Reviewerly/exHarmony huggingface dataset:
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution

Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR efficiently in a single encoder-and-decoder network. Also, a dynamic residual frame is jointly learned with the DP3DF via a shared backbone to further boost the SR quality. We performed extensive experiments, including a large-scale user study, to show our method's effectiveness. Our method consistently surpasses the best state-of-the-art methods on all the challenging real datasets with top PSNR and user ratings, yet having a very fast run time. The code is available at https://github.com/xiaogang00/DP3DF.
STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training

Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored. In this work, we propose STOA-VLP, a pre-training framework that jointly models object and action information across spatial and temporal dimensions. More specifically, the model regards object trajectories across frames and multiple action features from the video as fine-grained features. Besides, We design two auxiliary tasks to better incorporate both kinds of information into the pre-training process of the video-language model. The first is the dynamic object-text alignment task, which builds a better connection between object trajectories and the relevant noun tokens. The second is the spatial-temporal action set prediction, which guides the model to generate consistent action features by predicting actions found in the text. Extensive experiments on three downstream tasks (video captioning, text-video retrieval, and video question answering) demonstrate the effectiveness of our proposed STOA-VLP (e.g. 3.7 Rouge-L improvements on MSR-VTT video captioning benchmark, 2.9% accuracy improvements on MSVD video question answering benchmark, compared to previous approaches).
Self-Supervised Learning for Multilevel Skeleton-Based Forgery Detection via Temporal-Causal Consistency of Actions

Skeleton-based human action recognition and analysis have become increasingly attainable in many areas, such as security surveillance and anomaly detection. Given the prevalence of skeleton-based applications, tampering attacks on human skeletal features have emerged very recently. In particular, checking the temporal inconsistency and/or incoherence (TII) in the skeletal sequence of human action is a principle of forgery detection. To this end, we propose an approach to self-supervised learning of the temporal causality behind human action, which can effectively check TII in skeletal sequences. Especially, we design a multilevel skeleton-based forgery detection framework to recognize the forgery on frame level, clip level, and action level in terms of learning the corresponding temporal-causal skeleton representations for each level. Specifically, a hierarchical graph convolution network architecture is designed to learn low-level skeleton representations based on physical skeleton connections and high-level action representations based on temporal-causal dependencies for specific actions. Extensive experiments consistently show state-of-the-art results on multilevel forgery detection tasks and superior performance of our framework compared to current competing methods.
Index/Impressum: Vakuum in Forschung und Praxis 3/2023

Vakuum in Forschung und PraxisVolume 35, Issue 3 p. 51-51 Magazin Index/Impressum: Vakuum in Forschung und Praxis 3/2023 First published: 27 June 2023 https://doi.org/10.1002/vipr.202370309AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume35, Issue3June/July 2023Pages 51-51 RelatedInformation
Forschung: Vakuum in Forschung und Praxis 3/2023

Vakuum in Forschung und PraxisVolume 35, Issue 3 p. 18-29 Magazin Forschung: Vakuum in Forschung und Praxis 3/2023 First published: 27 June 2023 https://doi.org/10.1002/vipr.202370304AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume35, Issue3June/July 2023Pages 18-29 RelatedInformation
PELATIHAN KETERAMPILAN KEJURUAN GARMEN APPAREL DI LEMBAGA PEMASYARAKATAN NARKOTIKA KLAS II A KOTA LUBUKLINGGAU

The purpose of community service between PGRI Silampari University, in collaboration with the Lubuklinggau City Manpower Service and the Lubuklinggau City Class II.A Narcotics Correctional Institution is to improve apparel garment vocational skills in the assisted community in Lubuklinggau Class II.A Narcotics Correctional Institution. Methods of community service activities include socialization, discussion, practice and evaluation of participants' responses to activities. Based on the results of community service activities, it is known: 1) There is collaboration between the PGRI Silampari Lubuklinggau university and the Lubuklinggau City Manpower Office and the Lubuklinggau City Narcotics Penitentiary. 2) Good and effective implementation of apparel garment vocational skills socialization activities. 3) Effective discussion activities increase the motivation of participants towards training activities. 4) The assisted community is able to practice the skills that have been provided by the resource persons, and 5) The activities are going well and effectively as seen from the results of the responses stating that the activities are very effective. Based on the results of the PKM activities that the team has carried out, it can be concluded that the PKM activities are going well and effectively in accordance with the needs of the assisted community of the Lubuklinggau City Narcotics Correctional Institution
School of Practical Analytics: Analytical writing

This article is devoted to explaining the concept and meaning of applied analytical writing. The author outlines the requirements for analytical writing and explains some of them, including: analytical metacognitive distance; analytical insight; analytics maturity; evaluative and interpretive certainty; analytical fluency; analytical flexibility; analytical originality, analytical precision.
Search-based Time-Aware Graph-Enhanced Recommendation with Sequential Behavior Data

Extending from sequential recommendation models, in this paper, we present a novel framework named S earch-based T ime- A ware Rec ommendation (STARec) , which first retrieves the historical behaviors of the given user through a search-based retriever, and then captures the user’s evolving demands over time through a time-aware sequential network. We notice that the key insight of STARec is to use the feature and labels to augment the representations, and thus the effectiveness of STARec relies on the acquisition of rich browsing records of the target user and powerful representation of each browsed item and thus its performance could heavily drop regarding long-tail users and items. To this end, we extend STARec by constructing a graph upon the user-item interactions and leveraging the graph structure to enhance the representation learning. We call this extended version S earch-based T ime- A ware G raph- E nhanced Recommendation (STAGE) . We conduct extensive experiments on three real-world datasets and STARec achieves consistent superiority. We further compare STAGE against STARec long-tail users and our results demonstrate that STAGE could outperform STARec at most cases. Results of online A/B tests show that STARec and STAGE achieve an average CTR improvement of around 6% and 1.5% in the two main item recommendation scenarios respectively.
A Study on The Experience and Effect of The Construction of Professional Teaching Resource Bank in Vocational Education-- A Case Study of Automobile Intelligent Technology

This paper mainly takes automobile intelligent technology as an example to introduce the experience and effect of professional teaching resource pool construction in Vocational education.By means of integrated design, advanced technology support, open management, network operation and continuous updating, the project systematically builds a "progressive ability" course system, develops high-quality teaching resources, realizes the functions of "energy learning and supplementary education", explores the certification, accumulation and transformation of learning results, reforms the professional teaching mode, improves the quality of professional training and social service ability, and drives the National Teaching Reform of automotive intelligent technology specialty.Finally, it summarizes the problems of the construction of vocational education resource pool, puts forward corresponding strategies, and hopes to provide scientific guidance and help in the teaching resource pool of Vocational education.
A Hybrid Transfer Learning and Segmentation Approach for the Detection of Acute Lymphoblastic Leukemia

Acute Lymphoblastic Leukemia (ALL) is a subcategory of leukemia which is a type of blood cancer. ALL is also the most commonly diagnosed form of pediatric cancer, with early detection of the cancer playing a crucial role in the survivability of the diagnosed patient. This paper therefore proposes a Hybrid Transfer Learning eXtreme Gradient Boosting (HTL-XGB) algorithm which exploits Transfer Learning and through utilizing modern developments of state-of-the-art CNNs (Convolutional Neural Networks) effective ability for feature extraction, combined with the robust performance of eXtreme Gradient Boosting for the classification and detection of ALL. An object detection methodology is proposed for the individual detection of activated and non-activated lymphocytes within full blood smear images, using image processing techniques with the proposed HTL-XGB architecture. Several state-of-the-art CNNs were experimented through fair trials, with Xception achieving the highest accuracy of 89.7%.
An Efficient Cryptosystem for Image Using 1D and 2D Logistic Chaotic Maps

Image is very important in multimedia technology and social media such as WhatsApp, Facebook, Viber, and Telegram, and in many fields, including military, bank accounts ,and communications.Therefore, protecting the image from hacking or theft during storage or transmission over the net is the most demanded matter nowadays.This paper proposes a new method for grayscale image encoding based on two types of logistic maps, one-dimension (1D) and two-dimension (2D).First, convert a color image to grayscale and then into a binary form as a confusion step.Second, compute the initial parameters of (1D) and (2D) logistic maps using newly suggested equations.After that, permute the binary image with the new positions generated by a (2D) logistic map as a diffusion step.At the same time, a highly complicated random key is generated using a new suggested equation based on a (1D) logistic map.Then, XOR is applied between the generated key and the final image to spawn the encoded image.Finally, a median filter is applied as a post-processing step for the noisy cipher image to increase the efficiency of the proposed system when retrieving the decrypted image.The proposed method encodes images of size (1024×1024) larger than the other works.A high Mean Square Error (MSE) of 7768.40, a low peak signal to noise ratio (PSNR) of 9.22749, a high number of pixel change rate (NPCR) up to 99.63%, and a high entropy of 7.99985 are obtained.Additionally, a high speed and a high key precision 10 16 are achieved.The proposed schema outperforms the other conducted works in terms of the previous evaluation measurements.The system retrieves 92% of the images with 50% salt and pepper noise and 84% of the images with 0.1 Gaussian noise, indicating higher security against noise attacks than the other works.
MSFNet: modality smoothing fusion network for multimodal aspect-based sentiment analysis

Multimodal aspect-based sentiment classification (MABSC) aims to determine the sentiment polarity of a given aspect in a sentence by combining text and image information. Although the text and the corresponding image in a sample are associated with aspect information, their features are represented in distinct semantic spaces, creating a substantial semantic gap. Previous research focused primarily on identifying and fusing aspect-level sentiment expressions of different modalities while ignoring their semantic gap. To this end, we propose a novel aspect-based sentiment analysis model named modality smoothing fusion network (MSFNet). In this model, we process the unimodal aspect-aware features via the feature smoothing strategy to partially bridge modality gap. Then we fuse the smoothed features deeply using the multi-channel attention mechanism, to obtain aspect-level sentiment representation with comprehensive representing capability, thereby improving the performance of sentiment classification. Experiments on two benchmark datasets, Twitter2015 and Twitter2017, demonstrate that our model outperforms the second-best model by 1.96% and 0.19% in terms of Macro-F1, respectively. Additionally, ablation studies provide evidence supporting the efficacy of each of our proposed modules. We release the code at: https://github.com/YunjiaCai/MSFNet .
The problems and optimization strategies of primary education informatization development

Education informatization is the development trend of education in the new era, which promotes the development of primary education in China.However, in primary school teaching, teachers do not apply information technology to daily teaching, which leads to the information knowledge accepted by students is inconsistent with actual life.Therefore, in order to better adapt to the development requirements of the information age, teachers should update their own concepts, apply information technology to daily teaching, and cultivate students' ability to solve problems with information technology.On this basis, we will continue to improve students' understanding and understanding of information technology, and then improve students' learning interest and learning efficiency.
Emergence of Sign Language Recognition System into Text

Purpose: Communication is the passport of success. The objective of this undertaking was to fabricate a neural network ready to group which letter of the American Sign Language (ASL) letters in order is being marked, given a picture of a marking hand. Design/Methodology/Approach: It was developed using Python programming with the help of TensorFlow for all AI-related tasks and Deep Learning. Findings/Result: This examination is an initial move towards building a potential communication via gestures interpreter, which can take correspondences in communication through signing and make an interpretation of them into composed and oral language. Such an interpreter would enormously bring down the hindrance for some hard of hearing and quiet people to have the option to more readily speak with others in everyday collaborations. This objective is additionally propelled by the seclusion that is felt inside the hard of hearing local area. Dejection and sorrow exist at higher rates among the hard of hearing populace, particularly when they are drenched in a conference world. Originality/Value: Enormous obstructions that significantly influence life quality originates from the correspondence disengagement between the hard of hearing and the conference. A few models are data hardship, impediments of social associations, and trouble coordinating in the public eye. Paper Type: Research paper
Research and Application of Blended Learning in College English Speaking Teaching Based on Smart Mobile Platform

College oral English teaching is the weakest link in college at this stage.English teachers are constantly exploring effective methods and approaches to improve the quality of oral English teaching in their teaching practices.With the advancement of teaching informatization, blended learning has entered the historical stage in line with the development of the informatization era.In the context of educational informatization, this research is carried out for the teaching exploration and practice of blended learning for the three freshmen classes of non-English majors in Yinchuan Energy College from 2021.Through literature review, questionnaire surveys, interviews, and other research methods, practice shows that the oral blended learning model has been recognized by most students in the experimental class.The model effectively promotes the teaching of college oral English, which is of great significance and value from theoretical research to pilot teaching.
COMMUTATORS OF CLASSICAL OPERATORS IN A NEW VANISHING ORLICZ-MORREY SPACE

We study mapping properties of commutators of classical operators of harmonic analysis -commutators of maximal, singular and fractional operators in a new vanishing subspace of Orlicz-Morrey spaces.We show that the vanishing property defining that subspace is preserved under the action of those operators.
Teaching Design of English Grammar in High School Based on the Activity-based Approach to English Learning

The English Curriculum Standards for General High Schools (2017 Edition, Revised 2020) clearly points out that the Activity-based Approach to English Learning under the six components of integration is an important way to implement the English subject core competencies.To address the problems of teachers' single teaching method and students' lack of systematic learning in English grammar teaching, this paper takes the teaching design of a high school English grammar lesson as an example to explain how to carry out English grammar teaching based on the Activity-based Approach to English Learning, and puts forward three suggestions, insisting on the subjectivity of students; creating authentic learning situations and activities; conducting evidence-based evaluation of classroom teaching and learning.
Summarization Attack via Paraphrasing (Student Abstract)

Many natural language processing models are perceived to be fragile on adversarial attacks. Recent work on adversarial attack has demonstrated a high success rate on sentiment analysis as well as classification models. However, attacks to summarization models have not been well studied. Summarization tasks are rarely influenced by word substitution, since advanced abstractive summary models utilize sentence level information. In this paper, we propose a paraphrasing-based attack method to attack summarization models. We first rank the sentences in the document according to their impacts to summarization. Then, we apply paraphrasing procedure to generate adversarial samples. Finally, we test our algorithm on benchmarks datasets against others methods. Our approach achieved the highest success rate and the lowest sentence substitution rate. In addition, the adversarial samples have high semantic similarity with the original sentences.
Flaky Performances When Pretraining on Relational Databases (Student Abstract)

We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extracted from relational databases (RDBs). Intuitively, this joint use of SSL and GNNs should allow to leverage more of the available data, which could translate to better results. However, we found that naively porting contrastive SSL techniques can cause ``negative transfer'': linear evaluation on fixed representation from a pretrained model performs worse than on representations from the randomly-initialized model. Based on the conjecture that contrastive SSL conflicts with the message passing layers of the GNN, we propose InfoNode: a contrastive loss aiming to maximize the mutual information between a node's initial- and final-layer representation. The primary empirical results support our conjecture and the effectiveness of InfoNode.
Adaptive Temporal Planning for Multi-Robot Systems in Operations and Maintenance of Offshore Wind Farms

With the fast development of offshore wind farms as renewable energy sources, maintaining them efficiently and safely becomes necessary. The high costs of operation and maintenance (O&amp;M) are due to the length of turbine downtime and the logistics for human technician transfer. To reduce such costs, we propose a comprehensive multi-robot system that includes unmanned aerial vehicles (UAV), autonomous surface vessels (ASV), and inspection-and-repair robots (IRR). Our system, which is capable of co-managing the farms with human operators located onshore, brings down costs and significantly reduces the Health and Safety (H&amp;S) risks of O&amp;M by assisting human operators in performing dangerous tasks. In this paper, we focus on using AI temporal planning to coordinate the actions of the different autonomous robots that form the multi-robot system. We devise a new, adaptive planning approach that reduces failures and replanning by performing data-driven goal and domain refinement. Our experiments in both simulated and real-world scenarios prove the effectiveness and robustness of our technique. The success of our system marks the first-step towards a large-scale, multirobot solution for wind farm O&amp;M.
DFEE: Interactive DataFlow Execution and Evaluation Kit

DataFlow has been emerging as a new paradigm for building task-oriented chatbots due to its expressive semantic representations of the dialogue tasks. Despite the availability of a large dataset SMCalFlow and a simplified syntax, the development and evaluation of DataFlow-based chatbots remain challenging due to the system complexity and the lack of downstream toolchains. In this demonstration, we present DFEE, an interactive DataFlow Execution and Evaluation toolkit that supports execution, visualization and benchmarking of semantic parsers given dialogue input and backend database. We demonstrate the system via a complex dialog task: event scheduling that involves temporal reasoning. It also supports diagnosing the parsing results via a friendly interface that allows developers to examine dynamic DataFlow and the corresponding execution results. To illustrate how to benchmark SoTA models, we propose a novel benchmark that covers more sophisticated event scheduling scenarios and a new metric on task success evaluation. The codes of DFEE have been released on https://github.com/amazonscience/dataflow-evaluation-toolkit.