Reviewerly/exHarmony huggingface dataset:
A Deep Recommendation Model Considering the Impact of Time and Individual Diversity

Collaborative filtering (CF) technology has been widely used in recommendation systems. Usually, the latent factor model (LFM) is used as the basis for implementing CF recommendation in deep learning systems. This study differs from previous studies in two respects. First, for different target items, the user embedding vector should be dynamically adjusted according to the content of the target item. Therefore, we have added an attention mechanism to dynamically adjust the user’s embedding vector. However, people’s preferences usually change over time. Therefore, based on the above attention model, this study considers two time-decay functions to emphasize the user’s recent preferences. The first decay function considers the situation where the recent rating is more important than the long ago rating. The second time-decay function considers the situation, whereby users generally prefer movies that have been released recently rather than movies that have been released a long time ago. By combining these two time-decay functions with the attention model, we propose a time-decay adaptive latent factor model (TDADLFM) model for item score prediction. This study applies this model to a dataset integrating Movielens-10M and HetRec2011 and proves that all three new considerations can improve recommendation performance.
Novel Foreground and Background Separation Based Multi-level Coding Framework for Indoor Surveillance Video

There is larger compression potential for surveillance video coding due to the inherent super-redundancy in fixed camera scenarios. So, this paper propose a novel compression framework for indoor surveillance video.
A Review of Context-Aware Machine Learning for Stress Detection

Excessive stress can lead to poor physical and psychological outcomes thereby reducing quality of life and increasing health-related consequences. Context awareness refers to sources of information that can help technological applications be aware of human-environment interaction. Machine learning with context awareness is an emerging field with the potential to accurately detect stress and subsequently allowing for intervention implementation. Most currently existing applications do not incorporate context in machine-learning models for stress detection, and as such their applicability in real-life settings is quite low. Using context awareness will provide relevant information regarding the activity, location, and day-time resulting in increased accuracy and precision of stress detection. In this article, existing machine learning-based approaches for stress detection that considers context are reviewed.
PersonaSAGE: A Multi-Persona Graph Neural Network

Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite that a node often exhibits polysemous behavior in different contexts. In this work, we develop a persona-based graph neural network framework called PersonaSAGE that learns multiple persona-based embeddings for each node in the graph. Such disentangled representations are more interpretable and useful than a single embedding. Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings. The framework is flexible enough and the general design helps in the wide applicability of the learned embeddings to suit the domain. We utilize publicly available benchmark datasets to evaluate our approach and against a variety of baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a variety of important tasks including link prediction where we achieve an average gain of 15% while remaining competitive for node classification. Finally, we also demonstrate the utility of PersonaSAGE with a case study for personalized recommendation of different entity types in a data management platform.
Achieving Privacy and Tracing Unauthorised Usage: Anonymisation-based Fingerprinting of Private Data

Since many types of data nowadays contain personally identifiable information about individuals, it is important to apply privacy protection techniques to mitigate disclosure risks. One approach is k-anonymity, where hiding the identity within a group of k similar entities reduces the risk of re-identification. Another risk when distributing data is the loss of control over their further re-distribution and sharing. This risk is frequently addressed by fingerprinting, a method that allows to identify the recipient of a specific copy of the data, by embedding a generally invisible mark.In this paper, we specifically implement and adapt an intrinsic fingerprint scheme that makes use of k-anonymity and the fact that multiple, differently perturbed versions of a dataset can be found that all fulfil a certain k-anonymity, and share a rather similar level of data precision. Thus, these different datasets can be seen each as a fingerprinted version of the original.One research question we address in this paper is the evaluation of the most common generalisation algorithms according to their generalisation strategy and their influence on data utility and the number of resulting release candidates, i.e. fingerprints and execution time. In addition, we investigate the properties and robustness of these fingerprints against intentional (adversarial) manipulation through attack simulations. We further provide recommendations and guidelines on how fingerprinting can be best achieved based on the results of our evaluation.
A Comparison of Federated Aggregation Strategies and Architectures for Next-word Prediction

Federated learning is an important technique for training language models, which are frequently used for next-word prediction since federated learning allows utilising large quantities of real-life data without compromising the privacy of the data owners. Training a model that generalises well in this setting is a challenging task due to the inherent statistical heterogeneity of the training data, and due to the hardware limitations of private mobile devices. There are different approaches that address these issues, e.g. through model selection, different aggregation and learning strategies, and update compression. In this paper, two popular model architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are evaluated in centralised and federated settings. For federated learning, the vanilla Federated Averaging algorithm and two alternatives that try to address statistical heterogeneity, namely FedProx, which uses a proximal term to restrict the divergence from the global model during local model training, and Federated Attention, which has similar aims of reducing the distance between models as well to ensure faster convergence and improve generalisation, but is performing this during the aggregation station, are evaluated for their achieved perplexity and accuracy in various settings on two datasets. Based on these results, we provide guidelines on which methods to use, depending on the scenario.
Dividing and Merging for Imbalanced Long-Tail Datasets

In this work, we introduce a novel approach for addressing class imbalance in long-tail datasets, focusing on the strategy of dividing and merging. Our methodology involves dividing the dataset into separate clusters, each representing a different segment of the class distribution. By training dedicated models for each cluster and subsequently merging their parameters, we achieve a more balanced learning process across diverse class sizes.
Handwritten Word Recognition

Humans are the only species with the ability to recognize anything. The hand transcription is simple for humans to recognize. We have created a Convolutional Neural Network model that accurately converts handwritten words into digital text, aiming to simplify our lives. Despite the challenges posed by variations in patterns across different languages, our system employs advanced techniques such as image processing, feature extraction, and classification to enhance accuracy. Through extensive training, the system learns to recognize similarities and differences among various handwritten samples, improving its ability to identify and interpret text accurately. Our CNN-based approach holds great promise for streamlining tasks that rely on converting handwritten words into digital text.
Constructing Knowledge Graph from Cyber Threat Intelligence Using Large Language Model

Cyber Threat Intelligence (CTI) reports are valuable resources in various applications but manually extracting information from them is time-consuming. Existing approaches for automating extraction require specialized models trained on a substantial corpus. In this paper, we present an efficient methodology for constructing knowledge graphs from CTI by leveraging the Large Language Model (LLM), using ChatGPT for instance. Our approach automatically extracts attack-related entities and their relationships, organizing them within a CTI knowledge graph. We evaluate our approach on 13 CTIs, demonstrating better performance compared to AttacKG and REBEL while requiring less manual intervention and computational resources. This proves the feasibility and suitability of our method in low-resource scenarios, specifically within the domain of cyber threat intelligence.
Differentially Private Federated Learning: Privacy and Utility Analysis of Output Perturbation and DP-SGD

Federated Learning (FL) is a method that allows multiple entities to jointly train a machine learning model using data located in various places. Unlike the conventional approach of gathering private data from distributed locations to a central place, federated learning involves solely exchanging and aggregating the machine learning models. Each party shares only a machine learning model trained locally on their private data, ensuring that the sensitive data remains within the respective silos throughout the process. However, these shared models in FL may still leak sensitive information about the training data in the form of e.g. membership disclosure. To mitigate these residual privacy risks in federated learning, one has to use additional defence techniques such as Differential Privacy (DP), which introduces noise into the training data or the model. Differential Privacy provides a mathematical definition of privacy and can be applied in machine learning via different perturbation mechanisms. This work focuses on the analysis of Differential Privacy in federated learning through (i) output perturbation of the trained machine learning models and (ii) a differentially-private form of stochastic gradient descent (DP-SGD). We consider these two approaches in various settings and analyse their performance in terms of model utility and achieved privacy. To evaluate a model’s privacy risk, we empirically measure the success rate of a membership inference attack. We observe that DP-SGD allows for a better trade-off between privacy and utility in most of the considered settings. In some settings, however, output perturbation can provide a better or similar privacy-utility trade-off and at the same time better communication and computational efficiency.
Lightning Arrester Status Evaluation based on Defect Data Mining and 1D-CNN

In order to accurately judge and analyze the operating status of distribution network lightning arresters. Unlike state analysis that predicts temporal data and sets relevant thresholds, this article uses atomic clustering and hierarchical clustering algorithms to mine lightning arrester defect data from equipment operation data to eliminate interference from related operation data and reduce the requirement of excessive data volume. Secondly, an adaptive encoding and decoding framework is used to reduce the overall data and extract features, And set it as the input of a state classification model based on one-dimensional convolutional neural networks. By effectively mining defect data of lightning arresters and using 1D-CNN models, the defect status of lightning arresters can be identified. The experimental results demonstrate the correctness and effectiveness of data mining and automatic encoder, as well as the accuracy and timeliness of the 1D-CNN state evaluation model.
Knowledge Prompt for Whisper: An ASR Entity Correction Approach with Knowledge Base

Entity correction is crucial in Automatic Speech TABLE I Recognition (ASR), since erroneous entities seriously affect our understanding of ASR results. In this paper, in order to correct entity errors, we propose a knowledge prompt approach for Whisper (a recent ASR model trained with a corpus containing 680k hours of labeled speech recorded in various conditions). For a given audio, our approach consists of three steps: (1) obtaining its ASR result by Whisper; (2) fuzzy matching the ASR result with a knowledge base to obtain candidate entities; (3) using the candidate entities as a prompt to obtain the final ASR result by Whisper again. We conduct experiments on the test dataset of open-source Chinese speech corpus AISHELLNER. Experimental results show that our approach not only significantly improves the entity recall rate in ASR results (from 70.97% to 84.82%), but also reduces the overall Character Error Rate (CER).
Lightweight CNN based on Spatial Features for a Vehicular Damage Detection System

Autonomous vehicles are a key element of the automotive industry, where the impact of the human factor on the condition of the vehicle and driving is minimized. An important element is the analysis of vehicular condition, which allows maintainence of its value and correct operation. We propose a system based on the analysis of the image of vehicles, which determines whether there is any damage. For this purpose, we propose a new model of a Convolutional Neural Network (CNN) that has 0. 395M trained values. The architecture of the network is adapted to the analysis of spatial features that allow networks to be adapted to analyze primarily vehicular shape and orientation in relation to other objects. The model also implements spatial dropout and regularization techniques for preventing overtraining and maintaining model generalization. The modeled architecture contributes to obtaining high classification accuracy at 94.78% using a public database and exceeding metrics of known transfer learning models.
Exploring Wavelet Transform Usages for Error-bounded Scientific Data Compression

To address the challenges raised by the data management of exascale scientific data, error-bounded lossy compression has been proposed and well-researched as a prominent solution. Among the existing works, a recent trend leverages wavelet transforms in the error-bounded lossy compression task to effectively capture long-term data correlations within the inputs. Applying those transforms as data preprocessors and decorrelators, wavelet-based lossy compressors have achieved optimized compression rate-distortion on several datasets. However, certain significant limitations of wavelet-based compressors have also been observed: On one hand, attributed to the high computational cost of wavelet transforms, wavelet-based compressors suffer from relatively low computational efficiencies compared to other state-of-the-art compressors. On the other hand, one certain type of wavelet transform cannot perform well on all variations of scientific data. Consequently, to further fine-tune the wavelet-based scientific data lossy compression, more in-depth and systematic research and analysis needs to be conducted. In this paper, based on the FAZ auto-tuning-based modular compression framework, we have integrated a great number of wavelet transforms into the framework and evaluated them with various real-world scientific datasets and fields. From the analysis of those evaluations and the comparison to existing state-of-the-art wavelet-based and non-wavelet-based error-bounded lossy compressors, we conclude and present several essential takeaways for designing and optimizing the wavelet-based scientific error-bounded lossy compressor.
Audio-visual Neural Face Generation with Emotional Stimuli

In communication, it is important to generate realistic 3D facial avatars that can express various expressions. However, existing models have limitations in that they cannot show facial expressions in sufficient detail. To this end, we propose an audio-visual volumetric avatar generation method that reconstructs detailed facial expressions using two emotional stimuli. Emotional features extracted from the input and the rendered image induce the model to generate an avatar with the same emotion. At the same time, a speech clip is used as input along with the images, helping the model recognize the emotion of the target that is difficult to understand with the images. We showed qualitatively and qualitatively that our method achieved significantly more realistic and higher-quality avatar generation than current state-of-the-art methods.
A Hierarchical Model Loading Strategy in WebBIM Scene Based on Cloud-Edge-Terminal Collaborative Architecture

Focus on the problem of long loading time of complex model and user QOE in WebBIM scene, this paper proposes a hierarchical model loading strategy in WebBIM scene based on cloud-edge-terminal collaborative architecture. First of all, with edge computing technology, we apply the cloud-edge-terminal collaborative architecture to BIM scenarios to speed up model loading and service response processes. Next, by following the architectural semantics, the transformed IFC file is divided into index, geometry, material and related attributes, and component priorities are designed using geometry, semantics and user relevance to realize model loading based on the cloud-edge-terminal collaborative architecture. Finally, a cloud-edge-terminal collaborative BIM experimental scene is built, and we select 7 IFC scenes to compare initial loading time, earliest operable time, and effective pixel filling degree with BIMServer, the experimental results show that our strategy can effectively improve the above three aspects of performance and user QOE in WebBIM scene.
Fake Reviews Detection Based on Sentiment Analysis Using ML Classifiers

Fake reviews carry substantial amount of significance for both the consumers and business owners and thus detection of fake reviews early helps to reduce the negative influential growth has become crucial after increased popularity of online shopping. Here, in this paper we propose machine learning approach by analysing both, the text and behaviour. A dataset of reviews was collected from various sites and it was labelled as Real or Fake. We then used various machine learning approaches like the Support Vector Machine, Logistic Regression and Multinomial Naive Bayes for training and evaluation of the performance of our model. Results show that our method provides accuracy at par with the existing ones. Our method has its utilization by businesses and review platforms to detect and remove fake reviews which enhances trust in online reviews. The developed system achieves a high accuracy rate of 85% in identifying fake reviews, demonstrating its effectiveness in improving the reliability of online reviews.
Bi-Directional Face Tracking for Precise Facial De-identification in Video Data

With video data being prevalently used in numerous applications, there is a substantial privacy risk due to the potential exposure of personal information embedded in these video datasets, especially facial features. In this work, we propose a method for concealing personal facial features within video content. It aims to safeguard individuals’ personal privacy by preventing inadvertent exposure and ensuring their anonymity. This method performs video content analysis, including scene detection, face detection, and tracking, and safeguards the personal privacy of individuals by applying face masking techniques, except for target individuals of interest. We have demonstrated the superior performance of our proposed method, as evidenced by the reduction in facial exposure scores. This indicates the method’s ability to consistently uphold privacy across video sequences while minimizing the unintended exposure of personal information.
Degraded Data Enhancement Based on Regional Similarity Fusion

The scattering and absorption effect of dust particles on light leads to low contrast and serious color offset of visible light images obtained in sand-dust weather, which affects the reliability of outdoor visual applications such as traffic safety and monitoring systems. Due to complex scene structure and difficult parameter estimation, the existing sand-dust image enhancement methods can not effectively extract the semantic components of the image, resulting in unreal colors and blurred details of the enhanced image. Therefore, we propose a two-stage sand image enhancement method based on regional similarity fusion. Firstly, the gray distribution of the input dust image is compensated to recover the potential information in the scene, and two sub-images with color balance and high contrast are derived. Then, regional similarity calculation, weight allocation, and image fusion are carried out to generate the final clear image based on the regional similarity fusion strategy. The experimental results show that the proposed method can effectively restore the potential features in the dust scene, and the visible edge number ratio (e-score) and edge gradient ratio (r-score) have increased by 0.24 and 0.47 respectively.
A Management Model for the Development of Meaningful Digital Learning Resources in Homogeneous Linear Algebra Courses

After the COVID-19 pandemic, higher education institutions developed learning scenarios in interactive virtual learning environments. Different theoretical approaches allow the real representation of a mathematical problem finding an effective solution by integrating technological elements and the use of ICTs. The objective of this work is to present a management model for the development of significant learning resources in homogeneous courses of Linear Algebra in a polytechnic university in the city of Guayaquil, Ecuador. An empirical-analytical research methodology with a quantitative-quaisexperimental and longitudinal approach is used. Three work phases of this study are developed. The first phase, during October 2019 to April 2021. The second phase, from May 2021 to September 2021. The third phase, from October 2021 to April 2023. Teachers who are experts in Linear Algebra, students in each period and the team of the Academic Coordination of the institution are involved in follow-up days of the proposal. A process of development of effective learning resources with the support of technologies is evidenced. In general, it is evident how these resources affect the percentage of approved students. The teachers' perception is high in relation to the availability of the resources developed in a basic classroom. The students' response to the course is positive, evidencing their high level of satisfaction with the course.