Reviewerly/exHarmony huggingface dataset:
GACE: Learning Graph-Based Cross-Page Ads Embedding for Click-Through Rate Prediction

Predicting click-through rate (CTR) is the core task of many ads online recommendation systems, which helps improve user experience and increase platform revenue. In this type of recommendation system, we often encounter two main problems: the joint usage of multi-page historical advertising data and the cold start of new ads. In this paper, we proposed GACE, a graph-based cross-page ads embedding generation method. It can warm up and generate the representation embedding of cold-start and existing ads across various pages. Specifically, we carefully build linkages and a weighted undirected graph model considering semantic and page-type attributes to guide the direction of feature fusion and generation. We designed a variational auto-encoding task as pre-training module and generated embedding representations for new and old ads based on this task. The results evaluated in the public dataset AliEC from RecBole and the real-world industry dataset from Alipay show that our GACE method is significantly superior to the SOTA method. In the online A/B test, the click-through rate on three real-world pages from Alipay has increased by 3.6%, 2.13%, and 3.02%, respectively. Especially in the cold-start task, the CTR increased by 9.96%, 7.51%, and 8.97%, respectively.
Semi-supervised Classification on Data Streams with Recurring Concept Drift Based on Conformal Prediction

In this article, we consider the problem of semi-supervised data stream classification. The main difficulties of data stream semi-supervised classification include how to jointly utilize labeled and unlabeled samples to adress concept drift detection and how to use unlabeled to update trained classifier. Existing algorithms like the CPSSDS method constantly retrain a new classifier when concept drift is detected, it is very consuming and wasteful. In this paper, the algorithm of data stream semi-supervised classification with recurring concept drift named as CPSSDS-R is proposed. First, the labeled samples in the first data block are used to initialize a classifier, which is added into a pool and actived for classification. While a new data block arrives, concept drift is detected by computing conformal prediction results. If no concept drift is detected, the pseudo-labeled samples in the previous data block are added with the labeled samples in the current data block to incrementally train the active classifier. If a new concept is detected, a new classifier is trained on the labeled samples of the current data block and added into the pool and actived for classification, else if a recurring concept is detected, the pseudo-labeled samples and labeled samples in the current data block are used to incrementally update the classifier corresponding to the recurring concept in the pool and actived for classification. The proposed algorithm is tested on multiple synthetic and real datasets, and its cumulative accuracy and block accuracy at different labeling ratios demonstrate the effectiveness of the proposed algorithm. The code for the proposed algorithm is available on https://gitee.com/ymw12345/cpssds-r .
The Work of Student’s Nervousness in Learning English

The purpose of this study is to investigate the role of students' anxiousness in studying English at Madura University. The population consists of English Department students. This study employs an ex-post facto research method, using a quantitative approach; it emphasizes how past behavior might be used to anticipate future events. The sample approach is proportional stratified random sampling. To analyse the data, a simple regression approach was applied. The study's findings reveal that the percentage of students who are anxious about studying English at Madura University's English Department is not very high, at 0.64 (64%). The degree of anxiety among students affects their ability to produce English. those with low levels of anxiety will do better in English than those with high levels of anxiety. It indicates that teachers should give and support students with a pleasurable and exciting language environment in order to reduce their stress in language learning. It is required that academic society members, particularly those who are receptive to students' apprehension, emotional condition, and motivation in language acquisition.
Content Words and Readability in Students’ Thesis Findings

This study investigates the content words and readability in bachelor’s thesis findings in the English Literature Program at the University of Sumatera Utara. Qualitative analysis was applied in this study. The data for this study were content words and sentences taken from the data sources of 13 bachelor’s thesis findings. The content words were collected using a lexical density online tool, and the data for readability was collected and analyzed using an online Flesch Reading Ease tool. The results show that the lexical density of the content words ranges from 50.47% – 57.5%. Whilst the readability of the 13 texts range from 19.1 – 61.7. The average score of content word density indicates that the theses’ findings present concise information as represented in scientific writing, and the readability style ranges from "very difficult to read” to “standard readable”. In conclusion, these findings can be categorized as densely written language and content words, supported by college students' increasingly intricate choice of words and sentences frequently read.
EDDVPL: A Web Attribute Extraction Method with Prompt Learning

Since labeling web pages requires a lot of human resources and time, web attribute extraction methods based on few-shot learning have gained the attention of researchers. However, these methods still rely heavily on sufficient labeled data of several seed websites. In order to effectively alleviate the lack of domain information, we design a web attribute extraction model based on dual-view prompt learning named EDDVPL, achieving page-level few-shot learning which uses only a small number of labeled web pages for training. Specifically, we first retrieve semantic prompt information of DOM tree view by a simplified algorithm to stimulate domain-related knowledge of the pre-trained language model. Then, we introduce task prompt information of template view by constructing a template indicating the extraction target, which can help the pre-trained language model quickly understand the task of web attribute extraction. Finally, we integrate the dual-view prompt information by template filling to jointly guide the training of the pre-trained language model at semantic and task levels. Extensive experimental results on the public SWDE dataset show that EDDVPL performs the best results compared to the baselines.
Your Reputation’s Safe with Me: Framing-Free Distributed Zero-Knowledge Proofs

Distributed Zero-Knowledge (dZK) proofs, recently introduced by Boneh et al. (CRYPTO‘19), allow a prover $$\mathcal{P}$$ to prove NP statements on an input x which is distributed between k verifiers $$\mathcal{V}_1,\ldots ,\mathcal{V}_k$$ , where each $$\mathcal{V}_i$$ holds only a piece of x. As in standard ZK proofs, dZK proofs guarantee Completeness when all parties are honest; Soundness against a malicious prover colluding with t verifiers; and Zero Knowledge against a subset of t malicious verifiers, in the sense that they learn nothing about the NP witness and the input pieces of the honest verifiers. Unfortunately, dZK proofs provide no correctness guarantee for an honest prover against a subset of maliciously corrupted verifiers. In particular, such verifiers might be able to “frame” the prover, causing honest verifiers to reject a true claim. This is a significant limitation, since such scenarios arise naturally in dZK applications, e.g., for proving honest behavior, and such attacks are indeed possible in existing dZKs (Boneh et al., CRYPTO‘19). We put forth and study the notion of strong completeness for dZKs, guaranteeing that true claims are accepted even when t verifiers are maliciously corrupted. We then design strongly-complete dZK proofs using the “MPC-in-the-head” paradigm of Ishai et al. (STOC‘07), providing a novel analysis that exploits the unique properties of the distributed setting. To demonstrate the usefulness of strong completeness, we present several applications in which it is instrumental in obtaining security. First, we construct a certifiable version of Verifiable Secret Sharing (VSS), which is a VSS in which the dealer additionally proves that the shared secret satisfies a given NP relation. Our construction withstands a constant fraction of corruptions, whereas a previous construction of Ishai et al. (TCC‘14) required $$k={\textsf{poly}}\left( t\right) $$ . We also design a reusable version of certifiable VSS that we introduce, in which the dealer can prove an unlimited number of predicates on the same shared secret. Finally, we extend a compiler of Boneh et al. (CRYPTO‘19), who used dZKs to transform a class of “natural” semi-honest protocols in the honest-majority setting into maliciously secure ones with abort. Our compiler uses strongly-complete dZKs to obtain identifiable abort.
Enhancing Object Detection in Maritime Environments Using Metadata

Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV. This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art realtime object detector. To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model’s ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images. Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions. The source code of this work is publicly available at https://github.com/ipleiria-robotics/maritime-metadata-adaptation .
Lightweight Target Detection Algorithm for Aerial Images

Target detection for aerial images has been the focus of research. However, there are several difficulties in aerial image detection, such as complex backgrounds, high resolution, and a large number of small targets in UAV (Unmanned Aerial Vehicles) aerial images. In order to achieve high-precision detection of ground targets, a lightweight aerial image target detection algorithm LATD-YOLO (Lightweight Aerial Image Target Detector) based on YOLOv7 is proposed in this paper. Firstly, a new network structure dedicated to small target detection is proposed, and the feature extraction network and feature fusion network architectures are both lightweight. The fusion relationship between shallow and deep features is reconstructed. Then, the ELAN-OD module is proposed to reduce the model computation and strengthen the feature extraction ability of the network. In addition, the hybrid attention mechanism is added to the structure of the feature extraction network, where the effective information is extracted to enhance the learning ability. Finally, a new anchor frame position metric is introduced to improve the model’s ability to handle small targets. The experimental results show that LATD-YOLO can effectively improve the detection effect. The detection accuracy is improved by 359.6% on the ITCVD and 3.6% on the VisDrone2019 datasets respectively; the volume of the model is decreased by 67.74%; the amount of parameters is reduced by 67.91%; the amount of computation is decreased by 30.92%. Therefore, LATD-YOLO can achieve excellent detection performance in high-precision and lightweight computation scenarios.
The eROSITA Final Equatorial-Depth Survey (eFEDS): A machine learning approach to inferring galaxy cluster masses from eROSITA X-ray images

We have developed a neural network-based pipeline to estimate masses of galaxy clusters with a known redshift directly from photon information in X-rays. Our neural networks were trained using supervised learning on simulations of eROSITA observations, focusing on the Final Equatorial Depth Survey (eFEDS). We used convolutional neural networks that have been modified to include additional information on the cluster, in particular, its redshift. In contrast to existing works, we utilized simulations that include background and point sources to develop a tool that is directly applicable to observational eROSITA data for an extended mass range -- from group size halos to massive clusters with masses in between $10^ M_ M_ Using this method, we are able to provide, for the first time, neural network mass estimations for the observed eFEDS cluster sample from Spectrum-Roentgen-Gamma/eROSITA observations and we find a consistent performance with weak-lensing calibrated masses. In this measurement, we did not use weak-lensing information and we only used previous cluster mass information, which was used to calibrate the cluster properties in the simulations. When compared to the simulated data, we observe a reduced scatter with respect to luminosity and count rate based scaling relations. We also comment on the application for other upcoming eROSITA All-Sky Survey observations.
Spatial differential distribution of connective cells in two different tumor-associated collagen signatures at the breast cancer boundary

The invasion and metastasis of tumor cells are not only related to the tumor cells themselves, but also closely intertwined with other components of the tumor microenvironment. The connective cells, including fibroblasts and myofibroblasts, play a crucial role in determining the remodeling of collagen fibers in the tumor microenvironment during the process of tumor invasion, which determinant of tumor migration. In this study, we used the multi-photon imaging system to identify two different prognostic tumor-associated collagen signatures (TACS4 and TACS6). Then, the multi-photon images were used to co-locate with HE images, and open-source convolutional neural network Hover-Net was used to segment, classify and quantifythe nuclear feature. Our results showed that the connective cells are opposed to tumor cells and do not mix with tumor cells in TACS4, while in TACS6, they are scattered promiscuously with tumor cells. The spatial distribution of connected cells in TACS4 is denser than that in TACS6. These differences in spatial distribution of connected cells may potentially have different prognostic information.
The Impact of Mobile-Assisted Language Learning on Developing EFL Learners’ Vocabulary Knowledge

Currently, a great variety of educational apps have been developed and extensively used by learners and instructors, making, as a result, mobile-assisted language learning one of the most prominent learning methods in enhancing the quality of language learning. However, despite the growing popularity of commonly used apps supporting second language acquisition, such as Kahoot and Quizlet, empirical research on the pedagogical role of other more recent applications in boosting L2 vocabulary is still scarce. Given this, this experimental study examines the effectiveness of a mobile application in enhancing Moroccan EFL secondary school students’ vocabulary knowledge. The study consisted of the control group (n = 30) and the experimental group (n = 30). While participants in the first group received traditional methods of teaching vocabulary, the experimental group integrated a mobile application (Flashcards World) during one semester. Data collection comprised vocabulary pre- and post-tests to compare the vocabulary learning of participants in both groups. To determine if there was a difference in the scores of the groups before and after the intervention, the findings were analysed using Independent-Samples T Tests (SPSS-26). The results revealed that the experimental group outscored the control group. Therefore, it was concluded that using Flashcard World application effectively improved vocabulary learning of secondary school students more than conventional teaching.
Tree-Based Generation of Restricted Graph Languages

Order-preserving DAG grammars (OPDGs) is a formalism for representing languages of structurally restricted graphs. As demonstrated in [17], they are sufficiently expressive to model abstract meaning representations in natural language processing, a graph-based form of semantic representation in which nodes encode objects and edges relations. At the same time, they can be parsed in [Formula: see text], where [Formula: see text] and [Formula: see text] are the sizes of the grammar and the input graph, respectively. In this work, we provide an initial algebra semantic for OPDGs, which allows us to view them as regular tree grammars under an equivalence theory. This makes it possible to transfer results from the field of formal tree languages to the domain of OPDGs, both in the unweighted and the weighted case. In particular, we show that deterministic OPDGs can be minimised efficiently, and that they are learnable under the “minimal adequeate teacher” paradigm, that is, by querying an oracle for equivalence between languages, and membership of individual graphs. To conclude, we demonstrate that the languages generated by OPDGs are definable in monadic second-order logic.
Optimal Minimum Energy Path Planning of Mobile Robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing.
Soft Measurement of Rare Earth Multi-Element Component Content Based on Multi-LightVGG Modeling

The current hardware equipment used to detect the content of each element component in the rare earth extraction process has a complex structure and high maintenance cost. A modeling method for the soft measurement of rare earth multi-element component content is proposed to address this issue. This method uses the Multi-LightVGG multi-tasking learning model and the Multi Gradient Descent Algorithm based on Optimized Upper Bound (MGDA-OUB) to optimize the model for each prediction task and find the Pareto optimal solution. After conducting several experiments, the Multi-LightVGG model loaded with MGDA-OUB has lower MRE, RMSE for Pr, Nd prediction, and MAX(|error|) for Nd prediction than the Multi-LightVGG model without MGDA-OUB by 0.3778%, 0.5208%, 0.0015, 0.0015, and 0.1985%, respectively; and the MRE and RMSE of the Multi-LightVGG model for Pr and Nd prediction under the same optimization conditions are lower than those of Multi-ResNet18 by 0.3297%, 0.5423%, 0.0019, and 0.002, respectively, thus indicating that MGDA-OUB can effectively solve multiple task-specific Pareto solutions to avoid possible conflicts between specific tasks, while the Multi-LightVGG model, compared to the Multi-Resnet18 model, has a backbone network that can effectively capture the abstract representations in the images of the rare earth-extraction mixed solution, which in turn improves the prediction accuracy of the content of each elemental component.
HRBUST-LLPED: A Benchmark Dataset for Wearable Low-Light Pedestrian Detection

Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of pedestrians. With this in mind, we introduce a low-light pedestrian detection (called HRBUST-LLPED) dataset by capturing pedestrian data on campus using wearable low-light cameras. Most of the data were gathered under starlight-level illumination. Our dataset annotates 32,148 pedestrian instances in 4269 keyframes. The pedestrian density reaches high values with more than seven people per image. We provide four lightweight, low-light pedestrian detection models based on advanced YOLOv5 and YOLOv8. By training the models on public datasets and fine-tuning them on the HRBUST-LLPED dataset, our model obtained 69.90% in terms of AP@0.5:0.95 and 1.6 ms for the inference time. The experiments demonstrate that our research can assist in advancing pedestrian detection research by using low-light cameras in wearable devices.
Pronunciation errors of Korean nasal codas by Japanese learners: A statistical study using AI training data for foreign voice recognition

This study analyzes AI training data from Japanese learners regarding their pronunciation errors of Korean nasal codas. Two types of speech data sets were examined: reading speech and spontaneous speech. Approximately 38 hours of Korean speech data from 40 Japanese speakers indicated that Japanese learners of Korean exhibited a high error rate in pronouncing nasal codas due to L1 negative transfer effects. Additionally, this study investigated whether orthographic factors play a role when Japanese learners of Korean pronounce consecutively positioned nasal codas. It was found that assimilation error rates in reading, when consecutive codas were presented, were higher than those observed in spontaneous speech.