Reviewerly/exHarmony huggingface dataset:
Few-Shot Anomaly Detection in Text with Deviation Learning

Most current methods for detecting anomalies in text concentrate on constructing models solely relying on unlabeled data. These models operate on the presumption that no labeled anomalous examples are available, which prevents them from utilizing prior knowledge of anomalies that are typically present in small numbers in many real-world applications. Furthermore, these models prioritize learning feature embeddings rather than optimizing anomaly scores directly, which could lead to suboptimal anomaly scoring and inefficient use of data during the learning process. In this paper, we introduce FATE, a deep few-shot learning-based framework that leverages limited anomaly examples and learns anomaly scores explicitly in an end-to-end method using deviation learning. In this approach, the anomaly scores of normal examples are adjusted to closely resemble reference scores obtained from a prior distribution. Conversely, anomaly samples are forced to have anomalous scores that considerably deviate from the reference score in the upper tail of the prior. Additionally, our model is optimized to learn the distinct behavior of anomalies by utilizing a multi-head self-attention layer and multiple instance learning approaches. Comprehensive experiments on several benchmark datasets demonstrate that our proposed approach attains a new level of state-of-the-art performance (Our code is available at https://github.com/arav1ndajay/fate/ ).
Blockchain and Customer Experience

Technological entrepreneurship has considerably aided the commercialization of internet technology by transferring it to the web; internet technology has changed traditional buying practices. The idea of blockchain technology was created when digital signatures were first used in the 1990s to confirm that signed documents had not been altered. The chapter comes to a conclusion by emphasizing the crucial role that blockchains serve in maintaining a safe and decentralized record of transactions. The innovation of a blockchain resides in its capacity to establish trust without the involvement of a reliable third party and to ensure the safety and integrity of a record of data.
Depth and Width Adaption of DNN for Data Stream Classification with Concept Drifts

To handle data stream classification with concept drifts, recent studies have shown that a continuously evolving network structure can achieve better performance. However, firstly, they only change one hidden node at a time, which is not enough to alleviate underfitting or overfitting of network model, leading to model’s inability to fit data well. Secondly, during the growth process of the network, they did not consider reducing hidden layers, which would affect the learning ability of deep neural network models. To overcome these shortcomings, an adaptive neural network structure (ANSN) is proposed to handle data stream classification with concept drifts. ANSN has a completely open structure, its network structure, depth, and width can evolve automatically in online mode. The experimental results on ten popular data stream datasets show that the proposed ANSN outperforms the comparison methods. The codes of the proposed algorithm is available on https://gitee.com/ymw12345/ansn.git .
Regularized Loose Coupled Deep Non-negative Basis Matrix Factorization for Low-Resolution Face Image Recognition

Low-resolution face image recognition is a research focus in computer vision and pattern recognition. In this paper, the potential relationship between matched high-resolution face and low-resolution face images is considered to propose the regularized loose coupled deep non-negative basis matrix factorization (RLCDNBMF) architecture, which uses regularized deep non-negative basis matrix factorization for high-resolution images and couples its coefficient matrix with the coefficient matrix obtained from low-resolution non-negative factorization, so that the discriminative information of high-resolution face images can be fully utilized to guide the non-negative matrix factorization of low-resolution face images. Comparative experiments are conducted on three mainstream face databases, and the experimental results show that the method can improve the accuracy of low-resolution face recognition.
Weighted Consistent Multi-view Discriminant Analysis in Unreliable Labeling Environment

Multi-view discriminant analysis method (MvDA) is an effective supervised multi-view learning method. However, in practical multi-view learning application scenarios, there may be the problem that the sample label information from some viewpoints is unreliable. In the MvDA method, the information of all views is considered to have equal reliability. The data information in views containing unreliable label information may make it difficult for MvDA to learn accurate common features among data from different views. Therefore, in this paper, a weighted consistent multi-view discriminant analysis (WMvDA-VC) is proposed for the multi-view learning task in unreliable labeling environment. By considering the data characteristics under different views, the information from different views is assigned different reliability weights. Using the weighted scatter matrices, multiple projection matrices are learned. Then the data from different views are projected into the same common projection space. In the common subspace, dissimilar samples in different views are dispersed as much as possible and similar samples are aggregated as much as possible. The impact of data from views containing unreliable labels on the multi-view learning performance is reduced by adjusting the weights of the views with unreliable labels. The method is experimented on four widely used datasets. The experimental results show that the proposed WMvDA-VC exhibits excellent performance in the classification recognition task with unreliably labeled environment.
Vehicle target detection method based on improved YOLO V3 network model

For the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target vehicles in aerial photography. The optimization and adjustment of the anchor box and the improvement of the network residual module have improved the small target detection effect of the algorithm. Furthermore, the introduction of the rectangular prediction frame with orientation angles into the model of this article can improve the vehicle positioning efficiency of the algorithm, greatly reduce the problem of wrong detection and missed detection of vehicles in the model, and provide ideas for solving related problems. Experiments show that the accuracy rate of the improved algorithm model is 89.3%. Compared to the YOLO V3 algorithm, it is improved by 15.9%. The recall rate is improved by 16%, and the F1 value is also improved by 15.9%, which greatly increased the detection efficiency of aerial vehicles.
Text-Based Person re-ID by Saliency Mask and Dynamic Label Smoothing

The current text-based person re-identification (re-ID) models tend to learn salient features of image and text, which however is prone to failure in identifying persons with very similar dress, because their image contents with observable but indescribable difference may have identical textual description. To address this problem, we propose a saliency mask based re-ID model to learn non-salient but highly discriminative features, which can work together with the salient features to provide more robust pedestrian identification. To further improve the performance of the model, a dynamic label smoothing based cross-modal projection matching loss (named CMPM-DS) is proposed to train our model, and our CMPM-DS can adaptively adjust the smoothing degree of the true distribution. We conduct extensive ablation and comparison experiments on two popular re-ID benchmarks to demonstrate the efficiency of our model and loss function, and improving the existing best R@1 by 0.33% on CUHK-PEDE and 4.45% on RSTPReID.
Temporal Attention for Robust Multiple Object Pose Tracking

Estimating the pose of multiple objects has improved substantially since deep learning became widely used. However, the performance deteriorates when the objects are highly similar in appearance or when occlusions are present. This issue is usually addressed by leveraging temporal information that takes previous frames as priors to improve the robustness of estimation. Existing methods are either computationally expensive by using multiple frames, or are inefficiently integrated with ad hoc procedures. In this paper, we perform computationally efficient object association between two consecutive frames via attention through a video sequence. Furthermore, instead of heatmap-based approaches, we adopt a coordinate classification strategy that excludes post-processing, where the network is built in an end-to-end fashion. Experiments on real data show that our approach achieves state-of-the-art results on PoseTrack datasets.
WebAR as a Mediation Tool Focused on Reading and Understanding of Technical Drawings Regarding Tailor-Made Projects for the Scenographic Industry

Among the leading immersive technologies, augmented reality is one of the most promising and empowering for supporting designers in production environments. This research investigates the application of mobile augmented reality, based on the Web, as a mediation tool focused on cognitive activities of reading and understanding of technical drawings in the production and assembly of tailor-made projects of the scenographic industry. In this context, the research presents a method to use WebAR to improve the reading of technical drawings, seeking efficiency in the visualization of models and the exchange of information between professionals involved in the processes of design, production, and assembly of products, in the scope of scenography. This mediation tool was developed using Web AR platforms, compatible with native libraries (ARCore and ARKit) to ensure, first, compatibility with commonly used devices that workers or businesses can access, and second, to leverage hybrid tracking techniques that combine vision and sensors to improve the reliability of augmented reality viewing. The proposed solution adopts multiple tracking and navigation techniques in order to expand Space Skills components to provide greater exploratory freedom to users. The research process took place in light of the Design Science Research Methodology and the DSR-Model, since it aimed to develop a solution to a practical problem, as well as to produce knowledge from this process. Field experiments were conducted in two real companies, with end users on their respective mobile devices, in order to evaluate usability and behavioral intent, through the Acceptance, Intent, and Use of Technology questionnaires and perceived mental workload, NASA-TLX. The experimental results show that the adoption of this tool reduces the cognitive load in the process of reading technical drawings and project understanding. In general, its usability and intent to use provided significant levels of satisfaction, being positively accepted by all participants involved in the study.
How Effective are SETA Programs Anyway: Learning and Forgetting in Security Awareness Training

Prevalent security threats caused by human errors necessitate security education, training, and awareness (SETA) programs in organizations. Despite strong theoretical foundations in behavioral cybersecurity, field evidence on the effectiveness of SETA programs in mitigating actual threats is scarce. Specifically, with a broad range of cybersecurity knowledge crammed into in a single SETA session, it is unclear how effective different types of knowledge are in mitigating human errors in a longitudinal setting. his study investigates how knowledge gained through SETA programs affects human errors in cybersecurity to fill the longitudinal void. In a baseline experiment, we establish that SETA programs reduce phishing susceptibility by 50%, whereas the training intensity does not affect the rate. In a follow-up experiment, we find that SETA programs can increase employees’ cybersecurity knowledge by 12-17%, but the increment wears off within a month. Furthermore, technical-level knowledge decays faster than application-level knowledge. The longer “shelf-life” of application-level knowledge explains why training intensity makes no difference within a month. This study reveals a (relatively) more effective component of SETA programs and cast doubts on the overall effectiveness of SETA programs in the long run.
Deep Learning Aided Low Complex Breadth-first Tree Search for MIMO Detection

In this paper, we propose a deep learning based breadth-first sphere decoding (SD) scheme to reduce the detection complexity for multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the DenseNet-based deep neural network (DN-DNN) to provide the pruning threshold for SD at each layer. Then, we develop modified number-based SD (MNSD) to reduce the complexity of SD by constraining the number of visited nodes at each layer with the output of DN-DNN. We use a distance-based SD (DSD) to further reduce the complexity of MNSD by constraining the accumulated distance at each layer with the output of DN-DNN. Compared with the traditional M-best SD with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> = 16, the proposed MNSD achieves similar performance but reduces about 25% complexity for QPSK modulation; the proposed DSD has better performance with up to 75% complexity reduction at the high SNR region for 16QAM.
Fuzzy Inference Attention Module for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge acquired from the labeled source domain to the unlabeled target domain. However, the quality of samples can vary greatly. While partial samples are dominated by highquality domain-invariant class-related information, others may only contain irrelevant domain-specific information or useless random noise. Treating all samples equally may lead to negative transfer, significantly impairing the performance. To address the issue of varying sample quality, we propose an attention module to emphasize the samples that are most suitable for transfer. Within the attention module, we have designed a fuzzy inference system to assess the quality of data based on its class and domain information. Such a fuzzy inference attention module (FIA) demonstrates strong interpretability due to its consideration of the fuzzy nature inherent in class and domain information within the data. FIA also has high flexibility and extensibility as the rule base can be easily adjusted by expert knowledge. More importantly, FIA does not use any parameters requiring training and has a low overhead. This makes it fast and applicable to most existing unsupervised domain adaptation methods. The experiments on several benchmark datasets prove that FIA can bring significant improvement to existing methods
Automated Labelling of Traffic Police Data Based on Weak Supervision

In the context of autonomous driving, the effective recognition and understanding of traffic police gestures is crucial for safe driving on the road. Pedestrians’ body movements affect the driving system’s understanding of traffic police gestures; therefore, the localization and recognition of traffic police is key to the understanding of traffic police gesture. With the development of deep learning technology, a satisfactory traffic police localization and recognition model requires a huge amount of accurately labelled datasets, but there are no publicly available relevant datasets, and the cost of accurate labelling of datasets is extremely high, limiting the performance of the model. To address this problem, this paper proposes a High-Quality Proposals generation algorithm based on weak supervision (HQP). HQP improves the quality of region proposal generation by effectively enhancing the classification ability of a student model through guidance from a pre-trained teacher model. To compensate for the lack of localization information in image-level annotation data, HQP integrates gradient-weighted class activation maps to improve the model’s localization ability. HQP effectively reduces the workload of accurate data labelling and completes the automated labelling of data on traffic police localization and positioning by combining manual fine-tuning methods.
Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants

In this study, we investigate distributed optimization through the lens of linearly constrained optimization problems and analyze the loopless projection stochastic approximation (LPSA) method. LPSA incorporates a probabilistic projection, denoted as p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> , during the n-th iteration to ensure random feasibility. We set ${p_n} \propto \eta _n^\beta $, where η <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> represents the step size and β is a tuning parameter. Our previous research demonstrates that LPSA exhibits a fascinating bias-variance trade-off through diffusion approximation in the asymptotic regime. Specifically, for β < 0.5, the bias becomes degenerate, while for β > 0.5, the bias dominates the variance. In this work, we investigate the intricate scenario where β = 0.5 and discover that the last iterate, after appropriate scaling, weakly converges to a biased Gaussian distribution. As a result, we provide a comprehensive asymptotic analysis of LPSA and a complete characterization of phase transitions. We observe that a non-zero bias leads to slow convergence. Motivated by this observation, we propose a debiased version of LPSA, called Debiased LPSA (DLPSA), which effectively reduces projection complexity compared to the vanilla LPSA.
Visual Imagery Study of Micro SLR Camera Modeling Based on Triangular Fuzzy Theory

Micro SLR cameras have played a huge role in the 5G era and the era of self-publishing short videos with online videos and photos as the communication carriers. Nowadays, business is an era centered on consumer needs, and the shape of micro SLR cameras has a huge impact on the value of micro SLR cameras. This study aims to explore the visual imagery evaluation of a wide range of miniature SLR cameras. The study investigates the top 12 micro SLR cameras in China, and uses the semantic difference method (SD method) to screen the visual imagery adjective clusters of micro SLR cameras, and then uses factor analysis and triangular fuzzy theory to derive the visual imagery evaluation values of these 12 micro SLR cameras. The results of the study show the differences in the evaluation of different shaped miniature SLR cameras are larger in the smart and novel visual imagery, and smaller in the detailed visual imagery. The results of the study are beneficial for designers to design miniature SLR cameras with different experience sense.
Research Trend Topic Area on Mobile Anchor Localization

Localization in a dynamic environment is one of the challenges in WSN localization involving dynamic sensor nodes or anchor nodes. Mobile anchors can be an efficient solution for the number of anchors in a 3-dimensional environment requiring more local anchors. The reliability of a localization system using mobile anchors is determined by various parameters such as energy efficiency, coverage, computational complexity, and cost. Various methods have been proposed by researchers to build a reliable mobile anchor localization system. This certainly shows the many research opportunities that can be carried out in mobile anchor localization. The many opportunities in this topic will be very confusing for researchers who want to research in this field in choosing a topic area early. However, until now there is still no paper that discusses systematic mapping studies that can provide information on topic areas and trends in the field of mobile anchor localization. A systematic Mapping Study (SMS) was conducted to determine the topic area and its trends, influential authors, and produce modeling topics and trends from the resulting modeling topics. This SMS can be a solution for researchers who are interested in research in the field of mobile anchor localization in determining the research topics they are interested in for further research. This paper gives information on the mobile anchor research area, the author who has influenced mobile anchor localization research, and the topic modeling and trend that potentially promissing research in the future. The SMS includes a chronology of publications from 2017-2022, bibliometric co-occurrence, co-author analysis, topic modeling, and trends. The results show that the development of mobile anchor localization publications is still developing until 2022. There are 10 topic models with 6 of them included in the promising topic. The results of this SMS can be used as preliminary research from the literacy stage, namely Systematic Literature Review (SLR).
Title Pending 755

Objective: This paper details a pilot project to establish a baseline for current data management planning activities and offers more targeted data management planning training to researchers.Methods: The authors incorporated a collaborative transdisciplinary approach, leading to the development and delivery of a series of surveys to gain accurate feedback about current workflows, policy adherence, and identifying knowledge gaps.Results: Using formal survey results and informal feedback from researcher interactions to inform targeted training sessions and materials results in a more productive and collaborative experience for researchers and leads to more complete and structured data management plans.Conclusions: Understanding researchers’ current practices and needs is crucial to developing effective training and resources to help improve data management planning and workflows.
APPLYING GENDER DIFFERENCES IN PHONOLOGICAL AWARENESS TO ENHANCE STUDENTS’ READING ABILITY

Phonological awareness is the first stage in enhancing kids' reading skills. Phonological awareness provides the foundation for youngsters to associate written letters with their voices, which will help them understand the reading process. Primary school reading abilities provide the groundwork for subsequent reading success. The research technique that utilized to conduct study on phonological awareness and reading ability of male and female primary school pupils is a research method that combines quantitative and qualitative data, commonly known as the Mixed-Method. To obtain quantitative data, this method employs one experimental group, whereas to obtain qualitative data, classroom observation, questionnaires, and interviews conducted on experimental. The subjects of this study were low-grade male and female pupils at primary schools in Cirebon. A basic random sampling procedure is used to determine the samples. There were four types of data gathering procedures used in this study: phonological awareness, observation, questionnaires, and interviews. This study leads to several findings. First, the findings are broadly consistent with previous research in the field of PA in primary children. The study discovered a consistent developmental trajectory in the majority of the PA tasks in favor of primary children. In addition, females outperformed males in some PA subtests. In order to eliminate obstacles to advancing their reading abilities, elementary students must be trained on PA exercises.