Reviewerly/exHarmony huggingface dataset:
Unsupervised Noisy Tracklet Person Re-identification

Existing person re-identification (re-id) methods mostly rely on supervised model learning from a large set of person identity labelled training data per domain. This limits their scalability and usability in large scale deployments. In this work, we present a novel selective tracklet learning (STL) approach that can train discriminative person re-id models from unlabelled tracklet data in an unsupervised manner. This avoids the tedious and costly process of exhaustively labelling person image/tracklet true matching pairs across camera views. Importantly, our method is particularly more robust against arbitrary noisy data of raw tracklets therefore scalable to learning discriminative models from unconstrained tracking data. This differs from a handful of existing alternative methods that often assume the existence of true matches and balanced tracklet samples per identity class. This is achieved by formulating a data adaptive image-to-tracklet selective matching loss function explored in a multi-camera multi-task deep learning model structure. Extensive comparative experiments demonstrate that the proposed STL model surpasses significantly the state-of-the-art unsupervised learning and one-shot learning re-id methods on three large tracklet person re-id benchmarks.
Methods for bypassing the check of final qualifying works for the amount of borrowed text and ways to identify them. Inserting large amounts of third-party text. Part 2

The article is devoted to the issues of checking student papers for the amount of borrowed text and to the problems of controlling the circumvention of this check. The article considers the implementation of a number of methods of the widely used method of bypassing validation associated with the insertion of a large amount of third-party text. Shows various ways to detect this workaround in MS Word documents.
Improving motivational policy as part of the development strategy of an educational organization

This article reveals the issues of improving motivational policy as a significant element of the development strategy of an educational organization. The relevance of the problem is characterized by the influence of the corresponding motivational policy affecting the formation of effective activities of pedagogical workers, which allows you to determine the achievement of the strategic goals of the organization. One of the central problems of the organization's motivational policy can be considered to ensure the adequate linkage of wages with work results, due to the high workload of the pedagogical worker. Improving motivational policy in an educational organization, positive motivation develops, which activates a person's abilities, frees up his potential, and contributes to the achievement of the goals of the activity. Special attention is paid in the article to the consideration of non-standard methods of motivation in educational organizations that contribute to increasing the efficiency of pedagogical workers. When considering the designated topic, methods of theoretical analysis of the literature in the aspect of the studied questions, the study and synthesis of empirical data and experience, observation were used. The article developed some methodological recommendations to improve motivational policy as an element in the development of the strategy of the educational organization. The conclusions made and the proposed scientific and theoretical provisions can be useful to the leaders of educational organizations and have the opportunity to adapt to the conditions of the educational process.
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning

While designing inductive bias in neural architectures has been widely studied, we hypothesize that transformer networks are flexible enough to learn inductive bias from suitable generic tasks. Here, we replace architecture engineering by encoding inductive bias in the form of datasets. Inspired by Peirce's view that deduction, induction, and abduction are the primitives of reasoning, we design three synthetic tasks that are intended to require the model to have these three abilities. We specifically design these tasks to be synthetic and devoid of mathematical knowledge to ensure that only the fundamental reasoning biases can be learned from these tasks. This defines a new pre-training methodology called "LIME" (Learning Inductive bias for Mathematical rEasoning). Models trained with LIME significantly outperform vanilla transformers on four very different large mathematical reasoning benchmarks. Unlike dominating the computation cost as traditional pre-training approaches, LIME requires only a small fraction of the computation cost of the typical downstream task. The code for generating LIME tasks is available at https://github.com/tonywu95/LIME.
Re-labeling ImageNet: from Single to Multi-Labels, from Global to  Localized Labels

ImageNet has been arguably the most popular image classification benchmark, but it is also the one with a significant level of label noise. Recent studies have shown that many samples contain multiple classes, despite being assumed to be a single-label benchmark. They have thus proposed to turn ImageNet evaluation into a multi-label task, with exhaustive multi-label annotations per image. However, they have not fixed the training set, presumably because of a formidable annotation cost. We argue that the mismatch between single-label annotations and effectively multi-label images is equally, if not more, problematic in the training setup, where random crops are applied. With the single-label annotations, a random crop of an image may contain an entirely different object from the ground truth, introducing noisy or even incorrect supervision during training. We thus re-label the ImageNet training set with multi-labels. We address the annotation cost barrier by letting a strong image classifier, trained on an extra source of data, generate the multi-labels. We utilize the pixel-wise multi-label predictions before the final pooling layer, in order to exploit the additional location-specific supervision signals. Training on the re-labeled samples results in improved model performances across the board. ResNet-50 attains the top-1 classification accuracy of 78.9% on ImageNet with our localized multi-labels, which can be further boosted to 80.2% with the CutMix regularization. We show that the models trained with localized multi-labels also outperforms the baselines on transfer learning to object detection and instance segmentation tasks, and various robustness benchmarks. The re-labeled ImageNet training set, pre-trained weights, and the source code are available at {https://github.com/naver-ai/relabel_imagenet}.
TrNews: Heterogeneous User-Interest Transfer Learning for News  Recommendation

We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail. Luckily, in real-world recommendation services, some publisher (e.g., Daily news) may have accumulated a large corpus with lots of consumers which can be used for a newly deployed publisher (e.g., Political news). To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus. To tackle the heterogeneity of different user interests and of different word distributions across corpora, we design a translator-based transfer-learning strategy to learn a representation mapping between source and target corpora. The learned translator can be used to generate representations for unseen users in the future. We show through experiments on real-world datasets that TrNews is better than various baselines in terms of four metrics. We also show that our translator is effective among existing transfer strategies.
Streaming VR Games to the Broad Audience: A Comparison of the First-Person and Third-Person Perspectives

The spectatorship experience for virtual reality (VR) games differs strongly from its non-VR precursor. When watching non-VR games on platforms such as Twitch, spectators just see what the player sees, as the physical interaction is mostly unimportant for the overall impression. In VR, the immersive full-body interaction is a crucial part of the player experience. Hence, content creators, such as streamers, often rely on green screens or similar solutions to offer a mixed-reality third-person view to disclose their full-body actions. Our work compares the most popular realizations of the first-person and the third-person perspective in an online survey (N=217) with three different VR games. Contrary to the current trend to stream in third-person, our key result is that most viewers prefer the first-person version, which they attribute mostly to the better focus on in-game actions and higher involvement. Based on the study insights, we provide design recommendations for both perspectives.
Resolution-Based Distillation for Efficient Histology Image Classification

Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based methodology for improving the computational efficiency of histology image classification. The proposed approach is robust when used with images that have reduced input resolution and can be trained effectively with limited labeled data. Pre-trained on the original high-resolution (HR) images, our method uses knowledge distillation (KD) to transfer learned knowledge from a teacher model to a student model trained on the same images at a much lower resolution. To address the lack of large-scale labeled histology image datasets, we perform KD in a self-supervised manner. We evaluate our approach on two histology image datasets associated with celiac disease (CD) and lung adenocarcinoma (LUAD). Our results show that a combination of KD and self-supervision allows the student model to approach, and in some cases, surpass the classification accuracy of the teacher, while being much more efficient. Additionally, we observe an increase in student classification performance as the size of the unlabeled dataset increases, indicating that there is potential to scale further. For the CD data, our model outperforms the HR teacher model, while needing 4 times fewer computations. For the LUAD data, our student model results at 1.25x magnification are within 3% of the teacher model at 10x magnification, with a 64 times computational cost reduction. Moreover, our CD outcomes benefit from performance scaling with the use of more unlabeled data. For 0.625x magnification, using unlabeled data improves accuracy by 4% over the baseline. Thus, our method can improve the feasibility of deep learning solutions for digital pathology with standard computational hardware.
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks

It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks. This is particularly true for Bayesian models, as they are well-suited for safety-critical applications, in which adversarial attacks can have catastrophic outcomes. In this work, we take a deeper look at the adversarial robustness of Bayesian Neural Networks (BNNs). In particular, we consider whether the adversarial robustness of a BNN can be increased by model choices, particularly the Lipschitz continuity induced by the prior. Conducting in-depth analysis on the case of i.i.d., zero-mean Gaussian priors and posteriors approximated via mean-field variational inference, we find evidence that adversarial robustness is indeed sensitive to the prior variance.
Correlations transference and apparition of a metastable  decoherence-free subspace in dissipative reservoirs

The dynamic of correlations in a system composed of a two-mode quantum field coupled with the environment is studied. The quantum field corresponds to two entangled coherent states whose amplitude we vary up to the mesoscopic regime. We show that under the onset of decoherence, correlations in the quantum field are not lost but transferred to the environment. We also found that sudden transitions in the decoherence regimes appear along with the dynamics depending on the coherent states' amplitude. Increasing the amplitude of the entangled coherence state results in the apparition of a metastable decoherence-free subspace (DFS) in the field subsystem, and the transference of classical correlations freezes. This subspace only exists during a time interval that depends on the average number of photons. Interestingly, the reservoir subsystem also experiences the apparition of a DFS. Only quantum correlations are transferred while the DFS exists.
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words

How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words? We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives. We show that PLMs can be interpreted as serial dual-route models, i.e., the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words. This hypothesis is confirmed by a series of semantic probing tasks on which DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation. Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.
Detecting Fine-Grained Emotions on Social Media during Major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic.

The COVID-19 pandemic has affected the whole world in various ways. One type of impact is that communication, work, interaction, a great part of our lives has moved online on various platforms, with some of the most popular being the social media ones. Another, arguably less visible impact, is the emotional impact. Detecting and understanding emotions is important, to better discern the emotional health and well-being of the global population. Thus, in this work, we use a social media platform (Twitter) to analyse emotions in detail. Our contribution is twofold: (1) we propose EmoBERT, a new emotion-based variant of the BERT transformer model, able to learn emotion representations and outperform the state-of-the-art; (2) we provide a fine-grained analysis of the pandemic's effect in a major location, London, comparing specific emotions (annoyed, anxious, empathetic, sad) before and during the epidemic.
“Into a Thousand Parts Divide”

How does one navigate Shakespeare’s texts, and how precisely can one do it? This chapter will analyze the ways in which humans have fragmented and indexed Shakespeare’s texts throughout history to enable readers to traverse and manipulate them with ever-increasing specificity. It will extend the view to look at other types of symbolic interfaces to see that the trend toward fragmenting data into the smallest possible meaningful units as a means of conditioning it for transmission is a universal tendency. Shakespeare’s texts are perhaps unusual not in how they have been heavily fragmented for efficient handling by both man and machine, but rather in how they have resisted fragmentation.
Research Paper to Design and Develop an Algorithm for Optimization Chatbot

Digital era has witnessed the globe with the emergence of a special category of software bot-chatbot which is manifested in millions of popular and demanding applications. Prominance to study details behind popularity of chatbot and its characteristics which created a milestone and drastic changes in modern applications is quite fascinating. Aim is to detect potential knowledge gaps to identify, compare and evaluate chatbot. Objective is to propose an optimization chatbot model for evaluation with design and developing an optimization chatbot algorithm to predict levels of chatbot optimization. Focus on evaluating 16 chatbot-Botmother, Botpress, Botsify, Botsociety, Botstar, Bot.xo, Chatize, Chatfuel, Chengo, Clustaar, Crisp, Drift, Engati, Flow.xo, Flow.ai and Freshchat, based on 14 features visualflow builder, text chatbot, use, setup, tutorials, documentation, help, keywords, intents, entities, dialogflow integration, optimization A/B testing, multiple languages and live chat. Algorithm systematically computes scores of chatbot and feature, levels of optimization, mean, median, range, standard deviation and percentage of features optimized. The results depict importance of the distribution of key performance indicator features in chatbot, 9 optimized chatbot, 74.55% features in chatbot, 64.28% of optimized features. Thus, Optimization chatbot algorithm useful to predict aspects related to improving overall chatbot performance.
Comparison analysis of Rabin-Karp and Winnowing algorithms in automated essay answer assessment system

The role of technology during the Covid-19 pandemic has been evidently dominant, particularly in the teaching and learning process. The application of learning from home, enables E-Learning to serve as the primary learning medium. E-learning requires an automatic essay answer assessment feature to improve the objectives and efficiency of online learning activities. This study proposes the automated assessment solutions utilizing Rabin-Karp and Winnowing algorithms. The types of essay questions handled in this study include the free description and limited description types. The answer key data and answer data will be further preprocessed to be similarly calculated to the two algorithms in the process. Algorithm accuracy assessment is conducted by converting the value into a human rates version compared with the evaluation conducted by lecturers manually. This study indicated that Rabin-Karp's Algorithm had better accuracy with the slightest difference of 27.81%, compared to Winnowing's Algorithm accuracy with a more significant distinction of 44.32%.
Parsing Immune Correlates of Protection Against SARS-CoV-2 from Biomedical Literature.

After the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019, identification of immune correlates of protection (CoPs) have become increasingly important to understand the immune response to SARS-CoV-2. The vast amount of preprint and published literature related to COVID-19 makes it challenging for researchers to stay up to date on research results regarding CoPs against SARS-CoV-2. To address this problem, we developed a machine learning classifier to identify papers relevant to CoPs and a customized named entity recognition (NER) model to extract terms of interest, including CoPs, vaccines, assays, and animal models. A user-friendly visualization tool was populated with the extracted and normalized NER results and associated publication information including links to full-text articles and clinical trial information where available. The goal of this pilot project is to provide a basis for developing real-time informatics platforms that can inform researchers with scientific insights from emerging research.
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

Heterogeneous graphs are pervasive in practical scenarios, where each graph consists of multiple types of nodes and edges. Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information. However, most of the existing graph convolution approaches were designed for homogeneous graphs, and therefore cannot handle heterogeneous graphs. Some recent methods designed for heterogeneous graphs are also faced with several issues, including the insufficient utilization of heterogeneous properties, structural information loss, and lack of interpretability. In this paper, we propose HGConv, a novel Heterogeneous Graph Convolution approach, to learn comprehensive node representations on heterogeneous graphs with a hybrid micro/macro level convolutional operation. Different from existing methods, HGConv could perform convolutions on the intrinsic structure of heterogeneous graphs directly at both micro and macro levels: A micro-level convolution to learn the importance of nodes within the same relation, and a macro-level convolution to distinguish the subtle difference across different relations. The hybrid strategy enables HGConv to fully leverage heterogeneous information with proper interpretability. Moreover, a weighted residual connection is designed to aggregate both inherent attributes and neighbor information of the focal node adaptively. Extensive experiments on various tasks demonstrate not only the superiority of HGConv over existing methods, but also the intuitive interpretability of our approach for graph analysis.
An Efficient Generation Method based on Dynamic Curvature of the  Reference Curve for Robust Trajectory Planning

Trajectory planning is a fundamental task on various autonomous driving platforms, such as social robotics and self-driving cars. Many trajectory planning algorithms use a reference curve based Frenet frame with time to reduce the planning dimension. However, there is a common implicit assumption in classic trajectory planning approaches, which is that the generated trajectory should follow the reference curve continuously. This assumption is not always true in real applications and it might cause some undesired issues in planning. One issue is that the projection of the planned trajectory onto the reference curve maybe discontinuous. Then, some segments on the reference curve are not the image of any part of the planned path. Another issue is that the planned path might self-intersect when following a simple reference curve continuously. The generated trajectories are unnatural and suboptimal ones when these issues happen. In this paper, we firstly demonstrate these issues and then introduce an efficient trajectory generation method which uses a new transformation from the Cartesian frame to Frenet frames. Experimental results on a simulated street scenario demonstrated the effectiveness of the proposed method.
Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous  Rendering Machines

End-to-end neural networks have achieved promising performances in natural language generation (NLG). However, they are treated as black boxes and lack interpretability. To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how neural generators render an input dialogue act (DA) into an utterance. HRM consists of a renderer set and a mode switcher. The renderer set contains multiple decoders that vary in both structure and functionality. For every generation step, the mode switcher selects an appropriate decoder from the renderer set to generate an item (a word or a phrase). To verify the effectiveness of our method, we have conducted extensive experiments on 5 benchmark datasets. In terms of automatic metrics (e.g., BLEU), our model is competitive with the current state-of-the-art method. The qualitative analysis shows that our model can interpret the rendering process of neural generators well. Human evaluation also confirms the interpretability of our proposed approach.