Reviewerly/exHarmony huggingface dataset:
Face Mask Wearing Detection Based on YOLOv5

Abstract In recent years, COVID-19 has swept the world, and people in crowded public places are usually large. In order to reduce the risk of virus transmission, stop the spread of the epidemic and reduce cross-infection, wearing masks correctly has become an important measure to prevent the virus. Aiming at the time-consuming and laborious situation of wearing masks manually, this paper proposes a mask wearing detection method based on yolov5. The input layer is mainly used for mosaic data enhancement, that is, adaptive anchor box and adaptive image scaling technology; Yolov5 in backbone mainly adopts focus and CSP (cross stage partial) structure; The neck layer adopts spp (spatial pyramid pooling) module and FPN (feature pyramid networks) + pan (pixel aggregation network) structure; The output mainly adopts ciou for the bounding box loss function_Loss is the average index of NMS (non maximum suppression). This method uses 8000 preprocessed images as the data set and trains 200 epochs to get the final model. The algorithm visually displays the training and test results through tensor board, and inputs the pictures captured by the camera into the model to detect whether the face wears a mask. The accuracy, recall and mean accuracy (map) of the algorithm on the test set are 94.8%, 89.0% and 93.5% respectively, which are higher than the detection results of yolov3 and yolov4 algorithms.
Improved Random Forest Fault Diagnosis Model Based on Fault Ratio

Abstract With the rapid development of information technology, the informatization, integration and complexity of more and more large equipment are increasing day by day, so it is very important to carry out fault diagnosis for such complex equipment. In the traditional way, expert system technology is usually used for fault diagnosis of complex equipment. However, with the increasing of equipment data information, traditional methods cannot solve the fault diagnosis requirements in the case of a large amount of data. Therefore, data-driven fault diagnosis method can solve this problem, The carrier of data-driven fault diagnosis is a large amount of engineering data, and its focus is to explore new methods of fault diagnosis from a large amount of historical data. In this paper, the classical random forest algorithm is selected as the basic model, and aiming at the imbalance of complex equipment data, the improved random forest voting mechanism based on the fault ratio is proposed to optimize the model, which makes the final model diagnosis accuracy more than 95%, and has good application value.
Super-resolution Image Reconstruction Based on Double Regression Network Model

Abstract By learning nonlinear mapping functions from low resolution (LR) images to high resolution (HR) images, deep neural networks show good performance in image super-resolution (SR). However, the existing SR approach has two potential limitations. First, learning the mapping function from LR to HR images is usually an ill-conditioned problem, since there exist an infinite number of HR images that can be down-sampled to the same LR image. Thus, the space of possible functions can be very large, making it difficult to find a good solution. Second, paired LR-HR data may not be available in real-world applications, and the underlying degradation method is often unknown. For this more general case, existing SR models tend to generate adaptive problems and produce poor performance. To solve the above problem, we propose a dual regression scheme that reduces the space of possible functions by introducing additional constraints on LR data.
Fine-grained Recognition of Ships Under Complex Sea Conditions

Abstract For the traditional deep learning cannot solve the fog, coastal background interference, and the difficulty of small ships recognition, a multi-scale deep learning training model is proposed in this paper. Based on Faster R-CNN, this paper uses guided filtering to remove fog, as well as combined with negative sample enhancement learning to train the model, thus solving recognition of ship in complex sea conditions. And with multi-scale training strategy, the multi-scale ship samples are produced and sent to the network for training, so as to solve the problem of small target recognition. The experimental results show that compared with the Faster R-CNN, the precision and recall of our method increase by 6.43% and by 4.68% respectively. It solves the difficulty of ships recognition under complex sea conditions and small ship recognition that cannot be solved by traditional deep learning methods, the trained model has good generalization ability and robustness.
The use of APE in the Problem-Based Learning Process in English subjects

Every effective, inventive, and creative educational procedure will result in high-caliber students. Hence, every instructor should be able to instruct using a good, student-friendly learning model so that when learning is implemented, pupils can do so successfully. Apart from selecting the learning model to be utilized, the teacher must have innovative ideas and methods for creating instructional materials that will be delivered to students, especially in English content, if they are to follow the learning process with ease. One of these involves employing educational media. Students will be able to follow the learning process with excitement and fun if appropriate learning materials are used, which will prevent them from becoming disinterested. With the "APE media Secret Food Chain Box," this problem-based learning can be carried out. Problem-based learning, where problems from a lesson are used to help students build their problem-solving skills and also.
Faster goal-oriented shortest path search for bulk and incremental detailed routing

Abstract We develop new algorithmic techniques for VLSI detailed routing. First, we improve the goal-oriented version of Dijkstra’s algorithm to find shortest paths in huge incomplete grid graphs with edge costs depending on the direction and the layer, and possibly on rectangular regions. We devise estimates of the distance to the targets that offer better trade-offs between running time and quality than previously known methods, leading to an overall speed-up. Second, we combine the advantages of the two classical detailed routing approaches—global shortest path search and track assignment with local corrections—by treating input wires (such as the output of track assignment) as reservations that can be used at a discount by the respective net. We show how to implement this new approach efficiently.
Alih Kode dan Campur Kode Guru-Guru Bahasa Arab Pada Grup Whatsapp Majma’ Al-Lughah Al-‘Arabiyyah

Objective: This study aims to mention examples of code switching or code mixing for Arabic teachers in the Majma' Al-Lughah Al-'Arabiyyah Whatsapp group, explain the meaning of code switching or code mixing used, and mention the factors that encourage the use of code switching and code mixing. Design/Methodology: This study uses a qualitative approach and a descriptive method. The researcher collected chat data from the Majma' Al-Lughah Al-Arabiyyah WhatsApp group related to code switching and code mixing, then performed data classification, analysis, and conclusion. Findings: Researchers found 22 code mixing, and 6 code switching. While the factors that cause the use of code mixing and code switching are seven, namely: (1) Arabic speaking habits, (2) speakers do not master Indonesian, (3) limited meaning, (4) as a form of respect, (5) use of more popular terms, (6) enhance a sense of humor, and (7) speech situations. The most dominant factor is the speaker's habit of speaking Arabic and the speaker is not fluent in Indonesian, and the least dominant factor is the factor of increasing prestige. Research Limitations/Implications: This research is limited to Arabic teachers at the Majma' Lughah Al-Arabiyyah institution, with a span of only two months. Even so, this research has been able to provide an overview of the use of code switching and code mixing in the Majma' Al-Lughah Al-Arabiyyah WhatsApp group as a whole.
DSFFNet: Dual-Side Feature Fusion Network for 3D Pose Transfer

To solve the problem of pose distortion in the forward propagation of pose features in existing methods, this pa-per proposes a Dual-Side Feature Fusion Network for pose transfer (DSFFNet). Firstly, a fixed-length pose code is extracted from the source mesh by a pose encoder and combined with the target vertices to form a mixed feature; Then, a Feature Fusion Adaptive Instance Normalization module (FFAdaIN) is designed, which can process both pose and identity features simultaneously, so that the pose features can be compensated in layer-by-layer for-ward propagation, thus solving the pose distortion problem; Finally, using the mesh decoder composed of this module, the pose are gradually transferred to the target mesh. Experimental results on SMPL, SMAL, FAUST and MultiGarment datasets show that DSFFNet successfully solves the pose distortion problem while maintaining a smaller network structure with stronger pose transfer capability and faster convergence speed, and can adapt to meshes with different numbers of vertices. Code is available at https://github.com/YikiDragon/DSFFNet
Understanding the Country-Level Security of Free Content Websites and their Hosting Infrastructure

This paper examines free content websites (FCWs) and premium content websites (PCWs) in different countries, comparing them to general websites. The focus is on the distribution of malicious websites and their correlation with the national cyber security index (NCSI), which measures a country's cyber security maturity and its ability to deter the hosting of such malicious websites. By analyzing a dataset comprising 1,562 FCWs and PCWs, along with Alexa's top million websites dataset sample, we discovered that a majority of the investigated websites are hosted in the United States. Interestingly, the United States has a relatively low NCSI, mainly due to a lower score in privacy policy development. Similar patterns were observed for other countries With varying NCSI criteria. Furthermore, we present the distribution of various categories of FCWs and PCWs across countries. We identify the top hosting countries for each category and provide the percentage of discovered malicious websites in those countries. Ultimately, the goal of this study is to identify regional vulnerabilities in hosting FCWs and guide policy improvements at the country level to mitigate potential cyber threats.
Robust 3D-aware Object Classification via Discriminative Render-and-Compare

In real-world applications, it is essential to jointly estimate the 3D object pose and class label of objects, i.e., to perform 3D-aware classification.While current approaches for either image classification or pose estimation can be extended to 3D-aware classification, we observe that they are inherently limited: 1) Their performance is much lower compared to the respective single-task models, and 2) they are not robust in out-of-distribution (OOD) scenarios. Our main contribution is a novel architecture for 3D-aware classification, which builds upon a recent work and performs comparably to single-task models while being highly robust. In our method, an object category is represented as a 3D cuboid mesh composed of feature vectors at each mesh vertex. Using differentiable rendering, we estimate the 3D object pose by minimizing the reconstruction error between the mesh and the feature representation of the target image. Object classification is then performed by comparing the reconstruction losses across object categories. Notably, the neural texture of the mesh is trained in a discriminative manner to enhance the classification performance while also avoiding local optima in the reconstruction loss. Furthermore, we show how our method and feed-forward neural networks can be combined to scale the render-and-compare approach to larger numbers of categories. Our experiments on PASCAL3D+, occluded-PASCAL3D+, and OOD-CV show that our method outperforms all baselines at 3D-aware classification by a wide margin in terms of performance and robustness.
A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents

Many real-world applications (e.g., note taking, search) require extracting a sentence or paragraph from a document and showing that snippet to a human outside of the source document. Yet, users may find snippets difficult to understand as they lack context from the original document. In this work, we use language models to rewrite snippets from scientific documents to be read on their own. First, we define the requirements and challenges for this user-facing decontextualization task, such as clarifying where edits occur and handling references to other documents. Second, we propose a framework that decomposes the task into three stages: question generation, question answering, and rewriting. Using this framework, we collect gold decontextualizations from experienced scientific article readers. We then conduct a range of experiments across state-of-the-art commercial and open-source language models to identify how to best provide missing-but-relevant information to models for our task. Finally, we develop QaDecontext, a simple prompting strategy inspired by our framework that improves over end-to-end prompting. We conclude with analysis that finds, while rewriting is easy, question generation and answering remain challenging for today's models.
Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric

Existing vehicle re-identification methods mainly rely on the single query, which has limited information for vehicle representation and thus significantly hinders the performance of vehicle Re-ID in complicated surveillance networks. In this paper, we propose a more realistic and easily accessible task, called multi-query vehicle Re-ID, which leverages multiple queries to overcome viewpoint limitation of single one. Based on this task, we make three major contributions. First, we design a novel viewpoint-conditioned network (VCNet), which adaptively combines the complementary information from different vehicle viewpoints, for multi-query vehicle Re-ID. Moreover, to deal with the problem of missing vehicle viewpoints, we propose a cross-view feature recovery module which recovers the features of the missing viewpoints by learnt the correlation between the features of available and missing viewpoints. Second, we create a unified benchmark dataset, taken by 6142 cameras from a real-life transportation surveillance system, with comprehensive viewpoints and large number of crossed scenes of each vehicle for multi-query vehicle Re-ID evaluation. Finally, we design a new evaluation metric, called mean cross-scene precision (mCSP), which measures the ability of cross-scene recognition by suppressing the positive samples with similar viewpoints from same camera. Comprehensive experiments validate the superiority of the proposed method against other methods, as well as the effectiveness of the designed metric in the evaluation of multi-query vehicle Re-ID.
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation

Self-supervised learning general-purpose audio representations have demonstrated high performance in a variety of tasks. Although they can be optimized for application by fine-tuning, even higher performance can be expected if they can be specialized to pre-train for an application. This paper explores the challenges and solutions in specializing general-purpose audio representations for a specific application using speech, a highly demanding field, as an example. We enhance Masked Modeling Duo (M2D), a general-purpose model, to close the performance gap with state-of-the-art (SOTA) speech models. To do so, we propose a new task, denoising distillation, to learn from fine-grained clustered features, and M2D for Speech (M2D-S), which jointly learns the denoising distillation task and M2D masked prediction task. Experimental results show that M2D-S performs comparably to or outperforms SOTA speech models on the SUPERB benchmark, demonstrating that M2D can specialize in a demanding field. Our code is available at: https://github.com/nttcslab/m2d/tree/master/speech
Abstraction of Nondeterministic Situation Calculus Action Theories -- Extended Version

We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language. We assume that we have both an abstract and a concrete nondeterministic basic action theory, and a refinement mapping which specifies how abstract actions, decomposed into agent actions and environment reactions, are implemented by concrete ConGolog programs. This new setting supports strategic reasoning and strategy synthesis, by allowing us to quantify separately on agent actions and environment reactions. We show that if the agent has a (strong FOND) plan/strategy to achieve a goal/complete a task at the abstract level, and it can always execute the nondeterministic abstract actions to completion at the concrete level, then there exists a refinement of it that is a (strong FOND) plan/strategy to achieve the refinement of the goal/task at the concrete level.
Screening outcome for interpretation by the first and second reader in a population-based mammographic screening program with independent double reading

Background Double reading of screening mammograms is associated with a higher rate of screen-detected cancer than single reading, but different strategies exist regarding reader pairing and blinding. Knowledge about these aspects is important when considering strategies for future use of artificial intelligence in mammographic screening. Purpose To investigate screening outcome, histopathological tumor characteristics, and mammographic features stratified by the first and the second reader in a population based screening program for breast cancer. Material and Methods The study sample consisted of data from 3,499,048 screening examinations from 834,691 women performed during 1996–2018 in BreastScreen Norway. All examinations were interpreted independently by two radiologists, 272 in total. We analyzed interpretation score, recall, and cancer detection, as well as histopathological tumor characteristics and mammographic features of the cancers, stratified by the first and second readers. Results For Reader 1, the rate of positive interpretations was 4.8%, recall 2.3%, and cancer detection 0.5%. The corresponding percentages for Reader 2 were 4.9%, 2.5%, and 0.5% ( P &lt; 0.05 compared with Reader 1). No statistical difference was observed for histopathological tumor characteristics or mammographic features when stratified by Readers 1 and 2. Recall and cancer detection were statistically higher and histopathological tumor characteristics less favorable for cases detected after concordant positive compared with discordant interpretations. Conclusion Despite reaching statistical significance, mainly due to the large study sample, we consider the differences in interpretation scores, recall, and cancer detection between the first and second readers to be clinically negligible. For practical and clinical purposes, double reading in BreastScreen Norway is independent.
Development of An Android-Based Visual Implementation of Student Project Allocation System

Abstract Technology is everyplace we go in moment's life. So, scholars of seminaries or sodalities, or universities bear an operation that supports smartphones to get all types of information related to examination, lecture notes, placement, systems regarding announcement, events, transportation, etc. Rather of calling systems because nearly all mobile druggies have smartphones currently. This being system takes a pupil list and allocates administrators to scholars. Using a manual system in the management and allocation of projects to students is characterized by many problems, including the Inability of the project Guide to know that a title has been approved already for a student. Difficulty and inappropriate documentation of allocated project topics. Ineffective in entering, updating, and retrieving records of allocated projects. Difficulty in accessing the project Guide for approval of the topic. Duplication in project topics approved for students. We designed an operation to attain the demand of scholars. The main ideal of the pupil design allocation system is to make a system that will give information for each pupil. Projects can efficiently be allocated to students without delay, and topic conflict between students in the same department will not arise here. Pupil updates can be fluently penetrated if the database system is enhanced. This design will give a fruitful way to manage data at a low cost. The Student Project Allocation contains colorful options similar as login/logout, viewing and streamlining data, etc. It'll be secure. Data can be used by only those with an id and word while maintaining the data.
Efficient optimization algorithms for various machine learning tasks, including classification, regression, and clustering

The research on efficient optimization algorithms for machine learning is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models. Firstly, the proposed research focuses on developing more efficient algorithms for large-scale deep learning. While there have been many optimization algorithms proposed for deep learning, the proposed research aims to develop new algorithms that can handle the complexity and scale of these models and improve their efficiency. Secondly, the proposed research aims to explore the effectiveness of optimization algorithms for different types of machine learning tasks. While many studies have focused on deep learning, the proposed research aims to evaluate the effectiveness of optimization algorithms for other types of machine learning tasks, such as reinforcement learning, unsupervised learning, and semi-supervised learning. Thirdly, the proposed research aims to develop optimization algorithms that can handle noisy and incomplete data, which is a significant challenge for machine learning models. The proposed research aims to develop algorithms that can handle noisy and incomplete data and improve the accuracy of machine learning models. Fourthly, the proposed research aims to develop optimization algorithms that can handle non-convex objective functions. While some optimization techniques have been proposed for non-convex optimization, the proposed research aims to develop new algorithms that can handle these functions and improve the accuracy of machine learning models. The proposed research aims to investigate the trade-off between optimization efficiency and model performance. While previous research has explored this trade-off to some extent, the proposed research aims to develop algorithms that can balance these factors and optimize both efficiency and performance. The proposed research is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models for various tasks, including classification, regression, and clustering. By developing new algorithms and evaluating their effectiveness for different types of machine learning tasks, the proposed research can advance the field of machine learning and improve the accuracy and efficiency of machine learning models.