Reviewerly/exHarmony huggingface dataset:
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement

Object detection through LiDAR-based point cloud has recently been important in autonomous driving. Although achieving high accuracy on public benchmarks, the state-of-the-art detectors may still go wrong and cause a heavy loss due to the widespread corruptions in the real world like rain, snow, sensor noise, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . Nevertheless, there is a lack of a large-scale dataset covering diverse scenes and realistic corruption types with different severities to develop practical and robust point cloud detectors, which is challenging due to the heavy collection costs. To alleviate the challenge and start the first step for robust point cloud detection, we propose the physical-aware simulation methods to generate degraded point clouds under different real-world common corruptions. Then, for the first attempt, we construct a benchmark based on the physical-aware common corruptions for point cloud detectors, which contains a total of 1,122,150 examples covering 7,481 scenes, 25 common corruption types, and 6 severities. With such a novel benchmark, we conduct extensive empirical studies on 12 state-of-the-art detectors that contain 6 different detection frameworks. Thus we get several insight observations revealing the vulnerabilities of the detectors and indicating the enhancement directions. Moreover, we further study the effectiveness of existing robustness enhancement methods based on data augmentation, data denoising, test-time adaptation. The benchmark can potentially be a new platform for evaluating point cloud detectors, opening a door for developing novel robustness enhancement methods.
Federated Matrix Factorization Recommendation Based on Secret Sharing for Privacy Preserving

Traditional recommendation systems require users to upload local data to the server to generate recommendation results. In this process, users’ privacy is easy to disclose. Federated recommendations can solve the problem of local data privacy leakage, but the intermediate computing results are not protected. The existing work mainly protects the information in this process through encryption or disturbance schemes, which will result in complex calculations or low accuracy. Also, the two schemes only protect the rating data and process parameter information, but the existence information is not protected. Aiming at the above problems, this article proposes a federated matrix factorization based on secret sharing (FMFSS) to protect users’ privacy. The parameters are randomly divided into pieces, and then, the secret sharing technology is used to transmit private information between user–user and user–server, which does not introduce additional encryption cost and ensures value privacy, process privacy, and existence privacy. In addition, this article introduces the user–item interaction value, which is transmitted to the server with gradient information. In this way, the real gradient of the user cannot be inferred from the information received by the server from all parties, but the aggregated final average parameter information is real. The experimental comparison with the existing work and the analysis of computation time show that the proposed method can ensure the accuracy of model privacy recommendations without introducing additional encryption operations.
A Transferable Generative Framework for Multi-Label Zero-Shot Learning

Multi-label zero-shot learning (MLZSL) is a more realistic and challenging task than single-label zero-shot learning (SLZSL), which aims to recognize multiple unseen classes in a single image. To adapt generative models to the MLZSL task and better recognize multiple unseen object categories in an image, this paper proposes a Transferable Generative Framework (TGF), which consists of a Multi-Label Semantic Embedding Autoencoders (SEAs), a Semantic-Related Multi-Label Feature Transformation Network (FTN) and a Multi-Label Feature Generation Networks (FGNs). First, SEAs adaptively encodes the class-level word vectors corresponding to each sample containing different number of classes into sample-level semantic embeddings with the same dimension. Then, FTN transforms global features extracted by a CNN pre-trained on single-label images into features that are semantic-related and more suitable for multi-label classification. Finally, FGNs generates both global and local features to better recognize the dominant and minor object categories in a multi-label image, respectively. Extensive experiments on three benchmark datasets show that TGF significantly outperforms state-of-the-arts. Specifically, compared with the previous best generative MLZSL method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., Gen-MLZSL), TGF improves the mAP of the ZSL (GZSL) task by 5.4% (6.9%), 20.5% (27.9%), and 2.4% (3.9%) on NUS-WIDE, Open Images, and MS-COCO datasets, respectively.
Comparing counseling outcome for college students: Metaverse and in-person approaches

ABSTRACTObjective This study compared metaverse counseling with in-person counseling, using in-person counseling as a comparison group. To achieve this, we assessed whether metaverse counseling, a novel treatment approach, is comparable to traditional in-person counseling. Method: A total of 60 participants voluntarily participated in the study. Among the participants, 28 preferred in-person counseling, whereas 32 selected metaverse counseling as their preferred treatment option. Results and Conclusion: The findings indicated no statistically significant differences in the psychological symptom change patterns between the two counseling modalities. Both metaverse and in-person counseling demonstrated a common pattern of reduced symptom levels from pre-to post-session (Metaverse counseling Cohen’s d = 1.04, In-person counseling Cohen’s d = .62), which remained stable from post-session to follow-up regardless of the chosen counseling modality. Furthermore, the study revealed that the metaverse counseling group exhibited a higher level of working alliances than the in-person counseling group. Additionally, there was a slight tendency toward higher levels of counseling satisfaction in the metaverse counseling group than in the in-person counseling group. The results of this study support the use of synchronous metaverse programs to treat college students. The implications and limitations of this study are discussed. (195 words)Keywords: metaverse counselingin-person counselingcounseling outcomevirtual realityvirtual avatar AcknowledgmentsWe would like to thank the counselors and clients who participated in this study.Disclosure StatementNo potential conflict of interest was reported by the author(s).Supplemental dataSupplemental data for this article can be accessed https://doi.org/10.1080/10503307.2023.2270139.
Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ &gt; 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences.
Organizational Commitment and Employees Turnover Intention in Saudi Public Sector: The Mediating Role of Job Satisfaction and Work Environemnt

Purpose: Organisational commitment is critical to an organization's success and growth. It leads to increased job satisfaction, lower turnover, improved performance, a more positive organisational culture, and increased customer satisfaction. This research tries to highlight the relationship between organizational commitment and turnover intention taking into account the mediation role of job satisfaction and work environment. Theoretical framework: Organizational commitment is very crucial in today working environment. Employees are more likely to be inspired and dedicated to their work when they feel committed to their organisation.(Freudenberger, 1974) .This results in increased productivity and performance. Furthermore, organizational commitment is important in employee retention. Employees that are committed to their company are less inclined to depart for other chances(Mathieu, Fabi, Lacoursiere, &amp; Raymond, 2016). This lowers turnover rates and the costs associated with hiring and training new personnel. Methodology: This is a quantitative study that employed a structured questionnaire design. This study's sample consists of 193 public officers from the Saudi public sector in the western region who were chosen at randomly. Raw data gathered via mail questionnaires. The data collected was analyzed by SmartPLS 3 to test the study's hypotheses. Findings: Finding of this study indicates that organizational commitment has a substantial and statistically significant influence on Job Satisfaction (β = 0.601, t = 8.580, p = .001), and turnover intention (β = -0.363, t = 4.737, p = .001). In addition, the work environment and job satisfaction show mediation role in the relationship between organizational commitment and turnover intention. Research, Practical &amp; Social implications: Managers at the public organizations need to apply different strategies to enhance the overall job satisfaction like giving employees a voice and involving them in problem-solving. This make them feel valued and motivated. Such practice can lead to create a conducive working environment where few people decide to quit. Employees that are committed to their organisation are more inclined to put in extra effort and go the extra mile. It is highly recommended to adopt high-performance working environment. This requires an updated human resource management techniques and practices. Originality/Value: The findings of this study indicates that ORC has a substantial and statistically significant influence on Job Satisfaction, working environment, and turnover intention. This study also finds the work environment and job satisfaction are significantly mediated the relationship between organizational commitment and turnover intention. This contributes to the current literature especially in the public organizations studies.
Natural Language Processing: Recent Development and Applications

Natural Language Processing (NLP) can be categorized into the subfields of artificial intelligence (AI) and linguistics [...]
ChatGPT as Your Vehicle Co-Pilot: An Initial Attempt

One of the most challenging problems in human-machine co-work is the gap between human intention and the machine's understanding and execution. Large Language Models (LLMs) have been showing superior abilities in solving such issue. In this paper, we design a universal framework that embeds LLMs as a vehicle “Co-Pilot” of driving, which can accomplish specific driving tasks with human intention satisfied based on the information provided. Meanwhile, a utilization workflow is defined to handle the interaction between humans and vehicles, and memory mechanism is introduced to organize the information involved in the tasks. Expert Oriented Black-Box tuning is proposed to improve the performance of the Co-Pilot without finetuning or training the LLMs. In the experiment, the Co-Pilot is applied to two different tasks, i.e., path tracking control and trajectory planning. The Co-Pilot adjusts vehicle operating conditions by selecting a proper controller or planning a certain trajectory to fit human intentions. Simulation tests are conducted to evaluate the performance and generality of the proposed module. The results show that the Co-Pilot can accomplish most of the tasks based on only natural language processing, although it is not flawless. Finally, a discussion about human-machine hybrid intelligence and further applications of LLMs in autonomous driving is made. We believe that such a framework has promising potential in further applications in the field of automous vehicles.
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification

Deep learning (DL) has been successfully applied to encrypted network traffic classification in experimental settings. However, in production use, it has been shown that a DL classifier’s performance inevitably decays over time. Re-training the model on newer datasets has been shown to only partially improve its performance. Manually re-tuning the model architecture to meet the performance expectations on newer datasets is time-consuming and requires domain expertise. We propose AutoML4ETC, a novel tool to automatically design efficient and high-performing neural architectures for encrypted traffic classification. We define a novel, powerful search space tailored specifically for the early classification of encrypted traffic using packet header bytes. We show that with different search strategies over our search space, AutoML4ETC generates neural architectures that outperform the state-of-the-art encrypted traffic classifiers on several datasets, including public benchmark datasets and real-world TLS and QUIC traffic collected from the Orange mobile network. In addition to being more accurate, AutoML4ETC’s architectures are significantly more efficient and lighter in terms of the number of parameters. Finally, we make AutoML4ETC publicly available for future research.
Continuous-variable nonclassicality certification under coarse-grained measurement

Coarse graining is a common imperfection of realistic quantum measurement, obstructing the direct observation of quantum features. Under highly coarse-grained measurement, we experimentally certify the continuous-variable nonclassicality of both Gaussian and non-Gaussian states. Remarkably, we find that this coarse-grained measurement outperforms the conventional fine-grained measurement for nonclassicality certification: it detects nonclassicality beyond the reach of the variance criterion, and furthermore, it exhibits stronger statistical significance than the high-order moments method. Our work demonstrates the usefulness of coarse-grained measurement by providing a reliable and efficient way of nonclassicality certification for quantum technologies.
DoubleStrokeNet: Bigram-Level Keystroke Authentication

Keystroke authentication is a well-established biometric technique that has gained significant attention due to its non-intrusive and continuous characteristics. The method analyzes the unique typing patterns of individuals to verify their identity while interacting with the keyboard, both virtual and hardware. Current deep-learning approaches like TypeNet and TypeFormer focus on generating biometric signatures as embeddings for the entire typing sequence. The authentication process is defined using the Euclidean distances between the new typing embedding and the saved biometric signatures. This paper introduces a novel approach called DoubleStrokeNet for authenticating users through keystroke analysis using bigram embeddings. Unlike conventional methods, our model targets the temporal features of bigrams to generate user embeddings. This is achieved using a Transformer-based neural network that distinguishes between different bigrams. Furthermore, we employ self-supervised learning techniques to compute embeddings for both bigrams and users. By harnessing the power of the Transformer’s attention mechanism, the DoubleStrokeNet approach represents a significant departure from existing methods. It allows for a more precise and accurate assessment of user authenticity, specifically emphasizing the temporal characteristics and latent representations of bigrams in deriving user embeddings. Our experiments were conducted using the Aalto University keystrokes datasets, which include 136 million keystrokes from 168,000 subjects using physical keyboards and 63 million keystrokes acquired on mobile devices from 60,000 subjects. The DoubleStrokeNet outperforms the TypeNet-based authentication system using 10 enrollment typing sequences, achieving Equal Error Rate (EER) values of 0.75% and 2.35% for physical and touchscreen keyboards, respectively.
English Teaching in Higher Vocational Colleges under the Background of the "Three Teachings" Reform

With the development and progress of vocational education in China, how to effectively carry out English teaching in vocational colleges has become a key issue for major vocational colleges. In recent years, Chinese universities have made significant progress in the context of the "three education" reform, especially in the field of college English teaching, which has provided favorable conditions for improving the quality of college English teaching. This article mainly conducts research on the design of English teaching in vocational colleges under the background of the "Three Teachings" reform. Firstly, it introduces the current situation of English in vocational colleges, and then analyzes teaching problems, the content and significance of the "Three Teachings" reform; The third is to propose a design strategy for English teaching in vocational colleges under the background of the "three education" reform.
Development of Framework to Introduce Music Education through Blended Learning during Post Pandemic Era in Chinese Universities

This article presents a preliminary mixed-method study that explores the integration of blended teaching in music education at Chinese universities in the post-pandemic era. The study investigates the perceptions and attitudes of university music teachers towards blended instruction, using qualitati
Bi-level Sampling: Improved Clients Selection in Heterogeneous Settings for Federated Learning

Client selection (a.k.a., client sampling) is one of the hot topics in Federated Learning (FL). In each communication round, selecting some clients to participate in aggregation can effectively reduce the communication overhead caused by exchanging model parameters. However, due to statistical heterogeneity in FL, selecting clients randomly may affect the performance of aggregated global models. existing approaches regarding client selection firstly cluster clients and then sample(select) some representative clients from each cluster. However, these clustering-based approaches may be either time-intensive or high complexity. To address these issues, In this paper, we introduce Bi-level Sampling, a clustering-based approach for client selection. After multinomial distribution sampling, Bi-level Sampling clusters clients based on weighted per-label mean class scores and then selects participating clients for federated learning in each round. Bi-level Sampling can lead to better client representativity and the reduced variance of the client’s stochastic aggregation weights in FL. Our approach can be integrated into typical FL frameworks. Experimental results show that, compared with state-of-the-art approaches, our approach demonstrates significantly more stable and accurate convergence behavior-getting higher test accuracy and less training time, especially in highly Non-IID settings.
Detecting Phishing Websites using Decision Trees: A Machine Learning Approach

This study emphasises the value of feature selection and preprocessing in improving model performance and demonstrates the efficiency of decision trees in identifying phishing websites. Internet users are significantly threatened by phishing websites, hence a strong detection strategy is required. The Phishing Websites Dataset from the UCI Machine Learning Repository, which contains 30 website-related features, is used in the study together with a decision tree classifier from the scikit-learn package. The dataset is preprocessed to remove invalid and missing values, and the most pertinent features are chosen for model training. 80% of the dataset is utilised to train the model, while the remaining 20% is used for testing. The findings demonstrate the decision tree classifier's precision in detecting phishing websites, scoring 95.97% accurate and showing a high true positive rate (96.64%) and a negligible (3.04%) false positive rate using the confusion matrix. This study highlights the significance of feature selection and preprocessing for optimal model performance in addition to validating the efficacy of decision trees in phishing detection. The method described here can be helpful for businesses and individuals looking to protect themselves from phishing assaults, and the given data visualisations make it easier to understand datasets and assess models.
Trusted Mobile Edge Computing: DAG Blockchain-aided Trust Management and Resource Allocation

The integration of directed acyclic graph (DAG) blockchain and mobile edge computing (MEC) has emerged as a promising means to enable computation-intensive, delay-sensitive, and secure task execution in Internet of Things (IoT) applications. However, off-chain task execution results are not credible even if the results have been recorded on the chain, since blockchain cannot extend the trust of on-chain data to off-chain. To make the off-chain and on-chain trust consistent, we first develop a trusted MEC (T-MEC) framework by employing a DAG blockchain-aided decentralized trust management (DAG-DTM) mechanism. Specifically, DAG-DTM evaluates the off-chain trust of edge nodes according to the quality of task execution results, and the trust can be further verified off the chain by any edge node under the same trust management rule. Moreover, the approval time for recording the execution result of the edge node on the chain is positively correlated with the verified off-chain trust, which can further promote on-chain transaction security of trusted edge node. Second, we jointly optimize the bandwidth and computation resource allocation to minimize the system latency that consists of off-chain task execution delay and on-chain transaction confirmation delay. Numerical results compare system latency and security performance between the optimized T-MEC and the benchmark schemes. In particular, the optimized T-MEC can achieve a 33.12% gain of computation delay and a 10.19% gain of system latency at an affordable cost of transaction confirmation delay (i.e., 3.21%) over T-MEC, while meeting the requirements of off-chain task execution latency and on-chain transaction security simultaneously.