Reviewerly/exHarmony huggingface dataset:
Realizing Pixel-Level Semantic Learning in Complex Driving Scenes based  on Only One Annotated Pixel per Class

Semantic segmentation tasks based on weakly supervised condition have been put forward to achieve a lightweight labeling process. For simple images that only include a few categories, researches based on image-level annotations have achieved acceptable performance. However, when facing complex scenes, since image contains a large amount of classes, it becomes difficult to learn visual appearance based on image tags. In this case, image-level annotations are not effective in providing information. Therefore, we set up a new task in which only one annotated pixel is provided for each category. Based on the more lightweight and informative condition, a three step process is built for pseudo labels generation, which progressively implement optimal feature representation for each category, image inference and context-location based refinement. In particular, since high-level semantics and low-level imaging feature have different discriminative ability for each class under driving scenes, we divide each category into "object" or "scene" and then provide different operations for the two types separately. Further, an alternate iterative structure is established to gradually improve segmentation performance, which combines CNN-based inter-image common semantic learning and imaging prior based intra-image modification process. Experiments on Cityscapes dataset demonstrate that the proposed method provides a feasible way to solve weakly supervised semantic segmentation task under complex driving scenes.
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel  Attention

Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention modules have been proposed and implemented in various ways. Generally speaking, they are mainly based on convolution and pooling operations. In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way. The GPCA module intends to model the correlations among the channels, which are assumed to be captured by beta distributed variables. As the beta distribution cannot be integrated into the end-to-end training of convolutional neural networks (CNNs) with a mathematically tractable solution, we utilize an approximation of the beta distribution to solve this problem. To specify, we adapt a Sigmoid-Gaussian approximation, in which the Gaussian distributed variables are transferred into the interval [0,1]. The Gaussian process is then utilized to model the correlations among different channels. In this case, a mathematically tractable solution is derived. The GPCA module can be efficiently implemented and integrated into the end-to-end training of the CNNs. Experimental results demonstrate the promising performance of the proposed GPCA module. Codes are available at https://github.com/PRIS-CV/GPCA.
ARTS SYNTHESIS IN THE DESIGN THE EDUCATIONAL ENVIRONMENT OF PROFESSIONAL TRAINING OF FUTURE PRIMARY SCHOOL TEACHERS

The article outlines the essence of the concept of the educational environment, reveals its structure, and clarifies its key role in the system of future primary school teachers’ training. To meet that aim, several theoretical methods, including analysis of sources and literature on the research; structural and functional to justify the structure, content, and principles of building a training system; and theoretical modeling, are utilized. The interconnected components of the researched professional training have been described. They provide a positive result for the professional development of the future primary school teacher: axiological, socio-cultural, epistemological, cognitive, creative, personal-activity, and communicative. It has been proved that the educational environment design utilizing art synthesis allows: for more accurately predicting and controlling the teaching process, solving educational and training tasks, ensuring favorable conditions for the development of creative personality, and optimal use of available resources.
Deep Adversarial Learning Triplet Similarity Preserving Cross-Modal Retrieval Algorithm

The cross-modal retrieval task can return different modal nearest neighbors, such as image or text. However, inconsistent distribution and diverse representation make it hard to directly measure the similarity relationship between different modal samples, which causes a heterogeneity gap. To bridge the above-mentioned gap, we propose the deep adversarial learning triplet similarity preserving cross-modal retrieval algorithm to map different modal samples into the common space, allowing their feature representation to preserve both the original inter- and intra-modal semantic similarity relationship. During the training process, we employ GANs, which has advantages in modeling data distribution and learning discriminative representation, in order to learn different modal features. As a result, it can align different modal feature distributions. Generally, many cross-modal retrieval algorithms only preserve the inter-modal similarity relationship, which makes the nearest neighbor retrieval results vulnerable to noise. In contrast, we establish the triplet similarity preserving function to simultaneously preserve the inter- and intra-modal similarity relationship in the common space and in each modal space, respectively. Thus, the proposed algorithm has a strong robustness to noise. In each modal space, to ensure that the generated features have the same semantic information as the sample labels, we establish a linear classifier and require that the generated features’ classification results be consistent with the sample labels. We conducted cross-modal retrieval comparative experiments on two widely used benchmark datasets—Pascal Sentence and Wikipedia. For the image to text task, our proposed method improved the mAP values by 1% and 0.7% on the Pascal sentence and Wikipedia datasets, respectively. Correspondingly, the proposed method separately improved the mAP values of the text to image performance by 0.6% and 0.8% on the Pascal sentence and Wikipedia datasets, respectively. The experimental results show that the proposed algorithm is better than the other state-of-the-art methods.
Quantum-Dot Qubits Kept Under Control

Quantum-Dot Qubits Kept Under ControlTwo studies improve the status of artificial atoms-called quantum dots
The Influence of Educational Philosophy on the Class Management of Primary Schools in Shanghai

Starting from the four educational philosophies of essentialism, eternity, progressivism and social reconstruction, and taking the classroom management of Shanghai primary school as the research object, this paper makes an in-depth and detailed analysis and Exploration on the choice of educators' educational philosophy. Based on the research results, the author puts forward some suggestions on the application of educational philosophy in classroom management practice, and points out the limitations of current application and possible research directions in the future, in order to further promote the effective combination of educational philosophy and classroom teaching activities and promote the innovation and development of classroom management.
5G and F5G Converged Network Architecture for Fusion System of Video and 3D Model in Intelligent Park

In the construction of intelligent park based on digital twins, the fusion system of HD(High-Definition) video and the 3D model in intelligent park puts forward high requirements for network bandwidth and delay. At present, the 5G network solution of the intelligent park is still under exploration, and the independent 5G network architecture or independent Fixed-5G network architecture has limitations in supporting the full scene application of the park. This paper proposes 5G and F5G converged network architecture for fusion system of video and 3D model in intelligent park. 5G is flexibly deployed for wireless access scenarios, F5G is stable and reliable for fixed access scenarios, realizing efficient and comprehensive network coverage. The architecture of fusion system of video and 3D model in intelligent park based on 5G and F5G converged network is proposed. A video adaptive scheduling model is proposed for video transmission in the fusion of video and 3D model, which can improve the quality of user experience. Experiments show that the model can realize dynamic scheduling of video under different network bandwidths, and reduce the video transmission delay in the case of low energy consumption.
An Accurate Employment Prediction Model Based on Collaborative Filtering Algorithm

Aiming at the problems of single information resources, low matching degree and difficult employment of graduates on the college employment guidance platform, this paper proposes a college accurate employment model based on collaborative filtering. By extracting the data of fresh graduates and alumni and the feature database collection of employers and enterprises, this paper compares the feature similarity between fresh graduates and previous graduates, The similarity value between fresh graduates and business units is calculated according to the signing enterprises of previous graduates, the job search heat of business units is obtained by using random walk algorithm, and the list of recommended units that fresh graduates are most interested in is obtained by using comprehensive weight algorithm. The experimental test shows that the function of the system model meets the design requirements of precision employment, and can effectively improve the efficiency of graduates to obtain employment and recruitment information.
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information

Recent studies have shed some light on a common pitfall of Neural Machine Translation (NMT) models, stemming from their struggle to disambiguate polysemous words without lapsing into their most frequently occurring senses in the training corpus.In this paper, we first provide a novel approach for automatically creating high-precision sense-annotated parallel corpora, and then put forward a specifically tailored fine-tuning strategy for exploiting these sense annotations during training without introducing any additional requirement at inference time.The use of explicit senses proved to be beneficial to reduce the disambiguation bias of a baseline NMT model, while, at the same time, leading our system to attain higher BLEU scores than its vanilla counterpart in 3 language pairs.
Learning to Embed Multi-Modal Contexts for Situated Conversational Agents

The Situated Interactive Multi-Modal Conversations (SIMMC) 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i.e. visual appearances of objects and user utterances.It consists of four subtasks, multi-modal disambiguation (MM-Disamb), multi-modal coreference resolution (MM-Coref), multi-modal dialog state tracking (MM-DST), and response retrieval and generation.While many task-oriented dialog systems usually tackle each subtask separately, we propose a jointly learned multi-modal encoderdecoder that incorporates visual inputs and performs all four subtasks at once for efficiency.This approach won the MM-Coref and response retrieval subtasks and was nominated runnerup for the remaining subtasks using a single unified model at the 10th Dialog Systems Technology Challenge (DSTC10), setting a high bar for the novel task of multi-modal task-oriented dialog systems.
IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations

What is the relation between a word and its description, or a word and its embedding?Both descriptions and embeddings are semantic representations of words.But, what information from the original word remains in these representations?Or more importantly, which information about a word do these two representations share?Definition Modeling and Reverse Dictionary are two opposite learning tasks that address these questions.The goal of the Definition Modeling task is to investigate the power of information laying inside a word embedding to express the meaning of the word in a humanly understandable way -as a dictionary definition.Conversely, the Reverse Dictionary task explores the ability to predict word embeddings directly from its definition.In this paper, by tackling these two tasks, we are exploring the relationship between words and their semantic representations.We present our findings based on the descriptive, exploratory, and predictive data analysis conducted on the COD-WOE dataset.We give a detailed overview of the systems that we designed for Definition Modeling and Reverse Dictionary tasks, and that achieved top scores on SemEval-2022 CODWOE challenge in several subtasks.We hope that our experimental results concerning the predictive models and the data analyses we provide will prove useful in future explorations of word representations and their relationships.
Automatic scoring of short answers using justification cues estimated by BERT

Automated scoring technology for short-answer questions has been attracting attention to improve the fairness of scoring and reduce the burden on the scorer. In general, a large amount of data is required to train an automated scoring model. The training data consists of the answer texts and the scoring data assigned to them. It may also include annotations indicating key word sequences. These data must be prepared manually, which is costly. Many previous studies have created models with large amounts of training data specific to each question. This paper aims to achieve equivalent performance with less training data by utilizing a BERT model that has been pre-trained on a large amount of general text data not necessarily related to short answer questions. On the RIKEN dataset, the proposed method reduces the training data from the 800 data required in the past to about 400 data, and still achieves scoring accuracy comparable to that of humans.
PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model Fusion

This paper describes our system used in the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI) .Multimedia automatic misogyny recognition consists of the identification of misogynous memes, taking advantage of both text and images as sources of information.The task will be organized around two main subtasks: Task A is a binary classification task, which should be identified either as misogynous or not misogynous.Task B is a multi-label classification task, in which the types of misogyny should be identified in potential overlapping categories, such as stereotype, shaming, objectification, and violence.In this paper, we proposed a system based on multitask learning for multi-modal misogynous detection in memes.Our system combined image features with text features to train a multi-label classification.The prediction results were obtained by the simple weighted average method of the results with different fusion models, and the results of Task A were corrected by Task B. Our system achieves a test accuracy of 0.755 on Task A (ranking 3rd on the final leaderboard) and the accuracy of 0.731 on Task B (ranking 1st on the final leaderboard).
USST’s System for AutoSimTrans 2022

This paper describes our submitted text-to-text Simultaneous translation (ST) system, which won the second place in the Chinese→English streaming translation task of AutoSimTrans 2022.Our baseline system is a BPE-based Transformer model trained with the PaddlePaddle framework.In our experiments, we employ data synthesis and ensemble approaches to enhance the base model.In order to bridge the gap between general domain and spoken domain, we select in-domain data from a general corpus and mix them with a spoken corpus for mixed fine-tuning.Finally, we adopt a fixed wait-k policy to transfer our full-sentence translation model to simultaneous translation model.Experiments on the development data show that our system outperforms the baseline system.
Analysing the Correlation between Lexical Ambiguity and Translation Quality in a Multimodal Setting using WordNet

Multimodal Neural Machine Translation is focusing on using visual information to translate sentences in the source language into the target language.The main idea is to utilise information from visual modalities to promote the output quality of the text-based translation model.Although the recent multimodal strategies extract the most relevant visual information in images, the effectiveness of using visual information on translation quality changes based on the text dataset.Due to this, this work studies the impact of leveraging visual information in multimodal translation models of ambiguous sentences.Our experiments analyse the Multi30k evaluation dataset and calculate ambiguity scores of sentences based on the WordNet hierarchical structure.To calculate the ambiguity of a sentence, we extract the ambiguity scores for all nouns based on the number of senses in WordNet.The main goal is to find in which sentences, visual content can improve the text-based translation model.We report the correlation between the ambiguity scores and translation quality extracted for all sentences in the English-German dataset.
A Minimal Computational Improviser Based on Oral Thought

We describe our system for playing a minimal improvisational game in a group.In Chain Reaction, players collectively build a chain of word pairs or solid compounds.The game emphasizes memory and rapid improvisation, while absurdity and humor increases during play.Our approach is unique in that we have grounded our work in the principles of oral culture according to Walter Ong, an early scholar of orature.We show how a simple computer model can be designed to embody many aspects of oral poetics, suggesting design directions for other work in oral improvisation and poetics.The opportunities for our system's further development include creating culturally specific automated players; situating play in different temporal, physical, and social contexts; and constructing a more elaborate improvisor.
SHARP: Search-Based Adversarial Attack for Structured Prediction

Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations.In this work, we introduce SHARP, a new attack method that formulates the black-box adversarial attack as a search-based optimization problem with a specially designed objective function considering sentence fluency, meaning preservation and attacking effectiveness.Additionally, three different searching strategies are analyzed and compared, i.e., Beam Search, Metropolis-Hastings Sampling, and Hybrid Search.We demonstrate the effectiveness of our attacking strategies on two challenging structured prediction tasks: part-of-speech (POS) tagging and dependency parsing.Through automatic and human evaluations, we show that our method performs a more potent attack compared with pioneer arts.Moreover, the generated adversarial examples can be used to successfully boost the robustness and performance of the victim model via adversarial training.But stocks kept falling. Attacker Victim Model SourceAdv. Example And then stocks are falling.And then stocks are falling .But stocks kept
End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022

This paper describes the system submitted to AutoSimTrans 2022 from Huawei Noah's Ark Lab, which won the first place in the audio input track of the Chinese-English translation task.Our system is based on RealTranS, an end-to-end simultaneous speech translation model.We enhance the model with pretraining, by initializing the acoustic encoder with ASR encoder, and the semantic encoder and decoder with NMT encoder and decoder, respectively.To relieve the data scarcity, we further construct pseudo training corpus as a kind of knowledge distillation with ASR data and the pretrained NMT model.Meanwhile, we also apply several techniques to improve the robustness and domain generalizability, including punctuation removal, tokenlevel knowledge distillation and multi-domain finetuning.Experiments show that our system significantly outperforms the baselines at all latency and also verify the effectiveness of our proposed methods.
Learning to Generate Examples for Semantic Processing Tasks

Even if recent Transformer-based architectures, such as BERT, achieved impressive results in semantic processing tasks, their finetuning stage still requires large scale training resources.Usually, Data Augmentation (DA) techniques can help to deal with low resource settings.In Text Classification tasks, the objective of DA is the generation of well-formed sentences that (i) represent the desired task category and (ii) are novel with respect to existing sentences.In this paper, we propose a neural approach to automatically learn to generate new examples using a pre-trained sequence-tosequence model.We first learn a task-oriented similarity function that we use to pair similar examples.Then, we use these example pairs to train a model to generate examples.Experiments in low resource settings show that augmenting the training material with the proposed strategy systematically improves the results on text classification and natural language inference tasks by up to 10% accuracy, outperforming existing DA approaches.
SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi

Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts.We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis.To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70%.We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.
Findings of the Third Workshop on Automatic Simultaneous Translation

This paper reports the results of the shared task we hosted on the Third Workshop of Automatic Simultaneous Translation (AutoSimTrans). The shared task aims to promote the development of text-to-text and speech-to-text simultaneous translation, and includes Chinese-English and English-Spanish tracks. The number of systems submitted this year has increased fourfold compared with last year. Additionally, the top 1 ranked system in the speech-to-text track is the first end-to-end submission we have received in the past three years, which has shown great potential. This paper reports the results and descriptions of the 14 participating teams, compares different evaluation metrics, and revisits the ranking method.