Reviewerly/exHarmony huggingface dataset:
Average-Reward Off-Policy Policy Evaluation with Function Approximation

We consider off-policy policy evaluation with function approximation (FA) in average-reward MDPs, where the goal is to estimate both the reward rate and the differential value function. For this problem, bootstrapping is necessary and, along with off-policy learning and FA, results in the deadly triad (Sutton & Barto, 2018). To address the deadly triad, we propose two novel algorithms, reproducing the celebrated success of Gradient TD algorithms in the average-reward setting. In terms of estimating the differential value function, the algorithms are the first convergent off-policy linear function approximation algorithms. In terms of estimating the reward rate, the algorithms are the first convergent off-policy linear function approximation algorithms that do not require estimating the density ratio. We demonstrate empirically the advantage of the proposed algorithms, as well as their nonlinear variants, over a competitive density-ratio-based approach, in a simple domain as well as challenging robot simulation tasks.
Sentence Embeddings using Supervised Contrastive Learning

Sentence embeddings encode sentences in fixed dense vectors and have played an important role in various NLP tasks and systems. Methods for building sentence embeddings include unsupervised learning such as Quick-Thoughts and supervised learning such as InferSent. With the success of pretrained NLP models, recent research shows that fine-tuning pretrained BERT on SNLI and Multi-NLI data creates state-of-the-art sentence embeddings, outperforming previous sentence embeddings methods on various evaluation benchmarks. In this paper, we propose a new method to build sentence embeddings by doing supervised contrastive learning. Specifically our method fine-tunes pretrained BERT on SNLI data, incorporating both supervised crossentropy loss and supervised contrastive loss. Compared with baseline where fine-tuning is only done with supervised cross-entropy loss similar to current state-of-the-art method SBERT, our supervised contrastive method improves 2.8% in average on Semantic Textual Similarity (STS) benchmarks and 1.05% in average on various sentence transfer tasks.
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism

Representation learning on graphs that evolve has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. The propagation of information in graphs is important in learning dynamic graph representations, and most of the existing methods achieve this by aggregation. However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information propagation and thus affect the performance of the method. To alleviate this problem, we propose an aggregation-diffusion (AD) mechanism that actively propagates information to its neighbor by diffusion after the node updates its embedding through the aggregation mechanism. In experiments on two real-world datasets in the dynamic link prediction task, the AD mechanism outperforms the baseline models that only use aggregation to propagate information. We further conduct extensive experiments to discuss the influence of different factors in the AD mechanism.
Hierarchically Spatial Encoding Module for Chronic Stroke Lesion Segmentation

Since stroke is still the leading cause of death worldwide, neuroimaging research to investigate the mechanism under the brain reorganization and rehabilitation is becoming predominant. In such areas, lesion delineation is a critical step and manual tracing is the gold standard so far, but it is time-consuming and readily influenced by bias. In this work, we propose a volumetric convolutional neural network for lesion segmentation with a novel and well-motivated Hierarchically Spatial Encoding Module (HSEM) based on Recurrent Neural Network (RNN). The proposed HSEM leverages the inherent spatial characteristics of the human brain by encoding the context from various dimensions hierarchically, therefore enhancing the segmentation results. We evaluate our approach on a fresh-new benchmark data set ATLAS. Our method achieves the F1-score of 66.07%, precision of 69.32%, recall of 67.6%, and obtains superior performance compared with other state-of-the-arts. The experiments prove that our proposed module has strong robustness when embedded into the neural network structure and further verify the module can mimic the human brain to make a diagnostic decision with the existence of inherent anisotropy and spatial properties. Furthermore, our model has the potential to provide finer lesion segmentation in some cases where the lesion is not annotated by experts perfectly.
Trajectory tracking analysis of airborne data link antenna

With the development of weapons and equipment and changes in combat concepts, airborne data links have become more and more widely used in war communications. Whether their system performance is stable and reliable has gradually become a key indicator for measuring the combat effectiveness of the Air Force. This article mainly introduces the airborne data link to provide graphical information for combatants, solves the problem that people’s information cannot be shared synchronously, expands the transmission scale of data information, and greatly improves the situational awareness of the battlefield. This paper mainly studies the link between the airborne data link between the computer and the aircraft, and makes a substantial analysis of the trajectory tracking, which is helpful for the combatants to be familiar with the trajectory direction and internal structure, and provide clear trajectory information for the fighters Interpretation, you can see at a glance the graphics, and the information obtained by the combatants is consistent. This paper mainly uses the standard Kalman filter algorithm to study the basic characteristics of the target ballistic group motion, and based on the HLA-simulated development of GBR radar integration and completed the system test to verify the accuracy of the simulation signal processing. The experimental results of this paper show that the information acquisition rate of combatants under the airborne data link has been increased by 40%, and the consistency of simultaneous information acquisition has been improved by 90%. This is the future development direction of the theater.
Domain Generalization for Document Authentication against Practical Recapturing Attacks

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against a reference sample, we proposed a document recapture detection scheme by employing Siamese network to compare and extract distinct features in a recapture document image. The proposed algorithm takes advantages of both metric learning and image forensic techniques. Instead of adopting Euclidean distance-based loss function, we integrate the forensic similarity function with a triplet loss and a normalized softmax loss. After training with the proposed triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushes the recaptured samples apart. In the experiment, we consider practical domain generalization problems, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed schemes with different network backbones have consistently outperformed the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents that produced by different devices, we have achieved less than 5.00% APCER (Attack Presentation Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5% BPCER decision threshold.
Situation- and need-based method recommendation for coaching agile development teams

Interdisciplinary product development teams are responsible for the solution of complex problems and the creation of innovative ideas. Innovation Coaching within the approach of ASD-Agile Systems Design fosters agile product development processes by realizing agility in the work of development teams. With the aim to implement ASD-Innovation Coaching in practice, a support is needed to enable coaches to select and apply coaching methods based on the team´s situation and need. Within a systematic literature review, 217 proven coaching methods were identified and evaluated regarding their applicability and success in product development projects. Based on specific quality criteria a total of 51 methods are selected and presented in a systematic and intuitive process model.
Research on scratch detection and location technology based on vector product

Among the surface defects of many products, scratch defect is the most common. But the scratch itself has small scale and big shape difference. In this paper, combined with the actual technical needs of enterprises, aiming at the characteristics of scratch on the surface of thermos cap. We use MATLAB software to preprocess the image of thermos cap with scratch, and combine with vector product algorithm to identify the defect and calculate the position of the defect.
Implementation of Artificial Neural Network in Forecasting Sales Volume in Tokopedia Indonesia

Predicting sales is one way to get company profits.Tokopedia Indonesia is one of the marketplaces that is included in the type of e-commerce customer to customer (C2C).This research was conducted in order to help sellers in the Tokopedia Indonesia marketplace to predict the sales of their merchandise, so that sellers can prepare or stock items that are predicted to increase in sales by implementing Artificial Neural Networks.Artificial neural networks can help predict future sales values.The data is divided into training data and testing data.The results of the analysis of this study indicate that the network model obtained reaches an accuracy rate of 95%.
Effective Batching for Recurrent Neural Network Grammars

As a language model that integrates traditional symbolic operations and flexible neural representations, recurrent neural network grammars (RNNGs) have attracted great attention from both scientific and engineering perspectives. However, RNNGs are known to be harder to scale due to the difficulty of batched training. In this paper, we propose effective batching for RNNGs, where every operation is computed in parallel with tensors across multiple sentences. Our PyTorch implementation effectively employs a GPU and achieves x6 speedup compared to the existing C++ DyNet implementation with model-independent auto-batching. Moreover, our batched RNNG also accelerates inference and achieves x20-150 speedup for beam search depending on beam sizes. Finally, we evaluate syntactic generalization performance of the scaled RNNG against the LSTM baseline, based on the large training data of 100M tokens from English Wikipedia and the broad-coverage targeted syntactic evaluation benchmark. Our RNNG implementation is available at https://github.com/aistairc/rnng-pytorch/.
Microenseñanza para el desarrollo de competencias en la educación superior

The physical, biological, mental, psychological and emotional aspect is inert to the human being, it always accompanies in his life, in this respect, the knowledge of how the human brain learns has a great impact on education. Comprehending the brain mechanisms related to learning, genetic effects, context, emotions and age are some factors that support transforming educational strategies in order to make them more effective and better targeted, therefore, this article encloses a proposal in the concept of micro teaching to make better the meaningful learning on higher education.
Distance learning as both a crisis and window of opportunity in student’s perception at Siberian universities

The relevance of the topic is related to the need of understanding the positive and negative effects of universities transitioning to distance learning. They should be taken into account in the management of the process of digitalization of education. The aim of this article is to study the perception of students of Siberian universities of the transition to distance learning in 2020. The novelty of our approach to the study of this problem is the use of theoretical premises of the phenomenological paradigm and the ethnomethodological approach. This approach allows revealing the representations of distance learning in the personal reality of students as participants in the educational process. The research method is a semi-formalized in-depth interview. This study involved full-time 2nd-4th year students of three universities of the Krasnoyarsk Region. A total of 24 interviews were conducted. The results show that the majority of students perceived the emergency transition to distance learning as a crisis. The conditions of distance learning came into conflict with the subjective meanings and expectations of students in relation to the educational process. Students express negative assessments of the abrupt transition to distance learning, talk about a decrease in educational motivation, a sense of social deprivation after switching to distance learning. The respondents’ answers indicate the experience of anomie, the blurring of student identity, and the sense of losing the meaning of the educational process. Students with an increased level of subjectivity in educational activities more positively perceived the transition to distance learning and saw in it new opportunities for self-development. They have a higher level of internal motivation to achieve personal educational results, and are able to effectively organize their activities in the context of distance learning. In the course of distance learning, most students have become accustomed to this format: there has been a restructuring of everyday practices, the formation of new dispositions that organize educational activities. Despite this, all respondents consider distance learning as an auxiliary format that complements the traditional classroom form of organizing the educational process. This indicates that at present, digital learning technologies are poorly integrated into the system of higher education, which, according to the authors, has become the main factor in students’ experience of the transition to distance learning as a crisis phenomenon.
COMPETENCE APPROACH IN DESIGNING A PROFESSIONAL EDUCATIONAL PROGRAM MEDIADESIGN

The theoretical aspect is highlighted and practical ways of designing a professional educational program Mediadesign are presented. The features of professional training in the design direction have been investigated. Topical issues of ensuring the quality of the educational process of future media designers using the tools of the competence approach are considered. The article highlights the importance of modeling competencies and the peculiarities of introducing a competent approach into the process of professional training for applicants for higher education. The issues of the importance of modeling competencies and the peculiarities of introducing a competence-based approach into the process of professional training of future media designers in higher education are analyzed. It is proved that the quality of professional training of future media designers justifiably causes the need for the formation of the necessary competencies among students, which combine knowledge, skills, abilities; abilities and personality traits; general culture indicators; the ability to perform professional duties efficiently. The acquisition by future specialists of a system of design, creative, technological, communicative and interactive knowledge, skills and abilities is aimed at the formation of competencies, which should be considered as a result of high-quality professional training in the professional educational program Mediadesign. The specificity of a media designer's activity lies in the ability to carry out design developments in the context of modern world material and artistic culture, to creatively comprehend the national heritage and to master the skills of using advanced digital technologies as much as possible, provides ample opportunities for employment, professional growth and further scientific activities; the ability to perform complex specialized tasks and solve complex problems in the field of digital media, provides for the implementation of innovations in professional activities and is characterized by the uncertainty of conditions and requirements. Determining the competencies of a media designer makes it possible to determine a clear logical and consistent structure for the design of an educational and professional program Media design. The acquisition by future media designers of a system of knowledge, skills and abilities is aimed at the formation of integral, general and special competencies, which should be considered as a result of the educational process of professional training of Mediadesign.
Quality analysis of a learning media analog electronics on the Android platform with ISO 25010

The design of analog electronics learning media applications needs quality analysis. ISO 25010 is one of the standard references in measuring the quality of an Android-based application product. Analysis that can be done is by testing the compatibility and performance efficiency aspect of the media being developed. This study uses a Research and Development (R&amp;D) method with the ADDIE development model which will produced a quality analog electronics learning media application. Data collection was conducted used a cloud testing and direct testing. Before data collection was conducted validity of media by expert judgement. The results showed that media is "valid" and the quality of the compatibility aspect this media learning was supported on more than 208 smartphones android device and performance efficiency predicate “Satisfied” so that the application could be used for the learning process during the COVID-19 pandemic or tested on other quality aspects.
Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data

Variational inference enables approximate posterior inference of the highly over-parameterized neural networks that are popular in modern machine learning. Unfortunately, such posteriors are known to exhibit various pathological behaviors. We prove that as the number of hidden units in a single-layer Bayesian neural network tends to infinity, the function-space posterior mean under mean-field variational inference actually converges to zero, completely ignoring the data. This is in contrast to the true posterior, which converges to a Gaussian process. Our work provides insight into the over-regularization of the KL divergence in variational inference.
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training

Federated Learning allows the training of machine learning models by using the computation and private data resources of many distributed clients. Most existing results on Federated Learning (FL) assume the clients have ground-truth labels. However, in many practical scenarios, clients may be unable to label task-specific data due to a lack of expertise or resource. We propose SemiFL to address the problem of combining communication-efficient FL such as FedAvg with Semi-Supervised Learning (SSL). In SemiFL, clients have completely unlabeled data and can train multiple local epochs to reduce communication costs, while the server has a small amount of labeled data. We provide a theoretical understanding of the success of data augmentation-based SSL methods to illustrate the bottleneck of a vanilla combination of communication-efficient FL with SSL. To address this issue, we propose alternate training to `fine-tune global model with labeled data' and `generate pseudo-labels with the global model.' We conduct extensive experiments and demonstrate that our approach significantly improves the performance of a labeled server with unlabeled clients training with multiple local epochs. Moreover, our method outperforms many existing SSFL baselines and performs competitively with the state-of-the-art FL and SSL results.
Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning

Semi-Supervised Learning (SSL) has shown its strong ability in utilizing unlabeled data when labeled data is scarce. However, most SSL algorithms work under the assumption that the class distributions are balanced in both training and test sets. In this work, we consider the problem of SSL on class-imbalanced data, which better reflects real-world situations. In particular, we decouple the training of the representation and the classifier, and systematically investigate the effects of different data re-sampling techniques when training the whole network including a classifier as well as fine-tuning the feature extractor only. We find that data re-sampling is of critical importance to learn a good classifier as it increases the accuracy of the pseudo-labels, in particular for the minority classes in the unlabeled data. Interestingly, we find that accurate pseudo-labels do not help when training the feature extractor, rather contrariwise, data re-sampling harms the training of the feature extractor. This finding is against the general intuition that wrong pseudo-labels always harm the model performance in SSL. Based on these findings, we suggest to re-think the current paradigm of having a single data re-sampling strategy and develop a simple yet highly effective Bi-Sampling (BiS) strategy for SSL on class-imbalanced data. BiS implements two different re-sampling strategies for training the feature extractor and the classifier and integrates this decoupled training into an end-to-end framework. In particular, BiS progressively changes the data distribution during training such that in the beginning the feature extractor is trained effectively, while towards the end of the training the data is re-balanced such that the classifier is trained reliably. We benchmark our proposed bi-sampling strategy extensively on popular datasets and achieve state-of-the-art performances.
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection

Zero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents. 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting. A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage. To address this problem, we propose a novel framework that utilizes unseen class labels to learn Class-Transductive Intent Representations (CTIR). Specifically, we allow the model to predict unseen intents during training, with the corresponding label names serving as input utterances. On this basis, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately. CTIR is easy to implement and can be integrated with existing methods. Experiments on two real-world datasets show that CTIR brings considerable improvement to the baseline systems.