Reviewerly/exHarmony huggingface dataset:
Dual-Teacher Knowledge Distillation for Strict Cold-Start Recommendation

Recommender systems (RecSys) aim to predict users’ preferences based on historical interactions and content profiles, and they are vital components of many online services. However, the strict cold-start (SCS) issue, i.e., users/items have no prior interactions, poses significant challenges for RecSys. The existing methods seek to transfer content knowledge, collaborative filtering (CF) knowledge, or combine the two from the warm-start scenario towards the (strict) cold-start scenarios. However, these approaches either ignore the available information or model the information in rough manners such that the two types of knowledge interfere with each other, leading to ineffective and uncontrolled knowledge transfer. In this work, we propose a novel dual-teacher knowledge distillation (DTKD) framework that simultaneously and effectively transfers both content and CF knowledge. The proposed DTKD framework contains two teachers, one for each knowledge type, that is specifically designed according to the characteristics of the content and CF data to distill the knowledge fully. Soft scoring is calculated during the distillation to denoise and augment the original hard-labeled interactions. A knowledge fusion module is then proposed to collect the consensus of the two teachers’ opinions. Finally, DTKD transfers both content and CF knowledge into a student module that learns the shared viewpoints of the teachers. We conduct extensive experiments on real-world datasets under the warm-start as well as three different SCS settings (i.e., strict cold users, strict cold items, and strict cold users & items). Experimental results show that DTKD outperforms strong baselines by large margins under all settings, especially the SCS ones.
Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis

Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR), the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Synthetic data that retains the statistical properties of the original data but does not include actual individuals’ information provides a suitable alternative to sharing sensitive datasets. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We also show that our framework can evade potential threats, such as re-identification and linkage issues, and secure data based on applicable regulatory policy rules.
Information Security Technology Based on Remote GPS Data Acquisition System

In the existing system to add security technology, this paper makes the existing system more practical, the purpose is to achieve the VPN virtual private network function, so that the transmitted data can be safely transmitted on the Internet. Based on the principles of the Internet Protocol Security (IPsec), the System connects existing Global Positioning System (GPS) data to the virtual private network so that GPS data can be transmitted securely. It can send and receive data using serial interface, and can transmit data across the network. Through the design of the database, it can understand how to write the database, and how to use the remote terminal to read the database data. In this paper, the password policy success rate is 93%, and the data backup strategy success rate is 92%. This paper is of great significance to improve the level and technical level of water traffic safety information system.
Multimodal sentiment analysis using Multi-Layer Fusion Convolution Neural Network

The rapid development of social network helps the people to express the emotions and opinions through videos streaming in online platforms. But, most of the recent researches based on multimodal sentiment analysis does not perform an effective fusion on multimodal data. To overwhelm this issues, this research introduced a multilayer fusion Convolutional Neural Network (CNN) to perform a multimodal sentiment analysis. The data acquisition is performed from CMU-MOSI and CMU-MOSEI, the textual data is processed by tokenization and BERT; the video embedding is performed using FACET transformer and Mel-spectrogram with VGGish is used in encoding. At last, the decoding takes place using Multilayer Fusion CNN. The results obtained through experimental analysis shows that proposed approach achieved optimal accuracy of 94.61 % for MOSI dataset which is comparably higher than hybrid contrastive learning method, Multi-Tensor Fusion Network (MTFN), Sparse- and Cross-Attention Network (SCANET) and Adaptive Modality-specific Weight fusion network (AdaMoW) with accuracies of 85.2%, 80.9%, 85.98%, and 86.58% respectively.
ATADA: Adaptive Time Aware Anomaly Detection Approach for Real-Time Intelligent Transportation Systems

Accurately detecting passenger traffic flow in public transportation, e.g., for the purpose of identifying congested stations, and detecting anomalies in that flow, are both important for enhancing passenger satisfaction and safety. However, current methods for traffic flow detection and prediction train their models in offline modes using potentially outdated data, which can’t be refreshed with new data. In this paper, we introduce a two-step, continuously updating framework aimed at real-time prediction of anomalies within transportation networks, which we name Adaptive Time-Aware Anomaly Detection Approach (ATADA). Specifically, the first step filters out regular traffic patterns and balances the data set, while the second step employs a sequence-to-sequence attention model, a type of deep learning model, to further detect the traffic anomalies. We propose a dynamic online time-aware learning mechanism which enables our models to continuously train on incoming data and to adapt predictive strategies based on the most recent traffic patterns. The proposed method is validated using real subway AFC data from Suzhou, China and Hangzhou, China. Experimental results demonstrate that our framework significantly improves efficiency while maintaining high accuracy in real-time traffic anomaly detection.
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision

Prompted weak supervision (PromptedWS) applies pre-trained large language models (LLMs) as the basis for labeling functions (LFs) in a weak supervision framework to obtain large labeled datasets. We further extend the use of LLMs in the loop to address one of the key challenges in weak supervision: learning the statistical dependency structure among supervision sources. In this work, we ask the LLM how similar are these prompted LFs. We propose a Structure Refining Module, a simple yet effective first approach based on the similarities of the prompts by taking advantage of the intrinsic structure in the embedding space. At the core of Structure Refining Module are Labeling Function Removal (LaRe) and Correlation Structure Generation (CosGen). Compared to previous methods that learn the dependencies from weak labels, our method finds the dependencies which are intrinsic to the LFs and less dependent on the data. We show that our Structure Refining Module improves the PromptedWS pipeline by up to 12.7 points on the benchmark tasks. We also explore the trade-offs between efficiency and performance with comprehensive ablation experiments and analysis. Code for this project can be found in https://github.com/BatsResearch/su-bigdata23-code.
Hospital Management Information System

Information systems are an important factor for improving services as well as data savings for hospitals. Computerized service information systems are the right solution to solve problems. This research aims to determine the application of the Hospital Management Information System at Luwu District Hospital in improving service quality. The hospital management information system at Batara Guru Belopa Hospital includes three activities, namely input, process and output, which in its implementation is influenced by driving and inhibiting factors. The results of this research reveal that the RL 1, RL 2a, RL 2b reporting mechanisms created are incomplete and not in accordance with the Ministry of Health's regulations which should consist of RL 1, RL 2a, RL 2b, RL 3, RL 4, RL 5 and RL 6, So there are still obstacles in data processing, both manually and with computerized systems. The conclusion is that the resulting Management Information System does not support the reporting produced as feedback on the evaluation of the implementation of the programs being run.
Development of information technology for secure file storage based on hybrid cryptography methods

This article explores the development of information technology for secure file storage based on hybrid cryptography methods. It highlights the importance of secure file storage in the digital age and introduces hybrid cryptography as a solution for enhanced data security. The purpose of the article is to provide a comprehensive understanding of the benefits and advancements in hybrid cryptography for secure file storage. The article discusses the differences between symmetric and asymmetric encryption algorithms and introduces hybrid cryptography as a combination of both. It delves into the advantages of hybrid cryptography, emphasizing its ability to provide robust security and efficient data protection. The use of hybrid cryptography in encryption at rest and encryption in transit is examined, highlighting its role in securing stored data and ensuring secure data transmission. Moreover, the article explores the authentication, integrity, and reliability features provided by hybrid cryptography. It discusses the importance of key management and its impact on secure file storage.
RSP-gcForest: A Distributed Deep Forest via Random Sample Partition

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing.
Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training

In this paper, we consider a heavy inner product identification problem, which generalizes the Light Bulb problem ([1]): Given two sets $A \subset\{-1,+1\}^{d}$ and $B \subset\{-1,+1\}^{d}$ with $|A|=|B|=n$, if there are exact k pairs whose inner product passes a certain threshold, i.e., $\{\left(a_{1}, b_{1}\right), \cdots,\left(a_{k}, b_{k}\right)\} \subset A \times B$ such that $\forall i \in[k],\left\langle a_{i}, b_{i}\right\rangle \geq \rho \cdot d$, for a threshold $\rho \in(0,1)$, the goal is to identify those k heavy inner products. We provide an algorithm that runs in $O(n^{2 \omega / 3+o(1)})$ time to find the k inner product pairs that surpass $\rho \cdot d$ threshold with high probability, where $\omega$ is the current matrix multiplication exponent. By solving this problem, our method speed up the training of neural networks with ReLU activation function.
Hubungan antara Kesadaran Orang Tua Tentang Perkembangan Bahasa Dengan Kemampuan Bahasa Ekspresif Pada Anak Usia Prasekolah di KB-TK Aisyiyah Al-Amin Surakarta

Abstract Background: The development of language skills in children is divided into 2, one of which is expressive language skills. An expansive language ability is a person's ability to convey messages verbally or in writing. The development of children can be influenced due to the awareness of the closest people, especially parents. But in reality, there are still many parents who do not have awareness of their role in stimulating children's language from an early age. Awareness consists of 5 aspects including AWAREness namely, Attention, Wakefulness, Architecture, Recall of Knowledge and Emotive. Purpose: The purpose of this study was to determine the relationship between parents' awareness about language development and expressive language skills in preschool-age children in KB-TK Aisyiyah Al-Amin Surakarta. Methods: This research is a quantitative study using a Cross Sectional research design. The sample used was 30 respondents based on the general benchmark of "role of thumb". The sampling technique uses purposive sampling technique. The statistical test used is the Spearman Rank correlation test. Results: Parents' awareness of language development was in the good category of 15 parents (50.0%). Enough for 10 parents (33.3%), and less for 5 parents (16.7%). Expressive language skills in the above category averaged 12 respondents (40.0%), in the average category there were 13 respondents (43.3%) while the remaining 5 respondents (10.0%) were in the below average category. The results of the bivariate analysis obtained that the p value was 0.001 which indicated that the p value was 0.001 &lt;0.05 and the correlation coefficient (r) was 0.560. Conclusion: Parents' awareness of language development is related to expressive language skills in preschool-age children in Aisyiyah Al-Amin Surakarta Kindergarten with moderate correlation strength. Keywords: Parental awareness, expressive language skills, age preschool.
Knowledge-based Visual Question Answering about Named Entities

This thesis is positioned at the intersection of several research fields, Natural Language Processing, Information Retrieval (IR) and Computer Vision, which have unified around representation learning and pre-training methods. We have defined and studied a new multimodal task: Knowledge-based Visual Question Answering about Named Entities (KVQAE). We were particularly interested in cross-modal interactions and different ways of representing named entities. We also focused on data used to train and, more importantly, evaluate Question Answering systems through different metrics. We annotated a dataset for this purpose, the first in KVQAE comprising various types of entities. We also defined an experimental framework for dealing with KVQAE in two stages through an unstructured knowledge base and identified IR as the main bottleneck of KVQAE, especially for questions about non-person entities. To improve the IR stage, we studied different multimodal fusion methods, which are pre-trained through an original task: the Multimodal Inverse Cloze Task. We found that these models leveraged a cross-modal interaction that we had not originally considered, and which may address the heterogeneity of visual representations of named entities. These results were strengthened by a study of the CLIP model, which allows this cross-modal interaction to be modeled directly. Awarded by : Université Paris-Saclay, Orsay, France on 8 November 2023. Supervised by : Olivier Ferret and Camille Guinaudeau. Available at : https://www.theses.fr/s247993.
Application of PRT Technology in Rehabilitation Intelligent Training System

Since ancient times, childhood education has been crucial. Group A received PRT intervention training, while Group B received traditional rehabilitation training. The evaluation results using the Autism Behavior Checklist (ABC) showed that the average score of Group A was around 69.74 points, while Group B was around 73.4 points. This proved that child rehabilitation training under PRT technology has significant advantages in enhancing children's social adaptability, promoting motor function recovery, and significantly improving the intelligence level of autistic children, thereby alleviating their psychological anxiety and depression, which is beneficial for their physical and mental health development. PTR technology is an excellent auxiliary tool in the intelligent training system for children's rehabilitation. It can not only alleviate the physical discomfort symptoms of children, but also enable patients to acquire better learning abilities, injecting new impetus into promoting the scientific direction of children's rehabilitation.
Key Analysis of Integer Factorizing based Public-Key Cryptosystems Using Machine Learning

This paper discusses the possibilities of using machine learning models to analyze the keys in public key encryption algorithms that are based on various mathematical hard problems. Given their distinct domains within computer science, machine learning and public key cryptosystems may appear unrelated at first glance. Nonetheless, there are intriguing correlations and potential points of convergence between these two domains. In general, machine learning models have the potential to help with differential cryptanalysis, a technique that uses statistical differences between plaintext-ciphertext pairs to deduce parts of the key. As a result, breaking encryption keys using machine learning is a complex and evolving research area that investigates cryptanalysis machine learning techniques. While public key encryption algorithms were designed to be secure against various attacks such as brute force, advances in computing power and machine learning have raised concerns about the potential emergence of new attack vectors.
Green City Environmental Monitoring and Landscape Planning and Design Based on Communication Technology and IoT

Cities are highly concentrated areas of human civilization, the contradiction between urban development and resources and environment has become increasingly prominent. Inefficient use of energy and land resources, shortage of water resources, and environmental pollution are threatening the healthy development of cities. In this paper, the signal reconstruction algorithm and measurement matrix design in the compressed sensing theory are mainly studied. Aiming at the problems of green city environmental monitoring and landscape design, signal underestimation or overestimation caused by the fixed selection step in the iterative process of sparse adaptive matching tracking algorithm, The threshold idea is introduced into atomic selection, and a variable step size strategy is proposed based on the change of step size. The experimental results show that the establishment of the green city environment monitoring and landscape design model system dynamically changes the network topology, so that data can be transmitted in the mobile ad hoc network.
Differential Evolution Enhanced by Combining Group Learning and Elite Learning

Differential evolution (DE) is fully validated as a feasible algorithm for solving optimization problems. Additionally, for the complex optimization problems with high dimension, the traditional DE suffers from slow convergence. This paper proposes an enhanced DE algorithm that combines group learning and elite learning. The proposed algorithm improves the global search capability while guaranteeing a certain convergence speed. Through extensive experiments we confirm the superior competitiveness of the proposed DE algorithm compared to the traditional ones.
Adaptive Real-time Rectifying Exercise Porsture of Sport Rehabilitation System Based on MediaPipe

Exercise and fitness play a vital role in maintaining individuals’ health and well-being throughout their career. It is crucial to choose the right type of fitness program that ensures regular exercise and measurable improvements in your overall health. Online fitness training and offline trainer-guided training are two primary types of exercise and fitness. Online passive learning solely relies on following a video, often leaving learners unaware of their action mistakes. On the other hand, offline sports and fitness methods are constrained by time, space and financial-factors with the effect of coach proficiency levels affecting the learner’s outcome. This poses difficulties for learners seeking effective guidance. Hence, enhancing the use of online fitness videos for intelligent exercise posture guidance has become a crucial research topic. To address this issue, the paper proposes a improved solution, which is designed to be the adaptive real-time rectifying exercise porsture of sport rehabilitation system that will be enable to provide effective identification and analysis of exercisers’ postures, along with real-time comparison with professional templates by leveraging mediapipe framework. By comparing joint angles and the adaptive function of templates, the system can provide users with real-time feedback for correcting their posture during home life training. This ensures timely and accurate sport rehabilitation guidance, resulting in a virtual fitness coach for real-time fitness guidance. In the squat posture test, the system showed an 15% increase in accuracy and a 7% reduction in misdirection in contrast to conventional online exercise methods. This provides a alternative inexpensive solutionover traditional methods and confirms the solution’s potential for real-time aligning exercise posture.