Reviewerly/exHarmony huggingface dataset:
코퍼스 기반 학습, 오류수정, 쓰기학습, 동사구습득

The purpose of this paper is to investigate the effects of corpus-based error correction of writing on college students’ acquisition of verb phrases. For this study, 26 college students were chosen as participants and instructed to correct the errors shown in their writing using concordance program. In addition, the students completed the pre- and post-verb tests measuring the students’ acquisition of verb phrases.&#x0D; The results of this study are as follows: First, it was shown that verb phrase usage frequency rate was the highest in S+V+O pattern. In terms of verb phrase error frequency, it was the highest in S+V+O pattern and the second highest was in S+V+A pattern in both groups. The most frequent error types of verb phrase in the two patterns are presumed to result from the incorrect use of prepositions with verbs. Second, there was a significant difference between the two groups with regards to verb phrase error correction ratio according to the 7 sentence structure patterns. Higher error correction ratio indicates that there is improvement in accuracy, which leads to a better writing ability. Third, the pre- and post- verb tests showed that there was a 9% more improvement in the experimental group(12%) than in the control group(3%). In conclusion, the effects of corpus-based error correction contributed to the improvement of students’ acquisition of verb phrases. Finally, the data shows that when speaking in English students are most likely to use certain types of sentence patterns; S+V+O pattern, S+V+C pattern and S+V+A pattern. Considering those data results, it can be suggested that to improve the speaking ability of the students, teaching should be focused on practicing most frequently used sentence patterns.
AN ANALYSIS OF THE MAIN CHARACTER IN THE FILM “THE NIGHTINGALE”: A LITERARY PSYCHOLOGY STUDY

This research was conducted to identify and analyze the main character of The Nightingale film using a literary psychology approach with the objective to analyze the depiction of the main character. This was a qualitative method. In obtaining the data, this research was conducted by watching The Nightingale film several times and analyzing Clare as the main character in the film. The researcher also uses the theory of emotion’s classification from David Krech. Results of the research found out that Clare as the main character of The Nightingale film had carried seven classifications of emotions, those data were one datum of guilt concept, two data of penetrated guilt, two data of punishing yourself, one datum of shame, four data of sadness, four data of hatred, and three data of love. From those 7 classifications, it was dominated with hatred concept. This proves that the driving force that causes change is very strong compared to other concepts. It can be seen from the storyline of the main character in The Nightingale film, that Clare's character was actually a kind person, but unfortunately she was always treated badly by people around her. This is the reason why Clare was full of resentment and determined to take a revenge for all kind of miserable things she went through.
Security level evaluation with F4SLE

In the realm of security measurements, extensive efforts have been made to evaluate and compare security levels at the country level, resulting in various indices. However, there has been a dearth of evaluations focusing on the information security posture of individual organizations and simultaneously on state-level status evaluation. Such evaluations hold significant potential for providing valuable feedback on the security status of organizations and facilitating assessments and supportive data-driven focused interventions at a national level. This study leverages the Framework for Security Level Evaluation (F4SLE) and the developed tool, Measurement Application for Self-assessing Security (MASS), to collect data for the evaluation. The paper presents diverse options for interpreting the collected data and establishes the foundation for an ongoing cross-country study. The results encompass the analysis of organization-level data and offer insights into overall approaches to security across organizations. This study is a preliminary step toward a more comprehensive information security examination.
Application of Simulated Annealing Algorithm in Emergency Logistics Optimization under 5G Network Environment

With the continuous advancement of technology, "delivery vehicles + drones" has gradually become a new delivery method, especially for emergency material distribution in disaster-stricken areas. The main areas affected by disasters are served by delivery vehicles, while remote areas are assisted by drones. This method will greatly improve delivery efficiency and shorten delivery time. This article explores the overall delivery route of different transportation methods such as "delivery vehicles only," "delivery vehicles + drones," and "reduced delivery vehicles + drones" under different roadmaps using path planning models and simulated annealing algorithms. The complexity of the roadmap is gradually increased, and constraints such as increasing the maximum load and longest route are added to construct a planning model for the fastest delivery solution. The simulated annealing algorithm is used to provide the final route planning scheme, which provides theoretical guidance for actual delivery scenarios.
Cooperative Physical Layer Authentication With Reputation-Inspired Collaborator Selection

Machine learning (ML) based physical layer authentication (PLA) has attracted much attention since neural networks can be constructed to identify channel characteristics in complex wireless environments. This enables high authentication performance and lightweight deployment in the Internet of Things (IoTs). Due to the booming growth of IoT connections, the workload of the central authenticator increases significantly. As a result, resource-constrained terminals are unable to independently handle the computationally complex ML task. Therefore, cooperative physical layer authentication (CoPLA), which introduces multiple supervised nodes as task-sharing entities, is emerged as a promising solution to address this concern. However, in existing CoPLA studies, some critical issues have been overlooked. For example, the consideration of which collaborative nodes are eligible or best suited for cooperation to maximize the authentication gains. Moreover, the security threats posed by untrusted collaborators are equally challenging. In this paper, we propose a federated learning (FL)-based CoPLA scheme that utilizes a group of edge devices to jointly build an authenticator. This ensures privacy preservation and higher robustness. To figure out the optimal collaborator selection in CoPLA, an adaptive search procedure via reinforcement learning (RL) is customized. Furthermore, we introduce a lightweight reputation estimation method to evaluate each collaborator’s credibility, thereby uncovering underperforming devices or hidden internal attackers. Finally, simulations and real-world experiments are carried out. The results show that the authentication accuracy of our scheme is 9.52% higher than that of blind cooperation. And, it outperforms other existing CoPLA schemes in terms of time efficiency and robustness.
The Design and Implementation of Reading Aid Tools Based on Raspberry Pi for Blind EFL Students

Because there is a dearth of accessible reading material, especially braille printed books, this blind reading aid was developed for EFL students. No longer will a blind learner have to rely on braille-printed books to learn and grow. This technology enables visually impaired EFL students to access the same literary works as their sighted peers. This study employs a qualitative approach to experimentation, with data gathered through observation, interviews, and a review of existing literature. This system planning is described using a flowchart and block diagram. The Blackbox testing approach was employed to conduct the tests in this research. The Raspberry Pi was used as the microcontroller, and the Push Button on the Raspberry Pi Camera was used to initiate the graphical capture and subsequent image file processing by Optical Character Recognition (OCR) and Text-to-Speech (TTS). This procedure yields an mp3 file that can be played through loudspeakers or speakers, allowing blind people to "read" printed text by osmosis.&#x0D;
Chinese image description evaluation method based on target domain semantic constraints

To address the problems of insufficient accuracy and difficulty of application in the current Chinese image description field, this paper proposes an evaluation method based on semantic constraints in the target domain. Unlike previous research, this method acts on the output stage of the model, and based on the extraction of key semantics in the target application domain, it is constrained by the macroscopic semantic space of that domain or by introducing external semantic information from other visual tasks. The experiments show that the proposed method effectively improves the semantic coherence between the model output description sentences and the input images in the target domain, and is helpful for the practical application of image description in specific domains.
MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data

Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is naturally extendable to more than two scanners. We also designed a set of criteria to investigate the scanner-related technical variability and evaluate the harmonization techniques. As an essential requirement of our criteria, we introduced a multi-scanner matched dataset of 3T T1 images across four scanners, which, to the best of our knowledge is one of the few datasets of this kind. We also investigated our evaluations using two popular segmentation frameworks: FSL and segmentation in statistical parametric mapping (SPM). Lastly, we compared MISPEL to popular methods of normalization and harmonization, namely White Stripe, RAVEL, and CALAMITI. MISPEL outperformed these methods and is promising for many other neuroimaging modalities.
A Study of Architecture Optimization Techniques for Convolutional Neural Networks

Edge devices such as smartphones or embedded computing platforms require a resource-aware approach. Therefore, it is often necessary to modify CNN models to make them compatible with the limited infrastructure. The diversity of solutions raises the question of how specific techniques may affect model performance. We address this question by empirically evaluating many techniques proposed in the literature on ResNet-101 and VGG-19 architectures. Our main contribution is the ablation study of how different approaches affect the final results in terms of the reduced number of model parameters, FLOPS, and unwanted accuracy drops. Thus, we also presented the possibility of implementing architecture compression methods that interfere with the low- or high-level model structure. We achieved a reduced ResNet-101 model with about 280 times fewer parameters with only 3.51 pp. accuracy drop compared to the baseline. We also performed post-training methods: pruning and quantization at different model sparsity levels. (All results are fully reproducible, the source code is available at https://github.com/artur-sobolewski/CNN-compression )
Artificial Intelligence Methods in Email Marketing—A Survey

The marketing industry has witnessed a significant prevalence of machine learning and artificial intelligence models that have enhanced the accuracy, precision, and personalization of marketing activities. Solutions based on machine learning and artificial intelligence models have become popular due to their ability to provide marketers with detailed insights into customer behavior and preferences, which can help devise strategies that increase customer engagement and retention. The paper begins by presenting six major challenges in the domain of email marketing and next investigates different approaches used to solve each of them. The subject of this work is an overview of applied works exclusively in the email marketing sector.
Measurement-Device-Independent Quantum Key Distribution

The security of the measurement-device-independent quantum key distribution protocol through untrusted nodes has been directly proven using fundamental entropy uncertainty relations. Reasons for the amazing coincidence of the expressions for the secrete key length in the BB84 and MDI protocols are presented.
Factors Associated with COVID-19 Testing, Vaccination, and Use of Digital Contact Tracing Apps among Black and Latinx MSM (BLMSM) in Los Angeles

Abstract This study examines the factors associated with COVID-19 testing, vaccination intent (both individually and jointly), and willingness to use contact tracing digital apps among a cohort of Black and Latinx men who have sex with men (BLMSM) living in Los Angeles during the initial peak (July 2020) of the COVID-19 pandemic. A questionnaire detailing participants COVID-19 experiences was sent to 300 primarily BLMSM after the first state-wide COVID-19 lockdown. Logistic regression models with random cluster effects were used for analyses. Forty-two percent (42%) tested for COVID-19, 27% were willing to get vaccinated, and about 45% reported willingness to use contact tracing digital apps. Controlling for intervention participation, age, education, marital status, employment, health, tobacco, binge drinking, and self-reported anxiety, those who were depressed had 33% (95% CI: 0.13 to 0.82) odds of using a prevention strategy (either test for COVID-19 or vaccination intent) as the group who were not depressed. Those who had high school diploma or less had 23% (95% CI: 0.11 to 0.48) odds to use digital contact tracing apps as the group with education level of at least Associate’s or Bachelor’s degree. Without considering the format of the test kits, vaccine side effects, and ease of use for digital contact tracing apps, participants appeared to still be hesitant in using COVID-19 prevention strategies at the initial height of the pandemic. Our findings suggest the need for further investigation into this hesitancy to better inform and prepare for future epidemics.
23 Algorithmen für verschiedene Aufgaben

No AccessC++ programmierenAug 202323 Algorithmen für verschiedene Aufgabenhttps://doi.org/10.3139/9783446478466.023SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2023Pages: 713-814Print ISBN: 978-3-446-47689-9eISBN: 978-3-446-47846-6 PDF downloadLoading ...
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles

A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some protected groups. In learning tasks, knowledge of the group attributes is essential to ensure non-discrimination, but in practice, these attributes may not be available due to legal and ethical requirements. To address this challenge, this paper studies a model that protects the privacy of individuals’ sensitive information while also allowing it to learn non-discriminatory predictors. A key feature of the proposed model is to enable the use of off-the-shelves and non-private fair models to create a privacy-preserving and fair model. The paper analyzes the relation between accuracy, privacy, and fairness, and assesses the benefits of the proposed models on several prediction tasks. In particular, this proposal allows both scalable and accurate training of private and fair models for very large neural networks.
Learning to Binarize Continuous Features for Neuro-Rule Networks

Neuro-Rule Networks (NRNs) emerge as a promising neuro-symbolic method, enjoyed by the ability to equate fully-connected neural networks with logic rules. To support learning logic rules consisting of boolean variables, converting input features into binary representations is required. Different from discrete features that could be directly transformed by one-hot encodings, continuous features need to be binarized based on some numerical intervals. Existing studies usually select the bound values of intervals based on empirical strategies (e.g., equal-width interval). However, it is not optimal since the bounds are fixed and cannot be optimized to accommodate the ultimate training target. In this paper, we propose AutoInt, an approach that automatically binarizes continuous features and enables the intervals to be optimized with NRNs in an end-to-end fashion. Specifically, AutoInt automatically selects an interval for a given continuous feature in a soft manner to enable a differentiable learning procedure of interval-related parameters. Moreover, it introduces an additional soft K-means clustering loss to make the interval centres approach the original feature value distribution, thus reducing the risk of overfitting intervals. We conduct comprehensive experiments on public datasets and demonstrate the effectiveness of AutoInt in boosting the performance of NRNs.
Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation

In large-scale e-commerce live-stream recommendation, streamers are classified into different levels based on their popularity and other metrics for marketing. Several top streamers at the head level occupy a considerable amount of exposure, resulting in an unbalanced data distribution. A unified model for all levels without consideration of imbalance issue can be biased towards head streamers and neglect the conflicts between levels. The lack of inter-level streamer correlations and intra-level streamer characteristics modeling imposes obstacles to estimating the user behaviors. To tackle these challenges, we propose a curriculum multi-level learning framework for imbalanced recommendation. We separate model parameters into shared and level-specific ones to explore the generality among all levels and discrepancy for each level respectively. The level-aware gradient descent and a curriculum sampling scheduler are designed to capture the de-biased commonalities from all levels as the shared parameters. During the specific parameters training, the hardness-aware learning rate and an adaptor are proposed to dynamically balance the training process. Finally, shared and specific parameters are combined to be the final model weights and learned in a cooperative training framework. Extensive experiments on a live-stream production dataset demonstrate the superiority of the proposed framework.
PLATE: A perception-latency aware estimator

Target tracking is a popular problem with many potential applications. There has been a lot of effort on improving the quality of the detection of targets using cameras through different techniques. In general, with higher computational effort applied, i.e., a longer perception-latency, a better detection accuracy is obtained. However, it is not always useful to apply the longest perception-latency allowed, particularly when the environment does not require to and when the computational resources are shared between other tasks. In this work, we propose a new Perception-LATency aware Estimator (PLATE), which uses different perception configurations in different moments of time in order to optimize a certain performance measure. This measure takes into account a perception-latency and accuracy trade-off aiming for a good compromise between quality and resource usage. Compared to other heuristic frame-skipping techniques, PLATE comes with a formal complexity and optimality analysis. The advantages of PLATE are verified by several experiments including an evaluation over a standard benchmark with real data and using state of the art deep learning object detection methods for the perception stage.
Lensless Opto-Electronic Neural Network Architecture For Processing Multi-Color-Channel Signals

The lensless opto-electronic neural network based on a multi-channel mask enables completing the convolution operations of multi-color-channel signals on the light field, significantly improving the task accuracy, eliminating the sensor components, and reducing energy consumption.
MuraNet: Multi-task Floor Plan Recognition with Relation Attention

The recognition of information in floor plan data requires the use of detection and segmentation models. However, relying on several single-task models can result in ineffective utilization of relevant information when there are multiple tasks present simultaneously. To address this challenge, we introduce MuraNet, an attention-based multi-task model for segmentation and detection tasks in floor plan data. In MuraNet, we adopt a unified encoder called MURA as the backbone with two separated branches: an enhanced segmentation decoder branch and a decoupled detection head branch based on YOLOX, for segmentation and detection tasks respectively. The architecture of MuraNet is designed to leverage the fact that walls, doors, and windows usually constitute the primary structure of a floor plan’s architecture. By jointly training the model on both detection and segmentation tasks, we believe MuraNet can effectively extract and utilize relevant features for both tasks. Our experiments on the CubiCasa5k public dataset show that MuraNet improves convergence speed during training compared to single-task models like U-Net and YOLOv3. Moreover, we observe improvements in the average AP and IoU in detection and segmentation tasks, respectively. Our ablation experiments demonstrate that the attention-based unified backbone of MuraNet achieves better feature extraction in floor plan recognition tasks, and the use of decoupled multi-head branches for different tasks further improves model performance. We believe that our proposed MuraNet model can address the disadvantages of single-task models and improve the accuracy and efficiency of floor plan data recognition.
A Machine-Learning Framework for Supporting Content Recommendation via User Feedback Data and Content Profiles in Content Managements Systems

Matrix Factorization (MF) which is a Collaborative Filtering (CF) based model, is widely used in Recommendation Systems (RS). In this research, we deal with a specific recommendation problem of recommending content to users in a Content Management System (CMS) utilizing users’ feedback data.
TikTok as a platform for educational dissemination in Ecuador and Peru

The incursion of digital platforms such as TikTok represents a rethinking of traditional communication processes, as well as the structure of the content presented. In response to these changes, educational dissemination in recent years integrates a conglomerate of strategies whose purpose is to democratize education and bring it closer to each individual; TikTok then emerges as the ideal platform to do so, due to the multiple resources it covers and the scope it implies in relation to the material that is broadcast. Therefore, this research aims to determine the communication practices of Tiktokers accounts from Ecuador and Peru dedicated to producing educational content: the topics they cover, the implicit narrative resources, and the reach of the posts they broadcast. The data collection for this descriptive-exploratory investigation uses a documentary analysis applied to the accounts selected for the investigation.