Reviewerly/exHarmony huggingface dataset:
A Stance Detection Approach Based on Generalized Autoregressive pretrained Language Model in Chinese Microblogs

Timely identification of Chinese Microblogs users' stance and tendency is of great significance for social managers to understand the trends of online public opinion. Traditional stance detection methods underutilize target information, which affects the detection effect. This paper proposes to integrate the target subject information into a Chinese Microblogs stance detection method based on a generalized autoregressive pretraining language model, and use the advantages of the generalized autoregressive model to extract deep semantics to weaken the high randomness of Microblogs self-media text language and lack of grammar. The impact of norms on text modeling. First carry out microblog data preprocessing to reduce the influence of noise data on the detection effect; then connect the target subject information and the text sequence to be tested into the XLNet network for fine-tuning training; Finally, the fine-tuned XLNet network is combined with the Softmax regression model for stance classification. The experimental results show that the value of the proposed method in the NLPCC2016 Chinese Microblogs detection and evaluation task reaches 0.75, which is better than the existing public model, and the effect is improved significantly.
Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction

In implicit discourse relation classification, we want to predict the relation between adjacent sentences in the absence of any overt discourse connectives. This is challenging even for humans, leading to shortage of annotated data, a fact that makes the task even more difficult for supervised machine learning approaches. In the current study, we perform implicit discourse relation classification without relying on any labeled implicit relation. We sidestep the lack of data through explicitation of implicit relations to reduce the task to two sub-problems: language modeling and explicit discourse relation classification, a much easier problem. Our experimental results show that this method can even marginally outperform the state-of-the-art, in spite of being much simpler than alternative models of comparable performance. Moreover, we show that the achieved performance is robust across domains as suggested by the zero-shot experiments on a completely different domain. This indicates that recent advances in language modeling have made language models sufficiently good at capturing inter-sentence relations without the help of explicit discourse markers.
TNCR: Table Net Detection and Classification Dataset.

We present TNCR, a new table dataset with varying image quality collected from free websites. The TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 high-quality labeled images. In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Cascade Mask R-CNN with ResNeXt-101-64x4d Backbone Network achieves the highest performance compared to other methods with a precision of 79.7%, recall of 89.8%, and f1 score of 84.4% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification, and structure recognition. The dataset and trained model checkpoints are available at this https URL.
Using NVivo for Mixed Methods Research

This chapter introduces NVivo's tools within the context of the specific mixed methods analysis and integrative functions they serve. Mixed methods research might include any or all of the following tasks: Capacities and tools provided within NVivo to support tasks involved in mixed methods analysis include: Effective use of software tools involves working out how best to apply them to the tasks that need to be undertaken. When a survey dataset is imported into NVivo it retains its tabular format. In NVivo, three tools are used for these types of analyses-framework analysis, the crosstab, and the matrix query. The particular strength offered to mixed methods researchers by NVivo is its capacity to flexibly manage very complex data sources and combinations through its use of codes, cases, sets, and matrix coding queries. NVivo provides a getting started tutorial for new users of the program whenever a new project is created in the software.
Anytime Inference with Distilled Hierarchical Neural Ensembles

© 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
Development of an Illumination Invariant Face Recognition System

The Face recognition systems have gained much attention for applications in surveillance, access control, forensics, border control. Face recognition systems encounter challenges due to variation in illumination, pose, expression, occlusion and most importantly, aging. The effect of the intensity of light on recognition image in contract with gallery image significantly affect the face recognition system. In this study, an illumination invariant Face Recognition System is developed using a 4-layered Convolutional Neural Network (CNN). The proposed system was able to recognize the different degree of face Illuminated image, thus making the model Illumination Invariant Face Recognition system. The variations caused by illumination was modelled as a form of light varying noise, and it was validated by computing its error statistics and comparing its performance with existing models found in literature. The result of the study showed that an adaptive and robust face recognition system that is illumination invariant could be achieved with CNN. The recognition accuracy achieved by the study was 99.22% with five (5) epochs and iteration of 85.
Asynchronous Teacher Guided Bit-wise Hard Mining for Online Hashing

Online hashing for streaming data has attracted increasing attention recently. However, most existing algorithms focus on batch inputs and instance-balanced optimization, which is limited in the single datum input case and does not match the dynamic training in online hashing. Furthermore, constantly updating the online model with new-coming samples will inevitably lead to the catastrophic forgetting problem. In this paper, we propose a novel online hashing method to handle the above-mentioned issues jointly, termed Asynchronus Teacher-Guided Bit-wise Hard Mining for Online Hashing. Firstly, to meet the needs of datum-wise online hashing, we design a novel binary codebook that is discriminative to separate different classes. Secondly, we propose a novel semantic loss (termed bit-wise attention loss) to dynamically focus on hard samples of each bit during training. Last but not least, we design a asynchronous knowledge distillation scheme to alleviate the catastrophic forgetting problem, where the teacher model is delaying updated to maintain the old knowledge, guiding the student model learning. Extensive experiments conducted on two public benchmarks demonstrate the favorable performance of our method over the state-of-the-arts.
Virtual Trial Room

In this covid pandemic situation retailers are finding a better option for their e- commerce businesses to boost up. This project will definitely help them to achieve a great marketing strategy with the help of the internet and it also has a wide scope. This web-based application will help the user to try clothes online right sitting at your home. This application uses OpenCV python for identifying the user’s body and to superimpose the clothes selected by the user. This will help the user to have the benefit of trying each and every attire they may like and also don’t waste their time by physically going through the shops. This is a user- friendly application that uses HTML + CSS+ JavaScript for the frontend which is open source and easy to use also it uses OpenCV which contains some inbuild functions which is easily accessible on the backend process. The net price of the current similar application is around 2.7 lakhs. So, working on such a project will help us on understanding of OpenCV website development structure of image processing. This application has a wide scope in the future as the e- commerce business has boosted up to 66% in the past 5 years.
Reinforcement learning with rare significant events

This paper shows that the CMAES direct policy search method fares significantly better than PPO gradient policy search for a reinforcement learning task where significant events are rare.
Efficient Retrieval of Human Motion Episodes Based on Indexed Motion-Word Representations

With the increasing availability of human motion data captured in the form of 2D or 3D skeleton sequences, more complex motion recordings need to be processed. In this paper, we focus on similarity-based indexing and efficient retrieval of motion episodes — medium-sized skeleton sequences that consist of multiple semantic actions and correspond to some logical motion unit (e.g. a figure skating performance). As a first step toward efficient retrieval, we apply the motion-word technique to transform spatio-temporal skeleton sequences into compact text-like documents. Based on these documents, we introduce a two-phase retrieval scheme that first finds a set of candidate query results and then re-ranks these candidates with more expensive application-specific methods. We further index the motion-word documents using inverted files, which allows us to retrieve the candidate documents in an efficient and scalable manner. We also propose additional query-reduction techniques that accelerate both the retrieval phases by removing semantically irrelevant parts of the motion query. Experimental evaluation is used to analyze the effects of the individual proposed techniques on the retrieval efficiency and effectiveness.
Fight Action Recognition Method using AI Deep Learning

In this study, we developed an AI deep learning-based fighting behavior recognition method for a video surveillance system and proved its effectiveness through various experiments. The proposed method consists of a two-step fighting behavior recognition framework. First, continuous video frames of the target surveillance video are transmitted to the Inflated 3D ConvNet (I3D) network, which shows a good behavior-recognition performance, to extract the spatiotemporal features. These extracted 3D features are then used as the inputs in the next step, where a fight situation is detected using a classification model consisting of a fully connected layer. To use the proposed aggressive behavior detection framework effectively, first, it is necessary to train the fight detection model. However, it is not possible to collect sufficient fighting videos in various outdoor environments. To overcome this limitation, we generated a large amount of learning data through data augmentation. Therefore, instead of directly learning from the training videos transmitted to the I3D network, the classifier trains itself to recognize various fighting actions using the Kinetics video dataset. That is, the action features are extracted from the transmitted consecutive frames using the pretrained I3D network and subsequently used to train the fully connected layer classification model. In addition, we proposed a learning method that includes recognizing ambiguous conflict boundaries using multiple instance learning to mitigate the ambiguous starting and ending of the contention videos. The effectiveness of the proposed method was verified through several experiments by drawing comparisons between the present results and those of the previously reported studies.
DEVELOPMENT OF INSTRUMENTAL AND METHODOLOGICAL SUPPORT FOR THE ASSESSMENT OF STUDENTS ' KNOWLEDGE IN MATHEMATICS IN THE FRAMEWORK OF HYBRID INTELLIGENT LEARNING ENVIRONMENT

The article is devoted to the development of an integrated complex of knowledge base and artifi-cial intelligence tools for assessing students' knowledge in mathematics within the framework of a hybrid intellectual learning environment. In accordance with the structural diagrams of mod-els of intellectual assessment of knowledge and creative potential of schoolchildren in mathe-matics, the structure of the corresponding knowledge base is formalized. The knowledge base is developed within the framework of data mining methods to create a module for monitoring and assessing knowledge in mathematics. As a methodological basis for the development of a knowledge base, the idea of V.P. Bespalko about the representation of the possible structure of human activity in the form of suc-cessive levels of assimilation, reflecting the development of the student's experience in a given subject in the learning process. The levels of assimilation (student, typical, heuristic, research) are determined through the ability to solve problems of the corresponding type. A formalized in-dicator of a student's achievement of a particular level of assimilation of educational material (level of training) is the coefficient of assimilation; assessment of the assimilation of material on a specific topic or section is provided for both at each of the above levels, and in general. The examples of the formation of a knowledge base for assessing the skills and abilities of schoolchildren in algebra are considered. To model the educational process, we propose methods for solving clustering, dimen-sionality reduction and classification problems based on machine learning and artificial neural networks. The foundations for the development of integrated complexes of knowledge bases and artificial intelligence tools to ensure the educational process are presented. The results obtained are aimed at creating methods that provide the processes of operational learning, control and assessment of knowledge, competencies and procedures, the level of formation of subject and professional competencies of students in the information and educational intellectual environment.
Breast Cancer Tumor Detection

Women in India confront serious fatalities such as respiratory difficulties, but some also confront serious diseases in the form of breast cancer, which differs for each lady; This dangerous illness has been detected in more than half of middle-aged women. Breast cancer is found by looking for lumps in women's breasts that look like tumors. These anomalies' cells can be treated right away if they're found. However, benign tumors, which are fully non-cancerous, can form lumps with cells of the same size and no structural changes between them. Machine Learning(ML) techniques are frequently utilized to create tools for physicians and diagnosis of carcinoma, which can dramatically improve patient survival rates. The goal of this project is to create a machine learning system that can predict if a tumor is benign or malignant, as well as visualize the properties of both cancers will next be illustrated via graph plotting. Support Vector Classifier(SVC), Logistic Regression, Decision Tree Classifier, and KNN are the used Machine Learning(ML) Algorithms for Breast Cancer Tumor Detection. To compare and assess the accuracy and ROC plotting performance of Machine Learning Classifiers.
Controllable cardiac synthesis via disentangled anatomy arithmetic

Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such underrepresentation. Assuming a proper representation, the idea of a latent vector could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed disentangled anatomy arithmetic, in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e.g. MRI), plausible new cardiac images are created with the target characteristics. To encourage a realistic combination of anatomy factors after the arithmetic step, we propose a localized noise injection network that precedes the generator. Our model is used to generate realistic images, pathology labels, and segmentation masks that are used to augment the existing datasets and subsequently improve post-hoc classification and segmentation tasks. Code is publicly available at this https URL.
Measuring Satisfaction and Loyalty of Guests Based on Vietnamese Hotel Online Reviews

Measuring customer satisfaction is a key task for hotels today. Analyzing online reviews of experienced guests will help the managers to know if guests are satisfied or dissatisfied with the service that they provided. Hence, they have solutions to improve service quality. This study presents a method to measure guest satisfaction based on sentiment lexicon that is developed for hospitality domain to increase the accuracy of the analysis results. Actual data is downloaded from TripAdvisor with 35 four-star to five-star hotels of five cities in Vietnam to analyze guest satisfaction that shows that nearly 80% of customers are satisfied with the quality of Vietnamese hotels. Based on data analysis, the study also evaluating guest loyalty through phrases like “came here several,” “come back,” “recommend,” etc. This rate corresponds to the number that was reported by the Vietnam National Administration of Tourism.
Data vs classifiers, who wins?

The classification experiments covered by machine learning (ML) are composed by two important parts: the data and the algorithm. As they are a fundamental part of the problem, both must be considered when evaluating a model's performance against a benchmark. The best classifiers need robust benchmarks to be properly evaluated. For this, gold standard benchmarks such as OpenML-CC18 are used. However, data complexity is commonly not considered along with the model during a performance evaluation. Recent studies employ Item Response Theory (IRT) as a new approach to evaluating datasets and algorithms, capable of evaluating both simultaneously. This work presents a new evaluation methodology based on IRT and Glicko-2, jointly with the decodIRT tool developed to guide the estimation of IRT in ML. It explores the IRT as a tool to evaluate the OpenML-CC18 benchmark for its algorithmic evaluation capability and checks if there is a subset of datasets more efficient than the original benchmark. Several classifiers, from classics to ensemble, are also evaluated using the IRT models. The Glicko-2 rating system was applied together with IRT to summarize the innate ability and classifiers performance. It was noted that not all OpenML-CC18 datasets are really useful for evaluating algorithms, where only 10% were rated as being really difficult. Furthermore, it was verified the existence of a more efficient subset containing only 50% of the original size. While Randon Forest was singled out as the algorithm with the best innate ability.
The Present and Future Prospects of the University in the On-tact Era

Covid-19 사태가 2년째 이어지고 있는 현재, 일상의 거의 모든 분야가 급속도로 비대면화되고 ‘온택트(On-tact)’라는 신조어가 만들어질 정도로 온라인의 영향력과 파급력이 커지고 있는 상황에서 (대학)교육의 지형 또한 급격히 변화하고 있어 그에 대한 전방위적인 대책마련이 강력히 요구되고 있는 실정이다. 특히, 코로나 사태 이전부터 예측되었던 학령인구 급감과 4차 산업혁명으로 인한 대학 위기가 팬데믹으로 인해 본격화되고 있기 때문에 뉴노멀의 급물살에 휩쓸린 고등교육계의 현재를 진단하고 장래의 위기 극복 방안을 논의하려는 움직임이 학계뿐만 아니라 매일 발 빠르게 업데이트되는 미디어 매체들을 통해서도 급박하게 이루어지고 있다. 본 논문은 이러한 논의들을 바탕으로 온택트 시대가 가속화 시킨 대학의 위기와 향후 전망을 범박하게나마 정리해보려는 시도이다. 소위 ‘뉴노멀 시대 온택트 환경’에서 대학교육의 현실을 진단해본 후, 대학의 역할과 미래에 대해서도 고찰해볼 것이다.As the COVID-19 has spread and prolonged worldwide, the ‘Untact’ society is becoming routinized. The impact and influence of online system is rapidly growing to the extent that a neologism ‘on-tact’ is widely used, even the topography of (university) education is rapidly changing, and thus there is a strong demand for countermeasures. In particular, the crisis of the university due to the 4th industrial revolution and the rapid decline in the school-age population predicted even before the COVID-19 outbreak is now in full swing. Currently, various experts from government, academia, and mass media are making efforts to diagnose the present of the higher education world, which has been swept by the rapid current of the new normal, and discuss ways to overcome the crisis in the future. Based on these discussions, this paper aims to summarize what are the effects of “On-tact environment” accelerated by COVID-19 pandemic on the university and to forecast the future of higher education in the New Normal Era.
Pengaruh Pembelajaran CTL Dengan Kemampuan Awal Matematika Terhadap Pemahaman Konsep Matematis Siswa Sekolah Dasar

The purpose of this study was to determine whether there was an effect of CTL learning and conventional learning with early mathematics abilities on the understanding of the fourth grade students of SD Negeri 117471 Sibargot. The research method used was an experiment using pre-test and post-test control group design. Hypothesis testing in this study used a two-way analysis of variance (ANOVA) test with a significance level of α = 0.05. The results of this study indicate that the sig. for CTL (MP₁) and Conventional Learning (MP₂) is the result of tcount -2.911&gt; ttable 2.002, so it can be concluded that H1 is accepted, which means that there is a significant influence of the learning model on understanding mathematical concepts; sig value. for Mathematical Initial Ability (KAM) with the result of tcount -6.434&gt; ttable 2.002 shows that the mean of the first group, namely high initial ability (KAMt) is higher than the second group, namely low initial ability (KAMr), so it can be concluded that H2 is accepted, which means that there is an influence. significant initial ability to understand mathematical concepts; sig value. for the effect of the Learning Method and KAM simultaneously on understanding mathematical concepts with the results of the value of Fcount 24.933&gt; Ftable 3.16. So it can be concluded that H3 is accepted, which means that there is an effect of learning methods and Early Ability simultaneously on understanding mathematical concepts.
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration.

Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain. We address these issues for a particularly pervasive type of domain shift called measurement shift, characterized by a change in measurement system (e.g. a change in sensor or lighting). In the source domain, we store a lightweight and flexible approximation of the feature distribution under the source data. In the target domain, we adapt the feature-extractor such that the approximate feature distribution under the target data realigns with that saved on the source. We call this method Feature Restoration (FR) as it seeks to extract features with the same semantics from the target domain as were previously extracted from the source. We additionally propose Bottom-Up Feature Restoration (BUFR), a bottom-up training scheme for FR which boosts performance by preserving learnt structure in the later layers of a network. Through experiments we demonstrate that BUFR often outperforms existing SFDA methods in terms of accuracy, calibration, and data efficiency, while being less reliant on the performance of the source model in the target domain.