Reviewerly/exHarmony huggingface dataset:
Which User Guidance Works Better in VR? A User Guidance Learning Effect Study in Virtual Environment

Virtual reality (VR) has become popular recently due to the release of several consumer-grade VR devices. At present, VR technology has been widely used in education, entertainment, and other fields. However, up to now, there is no unified method to teach users how to interact in the virtual environment. This work explores the impact of guidance forms on user experience and basic operations in virtual environments based on three VR games with interactive content ranging from simple to complex. We conducted a user study (n=105) to compare the impact of text-and-image-based guidance, video-based guidance, and interactive guidance on user experience and basic operational learning. The results show that interactive guidance makes users more immersive, especially in environments that involve complex interactions. At the same time, the user’s sense of immersion, which is reflected in the heart rate change in this study, is also significantly correlated with the user’s learning situation and can be used as an indicator to roughly estimate the user’s learning situation. These findings emphasize that user-guidance forms, as an important part of the virtual reality experience, can directly affect the user experience and the user’s learning of operations in the virtual environment.
Computer Vision Based Auto-ID for Optimizing Logistics Operations

The increase in logistics demand has resulted in a rapid growth of logistics warehousing management. Enterprises need to manage not only their goods but also their flow and storage. Automatic Identification technology based on barcodes and RFID can help identify items in the supply chain quickly and automatically. However, using barcodes can be inefficient, and RFID technology has issues with false alarms in logistics operations. To address these limitations, we propose a new solution using computer vision techniques. This proposal aims to optimize the identification process with a new set of visual codes called Bleecker and a computer vision-based system architecture that can be implemented in any logistics warehousing management system. The proposed solution offers a cost-effective and practical alternative to RFID-based methods, overcoming the limitations of traditional identification technologies. The authors provide a comprehensive case study of the proposed system, which was implemented in a primary plant of a company that specializes in the production and packaging of fresh horticultural products.
Decentralized Machine Learning on a Blockchain: Case Studies

Our approach involves utilizing smart contracts on a blockchain to enhance transparency and collaboration in machine learning. To achieve this, we integrate machine learning with blockchain technology. We showcase four practical examples of this approach in action through real-world case studies. Furthermore, we offer free and open source implementation of these case studies on the Bitcoin SV blockchain.
Research on Social Stability based on AHP-FCE and CART Decision Tree

To explicate the factors that contribute to the emergence of color revolutions, this study adopted two methodological approaches. Firstly, the Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation (AHP-FCE) method was utilized to construct a social stability index system consisting of five indicators, namely survival guarantee, economic support, social control, external environment, and social psychology. Weights were assigned to each variable based on a review of prior literature, which was then employed to calculate a social stability score. Secondly, a Classification and Regression Tree (CART) decision tree model was generated using the aforementioned variables, which allowed for a visual representation of the impact of each indicator on the outcome. To assess the validity of the findings, a case study was conducted on Egypt and Tunisia. The results revealed that a substantial deterioration of the external environment is an essential factor for the emergence of a color revolution, whereas a lack of economic support is the most critical factor influencing its success.
Graph Neural Network for Digital Twin-enabled Intelligent Transportation System Reliability

Digital Twin (DT) is a promising technology for executing real-time monitoring and diagnostic operations in Intelligent Transportation Systems (ITSs). Guaranteeing safety and functionality in DT-enabled ITS domain requires reliable data streams among vehicular edge and cloud network resources. Therefore, in this paper, we propose Graph Neural Network based Anomaly Detection (GNN-AD) as a generic end-to-end framework for achieving reliability in multi-dimensional data streams of ITS platforms. Leveraging unsupervised and supervised machine learning algorithms, namely BiDirectional Generative Adversarial Network (BiGAN), affinity propagation and Graph Convolutional Network (GCN), we semantically model the complex distributions of ITS data streams and perform anomaly detection utilizing graph representations. Employing DT applications and diverse sensory measurements from the BeIntelli smart mobility platform of TU Berlin, we compare the performance of our approach with traditional and deep learning based methods in terms of detection accuracy and Receiver Operating Characteristics (ROC). Experimental results show that GNN-AD results in higher accuracy and larger Area Under the Curve (AUC) values compared to other supervised and unsupervised multi-stage end-to-end machine learning techniques.
News-SBG: A novel news classification model with delivered label representation

With the acceleration of news data updating, the amount of news data increases sharply. The traditional complex Chinese news classification model can not meet the needs of users. The previous methods most follow the English text classification methods, ignoring the semantic structure of Chinese news itself. In this paper, we propose the News-SBG, a model for exploiting the implicit structure of Chinese news data. Besides achieving better results on news text classification tasks, it is more effective to use compared to previous models. Then, We design the Label-GRU to combine the representation features of the following sentences with the state features. Additionally, we borrow the Cosformer [1], which roughly reduces the complexity of computing the attention matrix. The most crucial aspect is that it can increase the precision of our model and emphasize key sentence elements. Our experiments prove News-SBG has a better classification effect than the previous models, achieving a satisfactory accuracy on the selected Chinese text datasets. Code is released at https://github.com/hap123ccj/News-SBG.
Sarcasm Detection of Dual Multimodal Contrastive Attention Networks

Sarcasm is a rhetorical method that is commonly used on social media platforms or in daily life to communicate a speaker’s feelings of irritation, anger, or mocking, and it often presented in implicit and exaggeration ways. Early sarcasm detection relied on unimodal text identification; however, sarcastic conversations cannot be precisely identified by unimodal model in domains like dialogues system. Most current multimodal studies focus on feature fusion or modality comparison. This method ignored dealing with out-of-vocabulary (OOV) words. For example, this sentence, ”All right, Amy’s in charge of pricing and being seventy-five.” which using the number seventy-five to represent outdated clothing tastes, an obvious sarcasm that would be taken as a neutral word and lead to inconsistencies across different modalities being ignored. We call such words out of vocabulary (OOV) words. In this paper, we propose a dual multimodal contrastive attention network (DMCAN) for sarcasm detection, which consists of two sub-networks containing contrastive attention mechanisms. The first of these networks scores the word polarity of OOV words (including neutral words with sarcasm meaning) and is called the polarity scoring network, while the second network detects sarcasm by inconsistency between different modalities and is called the sarcasm detection network. Our experiments on MUStARD (Multimodal Sarcasm Detection Dataset) prove the effectiveness of the model.
Community discovery method based on graph attention autoencoder

To explore the topology of the data, many clustering methods embedded in graphs have been developed in recent years, but none of them consider the community-specific distribution of node representations, leading to unsatisfactory clustering performance. In addition, node attribute reconstruction and graph structure reconstruction are rarely considered simultaneously, resulting in decreased graph learning ability. We integrate node representation learning and clustering into a unified framework and propose a new graph-attention auto-encoder for node clustering, which attempts to reconstruct node attributes by exploiting self-attention mechanism to learn more favorable node representations. At the same time, the community-specific distribution of node representations is constrained using an $\ell$1,2-parametric penalty so that node representations of the same community have the same distribution in the dimensional space, while node representations of different communities have different distributions in the dimension. We modify the decoder reconstruction parameters to map the inner product of node pairs. Our experimental results show that our proposed method outperforms the current state-of-the-art methods in terms of performance.
Emergent Outcomes of the veToken Model

Decentralised organisations use blockchains as a basis for governance: they use on-chain transactions to allocate voting weight, publish proposals, cast votes, and enact the results. However, blockchain-based governance structures have challenges, mostly notably, the need to align the short-term outlook of pseudononymous voters with the long-term growth and success of the decentralised organisation. The Vote-Escrowed Token (veToken) model attempts to resolve this tension by requiring voters to escrow or lock tokens of value for an extended period in exchange for voting weight. In this paper, we describe the veToken model and analyse its emergent outcomes. We show that voting behaviour follows bribes set by higher-level protocols, and that the cost per vote varies depending on how it is acquired. We describe the implementation of the veToken model by Curve Finance, a popular automated market maker for stablecoins, and the ecosystem of protocols that has arisen on top of this implementation. We show that voting markets such as Votium largely determine the outcome of fortnightly votes held by Convex Finance, and we show that Frax Finance, a stablecoin issuer, plays a central role in the ecosystem even though they directly lock relatively few tokens with Curve. Instead, they indirectly lock tokens through yield aggregators such as Convex Finance and purchase voting weight through voting markets such as Votium. Although the veToken model in isolation is straight-forward and easily explained, it leads to many complex and emergent outcomes. Decentralised organisations should consider these outcomes before adopting the model.
Dense Vehicle Counting Method Based on Deep Spatio-Temporal Network

Accurate estimation of the number of dense objects such as crowds or vehicles in an image is a meaningful research task, which has been applied in many fields such as safety management and traffic prediction. The existing CNN-based methods are not effective for extracting the features of long-distance queuing vehicles in traffic jams for complex spatiotemporal scenes. To tackle this issue, we propose MSCNet, a novel deep spatio-temporal network for vehicle counting. The network solves the problem of continuous scale variation for queuing vehicles and improves the feature extraction ability in mixed scenes. MSCNet mainly consists of three functional modules: the direction-based perspective coding module (DPCM) which encodes the perspective information in four directions to extract long-distance continuous features, the multi-scale dilated residual module (MDRM) to extract the large-scale variation features, and the channel-aware attention module (CAM) which enhances important channel features for vehicle counting in the human-vehicle mixed scenes. We have conducted extensive experiments on the TRANCOS dataset, the VisDrone2021 Vehicle&Crowd dataset, and the ShanghaiTech dataset. The experimental results show that our method outperforms the state-of-the-art methods for dense vehicle counting, especially in human-vehicle mixed scenes.
Inductive Node Classification Based on Masked Graph Self-Encoders

Inductive node classification aims to infer the new nodes classification of the unlabeled test graph according to the labeled nodes of the training graph. Recently, the proposed Graph Convolutional Networks (GCNs) have been utilized in the inductive node classifications since it have the high processing efficiency in the representation and prediction for the graph data. However, the existing GCNs-based methods often ignore the joint dependence of the nodes labels so that the final results accuracy cannot be improved well without the structural modeling for the nodes. Therefore, this paper proposes a novel inductive node classification model, which is a GCNs model based on the conditional random field(CRF) via the special masked graph self-coding coder, named INC-MGS in short. In particular, the CRF of the proposed INC-MGS model utilizes a structural prediction approach to obtain the joint dependence of the node labels. Meanwhile, GCNs utilized in the INC-MGS model can improve the capacity of the CRF. In addition, the INC-MGS model solves the over-emphasis of the node proximity through randomly masking some edges or node paths, which improve the robustness of graph representation learning. The experimental results demonstrate the proposed INC-MGS model’s performance outperforms those of the compared models.
FedGPS: Personalized Cross-Silo Federated Learning for Internet of Things-enabled Predictive Maintenance

Predictive maintenance (PdM) has entered into a new era adopting artificial intelligence and Internet-of-Things (IoT) technologies. It is necessary for a manufacturing company to collaborate with other clients using IoT-captured production data. However, training models in a cross-silo manner is still challenging when considering data privacy. In order to tackle these challenges, a personalized cross-silo federated learning mechanism named federated global partners searching (FedGPS) is proposed. Firstly, model parameters for the participating clients are encrypted and uploaded to the central server as input. Next, FedGPS automatically determines the collaboration degrees between clients based on data distribution. After that, personalized model updates are sent back to the clients. Finally, each client conducts local updating after data decryption. The effectiveness of the FedGPS is verified in real-world cases and our method achieves 92.35% Accuracy, 98.55% Precision, 92.90% Recall, and 95.27% F1-Score comparing with other existing models from the literature.
Software Product Line for Metaverse: Preliminary Results

The Metaverse is very challenging to implement solutions for eXtended Reality applications (XR apps), due to the combination of complex concerns that should be addressed: multiple users with non-traditional input and output devices, different hardware platforms that should be addressed, forceful interactive rates, and experimental interaction techniques, among other issues. Therefore, this work aims to present a Software Product Line (SPL)-based approach to support the development of Web XR apps. More specifically, we define a features model that represents similarities and variables (domain analysis); we defined a core composed of generic and reusable software artifacts (domain design); and we developed an interface to support the instantiation of a Web XR app family, named MetaSee Features Model Editor (domain implementation). This approach integrates with a component of the MetaSEE architecture (Metaverse for Software Engineering Education). A preliminary assessment found that Features Model has conceptual consistency from the point of view of the complexity of Web XR Apps multimodal interaction. As future work, features model and artifacts will be increased with improvements and an evaluation with a significant number of participants will be performed.
A Quantifying Approach for Evaluating Differential Privacy in Deep Learning

Differential privacy is a strong notion of privacy. The privacy risk of the differentially private machine learning algorithm can be quantified by privacy loss. Deep learning makes privacy loss difficult to measure accurately, making it difficult for model designers to make trade-offs between utility and privacy. In this paper, we propose a membership inference-based evaluation method. This method estimates privacy risk of differentially private algorithms by inserting label samples into the target dataset. Our method can obtain more accurate evaluation results than previous evaluation methods by examining differential distributions in training datasets. Additionally, we found that in certain application scenarios, adding too much noise during model training could reduce privacy effectiveness. This result can guide model designers in the parameter configuration of differentially private machine learning.
Trusted Outsourced Computing Framework for Smart Contract of Permissioned Blockchain

Permissioned blockchain can provide a decentralized, tamper-evident and trusted computing environment and has been widely deployed in e-government, finance and supply chain management. Smart contracts need to be developed using a specific programming language. Limited by the architectural design of blockchain systems, smart contracts still face many challenges in performing complex computing tasks. For example, there are many matrix operations in the inference process of deep neural network models. Loading deep neural network models directly in smart contracts not only requires relatively large development costs, the inference process of deep neural network models generally consumes more computing resources. We use Intel SGX to design a flexible architecture that can outsource complex computing tasks that need to be processed in smart contracts to off-chain computing servers, while being able to use Freivalds’ Algorithm to ensure the correctness of the outsourced computing results.
CAIM: A Context-Aware Incentive Mechanism for Robust Federated Learning

Incentive mechanisms in Federated Learning (FL) systems are generally designed to motivate clients to consume resources for efficient training by providing reasonable reward compensation. However, existing incentive mechanisms for FL mostly concentrate on utility optimization, while ignoring the negative impact of unreliable clients on system performance. In reality, unreliable clients unintentionally or maliciously upload unsatisfactory models to mislead the global training of FL, which degrades the performance of FL training and impairs the efforts of other normal clients. To address the above problems, we propose a Context-Aware Incentive Mechanism (CAIM) for FL in this paper to incentivize clients and avoid unreliable clients. Specifically, based on the Thompson sampling technique, CAIM first evaluates clients’ quality and selects a set of quality-maximizing clients within a given budget. Then, by borrowing the idea of clustering, CAIM discriminates unreliable clients based on the Aggregate Nesting technique, and provides contextual guidance for subsequent training iterations. Moreover, we theoretically prove that CAIM satisfies incentive compatibility, individual rationality, and computational efficiency. Extensive experimental results show that CAIM can improve the cumulative social utility of the FL system by 3.94% and the training performance of FL by at most 54.58% in scenarios containing unreliable clients.
A Trajectory Privacy Protection Publishing Method Based on Trajectory Segment Graph Division

With the widespread coverage of location-based services (LBS), privacy protection of trajectory data is increasingly important. The starting and ending locations of trajectories often contain rich sensitive information. In order to avoid the unnecessary information loss caused by the large anonymous area of published trajectory datasets and protect semantic information of the starting and ending locations, this paper proposes a privacy protection method based on trajectory segment graph division. The method takes into account direction, shape and semantic differences of trajectory segments to measure the spatiotemporal and semantic relationships of trajectory segments. Firstly, the initial and final trajectory segment equivalence classes are constructed with different time stamps. Secondly, we propose a grid filtering method to calculate the shape distance matrix of equivalence class, meanwhile a location semantic classification tree is built to store the semantic information of locations. Finally, the graph division method is used to construct K-anonymous sets of trajectory segments and publish them. Experimental results in real datasets show that the proposed scheme can improve data availability while ensuring a certain degree of data privacy.
Static and adaptive subspace information fusion for indefinite heterogeneous proximity data

Heterogeneous data is common in many real-world machine learning applications, such as healthcare, market analysis, environmental sciences, and social media analysis. In these domains, data is often represented in different modalities and, most of the time, in non-vectorial formats, like text, images, and video. Traditional machine learning algorithms are often limited in their ability to effectively analyze and learn from such diverse data types. In this paper, we propose two approaches for such heterogeneous data analysis: static and adaptive subspace kernel fusion. The first approach is a kernel-based method extracting the essential parts of the subspace of each input modality and creating one single fused representation of the data. The second approach utilizes an adaptation step by integrating the weighting of spectral properties into the fusion process in order to improve the data’s representation with respect to a given classification task. Our proposed methods are evaluated on several multi-modal, heterogeneous data sets and demonstrate significant performance improvement compared to other methods in the field. Our results highlight the importance of fusing the underlying subspace information of heterogeneous data for achieving superior performance in machine learning tasks.
Knowledge Graph Construction from Tables in Chinese Electric Power PDF Documents

The PDF documents of the electric power standard contain a large number of tables, and the main purpose of these table layouts application is to show people more intuitive information. However, the data in the tables is not easily processed by computers and the value of the data is difficult to be exploited. In this paper, an approach is proposed to convert tabular data into RDF knowledge graphs for the power standard PDF documents. Firstly, a variety of table processing techniques are used to extract tabular data from PDF. Then tabular data is normalized and transformed into RDF data. Finally, an electricity domain ontology is constructed and the tabular data is mapped to ontology classes through table interpretation. Extensive experiments are conducted to obtain a structured RDF dataset related to electricity standards, which provides the necessary data support for intelligent knowledge services in the electricity industry.