficial for guiding role model promotion or employment assistance work.
TetraCVD: A Temporal-Textual Transformer based Model for Cardiovascular Disease Diagnosis

Cardiovascular disease (CVD) is one of the leading causes of death globally. There is considerable clinical significance and an emerging need of assisting doctors to diagnose cardiovascular disease and identify the subtype of it, from which doctors can provide different treatments and medications to increase the cure rate. The goal of this paper is to develop a deep learning model to predict cardiovascular disease and classify its subtype, which by handling data from two modalities of time-series vital signs and text report. We propose a temporal-textual transformer based model for cardiovascular disease diagnosis, TetraCVD, to address the challenges of irregular temporal feature extraction and medical long-text feature extraction respectively. TetraCVD is a multimodal deep learning model, consisting of two networks, cvdGNN and cvdHierBERT, as its time-series and language backbones, which leverage knowledge from temporal vital signs and text reports of the individuals respectively. Our results show that TetraCVD achieves promising performance in predicting subtypes of cardiovascular disease using the P18-ECER dataset and obtains state-of-the-art results. This study is among the first efforts that use both time-series vital signs and text report data to predict cardiovascular disease and its subtype. We argue that our approach can be generalized to predict and diagnose other diseases easily, and it can potentially play a significant role in the domain of general disease diagnosis in the future.
Utilization of PjBL-STEM Based Interactive e-modules to Improve Visual Literacy: Teacher and Student Perspectives

This research aims to describe the use of interactive e-modules based on PjBL-STEM activities to train students' visual literacy. This research uses mixed methods with sequential explanatory design to obtain quantitative and qualitative data. Quantitative data was obtained by administering questionnaires to 39 science teachers in Lampung province and 149 junior high school students and qualitative data was obtained from interviews with teachers. The research results show as many as 41.70% of teacher respondents were new to using e-module teaching materials. The e-module they use is not yet interactive and the activities in the e-module do not invite students to carry out projects. The results of the teacher questionnaire were only 15.3% of teacher respondents using the PjBL learning model. As many as 74.5% of teacher respondents stated that the e-modules used had not been used to train students' visual literacy. The results of the questionnaire and interviews of teachers responded positively to the existence of teaching materials that can train students' visual literacy.
Deep Adversarial Network Based Stain Unmixing for Brightfield Multiplex Immunohistochemistry Images

Multiplex immunohistochemistry (IHC) makes it possible to simultaneously label multiple protein biomarkers with different colored stains in a tissue section. Unmixing the multiplex IHC image provides an efficient way to obtain the rich diagnostic information each biomarker contains. However, due to the limitation of three-channel RGB images taken by a CCD color camera, it is challenging to unmix brightfield multiplex IHC images with more than three stains. The main technical challenge is that the unmixing is inherent underdeterminate, leading to the possibility of multiple solutions. In this paper, we propose a novel unmixing method using generative adversarial networks (GANs) for brightfield multiplex IHC images containing more than three stains. Our method takes advantage of slides stained only with individual biomarker to address the intriguing task without any human annotation. We propose to employ adversarial training to automatically learn the optimal unmixing, without relying on any inadequately designed priori. To the best of our knowledge, the method achieves state-of-the-art (SOTA) results in terms of unmixing quality, speed, and practicality, as evidenced by both pathologists’ visual comparisons and quantitative experiments.
Explanation of machine learning models for predicting obesity level using Shapley values

Obesity is a multifactorial disease that includes genetic, biological and behavioral factors. Training machine learning algorithms on some of variables related to these factors can help healthcare professionals equip themselves with tools capable of predicting obesity. But for these tools to gain trust they must be understandable and explainable. SHAP and LIME are two methodologies that allow you to achieve this objective.
A Study on the Self-efficacy of Students in Normal University Physical Education Colleges for Crossfield Learning

STEM; Self-efficacy; Online education
Defeating Data Plane Attacks With Program Obfuscation

Data plane switches in software-defined networks are increasingly recognised as potential targets for attack, with recent exploits showing their vulnerability to full compromise. The serious consequences of such a breach have prompted the design of compromise detection mechanisms, which monitor switch forwarding behaviour at runtime to ensure that it has not been altered by an attack. However, such defences cannot achieve full coverage in stateful, programmable data planes, creating an opportunity for an attacker to evade detection by carefully editing a switch's forwarding program to mishandle a small subset of packets. To exploit this opportunity and avoid detection, an attacker must analyse and edit the program's behaviour within a narrow time window, which is possible when the data plane is defined by a uBPF program compiled from P4, due to the predictable compilation process. In this work, we aim to invalidate this analysis-guided attack technique with targeted obfuscation of P4-uBPF programs that increases the analysis complexity. We find that, by inserting additional program paths and syntactic dependencies between variables, we can force an attacker to analyse a higher proportion of program instructions and carry out time-consuming SMT solving to find valid program paths, rendering the previous attack technique infeasible. Furthermore, by applying our identified program optimisations, program performance can often be maintained after obfuscation. In evaluating our work, we identify the potential to improve our solution by tailoring obfuscations to individual program paths.
MAPPG: Multi-Agent Phasic Policy Gradient

We propose a Multi-Agent Phasic Policy Gradient (MAPPG) algorithm, which can assist agents to further alleviate the non-stationarity of the environment. Different from the existing methods, the auxiliary phase is introduced to train the local policy directly by using the environment state, which can be naturally integrated into other algorithms. Specifically, the hidden layer feature sharing is proposed, which ensures feature sharing between the global value network and the local policy network for the first time. Meanwhile, mirror descent is utilized to iteratively update the policy in the auxiliary stage, which makes the policy update more robust. Through a series of evaluations on multi-agent Particle and multi-agent Mujoco benchmark environments, the experimental results show that our method achieves higher rewards than state-of-the-art benchmarks.
Cloud-Based Smart Sensing System for Real-Time Health Management of Dementia Patients

The paper describes an innovative technique of monitoring updates on the health of people with dementia by integrating smart sensing technologies with a cloud-based platform. Because dementia is a degenerative neuropsychiatric disease that develops with time, constant patient monitoring is essential for ensuring the health and safety of those living with the condition. The proposed system uses smart sensing devices, including wearable and ambient sensors, to monitor dementia patients' health in real-time. Data like heart rates, movements used, and ambient conditions are collected and sent to a server in the cloud for analysis. The camera can monitor and analyze visual data, capture crucial information, and help evaluate the patient's well-being. Multiple patients can be monitored effectively in real time because of the cloud-based system's scalability, accessibility, and storage capacities. For healthcare providers to recognize irregular movements, identify possible risks to health, and implement preventative measures, doctors utilize the moment data analytics devices to gather valuable insights from the acquired data. In addition, the cloud-based solution improves collaborative care and enhances patient support by allowing for continuous communication and information sent among caregivers, doctors, nurses, and family members. Predefined thresholds or anomalous occurrences may trigger alerts and messages, allowing for quick action in reaction to emergencies. Modern technology and data analytics increase the health and happiness of people with dementia.
CNN-based Cognitive Impairment Prediction using Handwriting Recognition and Analysis

Dysgraphia is a part of Cognitive impairment and it hampers a child's writing skills. Child used to write misconstrued and teacher used to feel that child is having laziness or a lack of enthusiasm in academics and it leading to more frustration and low self-esteem. Diagnosis and rehabilitation of dysgraphia are delayed by the subjective nature of traditional assessment approaches and their heavy reliance on professional knowledge. Furthermore, the assessment procedure's accessibility is sometimes compromised by the requirement for specialized equipment and personnel with the necessary skills. In other words, the current method of identifying cognitive impairment typically relies on subjective assessments provided by teachers or psychologist. In the study Cognitive Impairment Prediction Using Handwriting Analysis, we employed the deep learning methods like Convolution Neural Network algorithms and CNN-3. The CNN-2 and VGG16 are the next best models, but performance of VGG19 is poor. Performance analysis is a critical step in evaluating the effectiveness of a system or model. In deep learning, it is important to comprehend a model's performance on a certain task.
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement

Recently, researchers have shown an increasing interest in automatically predicting the subjective evaluation for speech synthesis systems. This prediction is a challenging task, especially on the out-of-domain test set. In this paper, we proposed a novel fusion model for MOS prediction that combines both supervised and unsupervised approaches. In the supervised aspect, we developed a SSL-based predictor called LE-SSL-MOS. The LE-SSL-MOS utilizes pre-trained self-supervised learning models and further improves prediction accuracy by utilizing the opinion scores of each utterance in the listener enhancement branch. In the unsupervised aspect, two steps are contained: one is that we fine-tuned unit language model (ULM) using highly-intelligible domain data to improve the correlation of an unsupervised metric SpeechLMScore. Another is that we utilized ASR confidence as a new metric with the help of ensemble learning. To the best of our knowledge, this is the first architecture that fuses supervised and unsupervised methods for MOS prediction.With these approaches, our experimental results on the VoiceMOS Challenge 2023 show that LE-SSL-MOS performs better than the baseline. Our fusion system achieved an absolute improvement of 13 % over LE-SSL-MOS on the noisy and enhanced speech track. And our system ranked 1st and 2 nd respectively in the French speech synthesis track and the noisy and enhanced speech track of the challenge.
Towards Developing State-of-The-Art TTS Synthesisers for 13 Indian Languages with Signal Processing Aided Alignments

End-to-end (E2E) systems synthesise high-quality speech, but this typically requires a large amount of data. As E2E synthesis progressed from Tacotron to FastSpeech2, it became evident that features representing prosody, particularly subword durations, are important for error-free synthesis. Variants of FastSpeech use a teacher model or forced alignments for training. This paper uses signal processing cues in tandem with forced alignment to produce accurate phone boundaries for the training data. As a result of better duration modelling, good-quality synthesisers are developed. Evaluations indicate that systems developed using the proposed signal processing-aided approach are better than systems developed using other alignment approaches, especially in low-resource scenarios. Our systems also outperform the existing best TTS systems available for 13 Indian languages.
Improving Multilingual and Code-Switching ASR Using Large Language Model Generated Text

We investigate using large language models (LLMs) to generate text-only training data for improving multilingual and code-switching automatic speech recognition (ASR) through a text injection method. In a multilingual setup or a low-resource scenario such as code-switching, we propose to generate text data using the state-of-the-art PaLM 2. To better match the generated text data with specific tasks, we use prompt tuning to adapt PaLM 2 to generate domain-relevant multilingual or code-switched text data for text injection. We can achieve significant improvements in Word Error Rate (WER) in both multilingual and code-switching scenarios. The multilingual experiment shows a $6.2 \%$ relative WER reduction on average, i.e., from $11.25 \%$ to $10.55 \%$, compared to a baseline without text injection. The improvement is up to $23.1 \%$ improvement for certain languages. While in the code-switching scenario, we use English-only prompts to generate Mandarin-English code-switching text and achieve a 3.6% relative WER reduction for a code-switching test set, as well as WER reductions in both English and Mandarin monolingual scenarios, $5.3 \%$ and $8.5 \%$ relative, respectively. Our findings demonstrate that leveraging LLMs for text generation and then injection benefits multilingual or code-switching ASR tasks.
Optimization of the visual communication path of data news in the context of the Internet

Abstract Visual news belongs to a kind of data news, which uses emerging technology to bring users a new reading experience and improve the news dissemination effect. In this paper, we utilize the self-attention mechanism and BiLSTM model to extract the content of data news documents and select and classify the audio features of data news by the AdaBoost algorithm. ResNet50 is used as the backbone network, combined with multiple residual unit modules for news multi-scale image feature extraction, and the text, audio and image features of the data news are fused by constructing a multimodal feature adaptive fusion model to further optimize the visual display of the data news. This paper verifies the model’s effectiveness for the data news visualization technique through multiple perspectives, including training loss curve, performance comparison, and ablation experiment. The results show that the loss value of the model based on the Self-Attention-BiLSTM model for news text feature extraction is 0.211 after 150 iterations. The AdaBoost-based news audio feature classification method has an average classification accuracy of 97.56% for the positive diagonal when the noise is raised from 0dB to 30 dB. By adding the residual unit module to the ResNet50 backbone network, the accuracy of the multi-scale image feature extraction model has been improved by 2.77% compared to the single backbone network. The information age requires the full integration of technology and news and combining the corresponding Internet platform for the visual display of data news in order to promote the expansion of the visual communication path of data news.
Research on Physical Education Management System in Higher Education Institutions in the Context of Deep Learning

Abstract This paper focuses on the management of physical movement sports disciplines and combines relevant algorithms to design an artificial intelligence-based physical education management system framework. In terms of related algorithms, the first is for the research of character recognition algorithms. Considering the intra-class differences between individual movements, the LR-based feature extraction algorithm is proposed. Secondly, for the research of the action evaluation algorithm, the statistical value, DTW parameter action time series difference and correlation coefficient based on the statistical value and DTW parameter are used as the features, combined with SVM-based feature extraction algorithm for perceiving the action differences. Finally, with the help of deep neural networks for movement analysis, we can improve students’ movement techniques during training. In the course schedule supported by the system, the proportion of moderate-intensity exercise time is high, basically, more than 40%, which meets the WHO requirements for high-intensity exercise time in physical education classes. The post-course RPE feedback from students was spread out between 12 and 15, and the highest post-course RPE value was 14.82&lt;unk&gt;1.38, which was evaluated within a reasonable range of intervals. The teaching management system in this paper can meet the requirements of physical training and teaching, and provide quantifiable basis and management tools for the informatization and precise teaching of physical movement subjects.
Floods risk determination through a fuzzy logic system in developing countries. Case study Magdalena River, Colombia

Natural disasters around the world, and specifically floods, can generate great economic, en- vironmental, and human life losses in short periods of time. Early warning systems have been developed as a tool to mitigate the impact of these floods, nevertheless, most of this information is dispersed. In the particular case of the Colombian context, the Institute of Hydrology, Meteorology and Environmental Studies of Colombia (IDEAM) has developed a platform called Famine Early Warning Systems Network (FEWS), which presents data of interest for the different stations in the basins of the territory, such as: level, flow, and rainfall. From the height level of water table data of each station, the platform presents three possible alerts: yellow, orange, and red, which are obtained y comparing with reference or threshold levels associated ith ach station. However, the method currently used to determine the alerts does not consider the ariation of he water table level as a function of ime, or the degree of danger represented y a value close to or ending to flood threshold levels. This work proposed a contribution to determine the flood risk level, based on the development of a fuzzy system, taking as inputs the water table level and the level variation as a function of time, to obtain as output the flood risk level in numerical and linguistic terms. For the determination of the risk level, the system applies inference logic rules, involving operations between fuzzy sets represented through mathematical membership functions. Finally, the operation of the fuzzy system was validated using data from the FEWS platform for the hydrological stations of the Bajo Magdalena basin. This project aims to serve as a reference to improve early warning systems of developing couries
Analysis of Challenges and Countermeasures for Higher Education Management in the Era of Big Data

Abstract This paper analyzes the factors affecting the efficiency value of human resource management in higher education by using the Tobit regression model, puts forward the hypotheses of the influencing factors, constructs a model of the factors affecting the efficiency of human resource utilization in higher education, and derives the specific correlation coefficients of the influencing factors. After examining the correlation between each dimension of teacher management culture in higher education and teachers’ organizational commitment and its dimensions using Pearson’s product-difference correlation method, the correlation between educational management and teachers’ teaching commitment is verified through the quantitative measurement of correlation analysis and linear regression analysis. Based on the decision tree algorithm and multiple linear regression method to predict the resource allocation of teachers’ positions, we constructed a model of human resource optimization and allocation strategy for college teachers and carried out the actual analysis of the utilization rate of resources for teachers’ positions and teachers’ schedules by genetic algorithm. Analysis obtained shows that selecting different population sizes for test results in teacher positions and teacher scheduling programs results in higher algorithm efficiency for populations between 30-50. When the value of the population is taken in the range of 30-50, the average time of the experiment is 60380.4ms-153199.2ms.