Reviewerly/exHarmony huggingface dataset:
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding

We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional knowledge graph embedding methods map elements in a knowledge graph, including entities and relations, into continuous vector spaces by assigning them one or multiple specific embeddings (i.e., vector representations). Thus the number of embedding parameters increases linearly as the growth of knowledge graphs. In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities. To obtain the embeddings for the full set of entities, we encode their distinguishable information from their connected relations, k-nearest reserved entities, and multi-hop neighbors. We learn universal and entity-agnostic encoders for transforming distinguishable information into entity embeddings. This approach allows our proposed EARL to have a static, efficient, and lower parameter count than conventional knowledge graph embedding methods. Experimental results show that EARL uses fewer parameters and performs better on link prediction tasks than baselines, reflecting its parameter efficiency.
Effort Estimation in Agile Software Development: The State of the Practice in Colombia

Effort estimation is fundamental for developing software projects and critical for their success. This paper focuses on how Colombian agile practitioners perform effort estimates in agile projects. For this purpose, we conducted an exploratory survey study that involved 60 respondents. The main findings are the following: (1) Agile practitioners prefer estimation techniques based on Expert Judgement. (2) Most of the respondents perceive that their estimates have a medium accuracy level. (3) The determining effort drivers are features of the project team and the software to be built. (4) The use of datasets for estimation is not common. (5) Most of the results of related studies are similar to ours, with differences in terms of the roles involved and the techniques used.
Optimal Sparse Regression Trees

Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within the large literature on regression trees, there has been little effort towards full provable optimization, mainly due to the computational hardness of the problem. This work proposes a dynamic-programming-with-bounds approach to the construction of provably-optimal sparse regression trees. We leverage a novel lower bound based on an optimal solution to the k-Means clustering algorithm on one dimensional data. We are often able to find optimal sparse trees in seconds, even for challenging datasets that involve large numbers of samples and highly-correlated features.
Machines of Finite Depth: Towards a Formalization of Neural Networks

We provide a unifying framework where artificial neural networks and their architectures can be formally described as particular cases of a general mathematical construction---machines of finite depth. Unlike neural networks, machines have a precise definition, from which several properties follow naturally. Machines of finite depth are modular (they can be combined), efficiently computable, and differentiable. The backward pass of a machine is again a machine and can be computed without overhead using the same procedure as the forward pass. We prove this statement theoretically and practically via a unified implementation that generalizes several classical architectures---dense, convolutional, and recurrent neural networks with a rich shortcut structure---and their respective backpropagation rules.
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems

The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has been actively applied to solve many problems, such as robust dimensionality reduction and deep neural networks with orthogonal weights (Stiefel manifold). Although many optimization algorithms for minimax problems have been developed in the Euclidean setting, it is difficult to convert them into Riemannian cases, and algorithms for nonconvex minimax problems with nonconvex constraints are even rare. On the other hand, to address the big data challenges, decentralized (serverless) training techniques have recently been emerging since they can reduce communications overhead and avoid the bottleneck problem on the server node. Nonetheless, the algorithm for decentralized Riemannian minimax problems has not been studied. In this paper, we study the distributed nonconvex-strongly-concave minimax optimization problem over the Stiefel manifold and propose both deterministic and stochastic minimax methods. The Steifel manifold is a non-convex set. The global function is represented as the finite sum of local functions. For the deterministic setting, we propose DRGDA and prove that our deterministic method achieves a gradient complexity of O( epsilon(-2)) under mild conditions. For the stochastic setting, we propose DRSGDA and prove that our stochastic method achieves a gradient complexity of O( epsilon(-4)). The DRGDA and DRSGDA are the first algorithms for distributed minimax optimization with nonconvex constraints with exact convergence. Extensive experimental results on the Deep Neural Networks (DNNs) training over the Stiefel manifold demonstrate the efficiency of our algorithms.
Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks

We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly adopted in industry. Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization, and certification of the quantized representation is necessary to guarantee robustness. In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. Inspired by advances in robust learning of non-quantized networks, our training algorithm computes the gradient of an abstract representation of the actual network. Unlike existing approaches, our method can handle the discrete semantics of QNNs. Based on QA-IBP, we also develop a complete verification procedure for verifying the adversarial robustness of QNNs, which is guaranteed to terminate and produce a correct answer. Compared to existing approaches, the key advantage of our verification procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate experimentally that our approach significantly outperforms existing methods and establish the new state-of-the-art for training and certifying the robustness of QNNs.
Safety Validation of Learning-Based Autonomous Systems: A Multi-Fidelity Approach

In recent years, learning-based autonomous systems have emerged as a promising tool for automating many crucial tasks. The key question is how we can build trust in such systems for safety-critical applications. My research aims to focus on the creation and validation of safety frameworks that leverage multiple sources of information. The ultimate goal is to establish a solid foundation for a long-term research program aimed at understanding the role of fidelity in simulators for safety validation and robot learning.
Identify Event Causality with Knowledge and Analogy

Event causality identification (ECI) aims to identify the causal relationship between events, which plays a crucial role in deep text understanding. Due to the diversity of real-world causality events and difficulty in obtaining sufficient training data, existing ECI approaches have poor generalizability and struggle to identify the relation between seldom seen events. In this paper, we propose to utilize both external knowledge and internal analogy to improve ECI. On the one hand, we utilize a commonsense knowledge graph called ConceptNet to enrich the description of an event sample and reveal the commonalities or associations between different events. On the other hand, we retrieve similar events as analogy exam- ples and glean useful experiences from such analogous neigh- bors to better identify the relationship between a new event pair. By better understanding different events through exter- nal knowledge and making an analogy with similar events, we can alleviate the data sparsity issue and improve model gener- alizability. Extensive evaluations on two benchmark datasets show that our model outperforms other baseline methods by around 18% on the F1-value on average
Digital Literacy Assessment: Methodology, Conceptual Model and Measurement Tool

The development of new technologies, the extensive implementation of digital devices in all spheres of society contributed to the appearance of a new type of human literacy - digital literacy. Today, it ranks alongside other basic literacies such as numeracy, writing, and reading. Digital literacy has become an indicator to the new conditions of life, involving the use of digital technologies in education, family and professional activities; to some extent, it has become an indicator of the socialization of a modern person.&#x0D; Based on the analysis of international approaches to the assessment of digital literacy and related constructs, a conceptual model of its measurement is proposed, in which digital literacy is considered as a complex latent construct.&#x0D; The process of developing digital literacy assessment tools based on the Principled Assessment Design paradigm and the Evidence-Centered Design method is presented. &#x0D; An analysis of the results of digital literacy studies of students in grades 7-8 made it possible to confirm the quality of the developed tools for measuring digital literacy based on authentic performance-based tasks, to confirm the reliability and validity of the results of its measurement.
Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks

Objective: Scalable strategies to reduce the time burden and increase contact tracing efficiency are crucial during early waves and peaks of infectious transmission. Design: We enrolled a cohort of SARS-CoV-2-positive seed cases into a peer recruitment study testing social network methodology and a novel electronic platform to increase contact tracing efficiency. Setting: Index cases were recruited from an academic medical center and requested to recruit their local social contacts for enrollment and SARS-CoV-2 testing. Participants: A total of 509 adult participants enrolled over 19 months (384 seed cases and 125 social peers). Intervention: Participants completed a survey and were then eligible to recruit their social contacts with unique “coupons” for enrollment. Peer participants were eligible for SARS-CoV-2 and respiratory pathogen screening. Main Outcome Measures: The main outcome measures were the percentage of tests administered through the study that identified new SARS-CoV-2 cases, the feasibility of deploying the platform and the peer recruitment strategy, the perceived acceptability of the platform and the peer recruitment strategy, and the scalability of both during pandemic peaks. Results: After development and deployment, few human resources were needed to maintain the platform and enroll participants, regardless of peaks. Platform acceptability was high. Percent positivity tracked with other testing programs in the area. Conclusions: An electronic platform may be a suitable tool to augment public health contact tracing activities by allowing participants to select an online platform for contact tracing rather than sitting for an interview.
Query-Specific Embedding Co-Adaptation Improve Few-Shot Image Classification

Few-Shot Image Classification aims to identify unseen categories by a limited number of instances. Recently, some metric-based methods have attempted to generate more discriminative task-specific embeddings by embedding adaptation strategies. However, the generated embeddings are either query-agnostic or ignore local relations between instances in each category, resulting in limited performance improvement. To address the above issues, in this letter, we propose QS-CAN, a Query-Specific embedding Co-Adaptation Network, which generates task- and query-specific embeddings by fusing inter-class and intra-class information. The core modules of QS-CAN are Inter-Class Adapter(Inter-A) and Intra-Class Adapter(Intra-A). The Inter-Class Adapter encodes the global relationship within the task, pushing the different categories away from each other. At the same time, the Intra-Class Adapter focuses on modeling the local relationship within each category, pulling the instances within a category closer. Moreover, an Adaptive Fusion Module(AFM) is proposed to integrate two co-adapted embeddings to get a more discriminative space. Experiments show that our method performs comparably to other advanced methods on three widely used datasets.
Tandem of philosophical anthropology and philosophy of education as a soil for the formation of professional identity

Introduction . The problem of professional identity in modern socio-cultural conditions continues to be relevant and requires a non-surface, non-structural methodological and anthropological rethinking. The purpose of the article is to determine the role of the tandem of philosophical anthropology and philosophy of education in creating the ground for the formation of professional identity. Materials and Methods . The methodological principle of historicism is fundamental. The research methodology includes hermeneutical and systemic methods. The analysis and generalization of scientific research works on the philosophy of education and philosophical anthropology were also used. Results . A person from different metaphysical positions thinks about the issues of his own identity, his career aspirations and professional orientation. Modern society creates an environment for the development of an “informed person”, who is immersed in fundamentally different spheres of life. Philosophical anthropology in modern conditions acts as a practice of self-knowledge, the result of which should be contact with oneself, understanding one's goals and aspirations. The philosophy of education comprehends the trends of modern education, calls for a critical attitude to the newly created concepts of man, culture, and education. Professional identity gives an understanding and a sense of one's own "I" in professional development. Therefore, the task of the philosophy of education and philosophical anthropology is to harmonize the process of self-identification as much as possible. The tandem of the philosophy of education and philosophical anthropology reconciles the individual with the fundamental incompleteness of his being, looking for ways of harmonious development in the conditions of constant choice, constant search for his identity. Discussion and Conclusions . The task of both philosophers and educators is to create all the necessary conditions for the younger generation to cope with the challenge of the new era. Modern man needs not just to accumulate information about the world; he needs to know life itself: he wants to ask questions about its meaning, to see the boundaries of his own self, to talk about freedom and necessity, love and fidelity, understanding and loneliness. The relevance of philosophical anthropology lies in the fact that it acts as an arbiter between andragogy and pedagogy in a dialogue with the philosophy of education, since it asserts the main subject of a Person regardless of his social circumstances.
Mitigating Artifacts in Real-World Video Super-resolution Models

The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, hidden states tend to contain unpleasant artifacts and propagate them to restored frames. In this circumstance, our analyses show that such artifacts can be largely alleviated when the hidden state is replaced with a cleaner counterpart. Based on the observations, we propose a Hidden State Attention (HSA) module to mitigate artifacts in real-world video super-resolution. Specifically, we first adopt various cheap filters to produce a hidden state pool. For example, Gaussian blur filters are for smoothing artifacts while sharpening filters are for enhancing details. To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated. Equipped with HSA, our proposed method, namely FastRealVSR, is able to achieve 2x speedup while obtaining better performance than Real-BasicVSR. Codes will be available at https://github.com/TencentARC/FastRealVSR.
FSR: A General Frequency-Oriented Framework to Accelerate Image Super-resolution Networks

Deep neural networks (DNNs) have witnessed remarkable achievement in image super-resolution (SR), and plenty of DNN-based SR models with elaborated network designs have recently been proposed. However, existing methods usually require substantial computations by operating in spatial domain. To address this issue, we propose a general frequency-oriented framework (FSR) to accelerate SR networks by considering data characteristics in frequency domain. Our FSR mainly contains dual feature aggregation module (DFAM) to extract informative features in both spatial and transform domains, followed by a four-path SR-Module with different capacities to super-resolve in the frequency domain. Specifically, DFAM further consists of a transform attention block (TABlock) and a spatial context block (SCBlock) to extract global spectral information and local spatial information, respectively, while SR-Module is a parallel network container that contains four to-be-accelerated branches. Furthermore, we propose an adaptive weight strategy for a trade-off between image details recovery and visual quality. Extensive experiments show that our FSR can save FLOPs by almost 40% while reducing inference time by 50% for other SR methods (e.g., FSRCNN, CARN, SRResNet and RCAN). Code is available at https://github.com/THU-Kingmin/FSR.
FreeEnricher: Enriching Face Landmarks without Additional Cost

Recent years have witnessed significant growth of face alignment. Though dense facial landmark is highly demanded in various scenarios, e.g., cosmetic medicine and facial beautification, most works only consider sparse face alignment. To address this problem, we present a framework that can enrich landmark density by existing sparse landmark datasets, e.g., 300W with 68 points and WFLW with 98 points. Firstly, we observe that the local patches along each semantic contour are highly similar in appearance. Then, we propose a weakly-supervised idea of learning the refinement ability on original sparse landmarks and adapting this ability to enriched dense landmarks. Meanwhile, several operators are devised and organized together to implement the idea. Finally, the trained model is applied as a plug-and-play module to the existing face alignment networks. To evaluate our method, we manually label the dense landmarks on 300W testset. Our method yields state-of-the-art accuracy not only in newly-constructed dense 300W testset but also in the original sparse 300W and WFLW testsets without additional cost.
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variations and higher intra-class variations). To alleviate this problem, prior works predominately use a support set to reconstruct the query image and then utilize metric learning to determine its category. Upon careful inspection, we further reveal that such unidirectional reconstruction methods only help to increase inter-class variations and are not effective in tackling intra-class variations. In this paper, we for the first time introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. In addition to using the support set to reconstruct the query set for increasing inter-class variations, we further use the query set to reconstruct the support set for reducing intra-class variations. This design effectively helps the model to explore more subtle and discriminative features which is key for the fine-grained problem in hand. Furthermore, we also construct a self-reconstruction module to work alongside the bi-directional module to make the features even more discriminative. Experimental results on three widely used fine-grained image classification datasets consistently show considerable improvements compared with other methods. Codes are available at: https://github.com/PRIS-CV/Bi-FRN.
Reducing Domain Gap in Frequency and Spatial Domain for Cross-Modality Domain Adaptation on Medical Image Segmentation

Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing UDA methods depend on adversarial learning to address the domain gap between different image modalities, which is ineffective due to its complicated training process. In this paper, we propose a simple yet effective UDA method based on frequency and spatial domain transfer under multi-teacher distillation framework. In the frequency domain, we first introduce non-subsampled contourlet transform for identifying domain-invariant and domain-variant frequency components (DIFs and DVFs), and then keep the DIFs unchanged while replacing the DVFs of the source domain images with that of the target domain images to narrow the domain gap. In the spatial domain, we propose a batch momentum update-based histogram matching strategy to reduce the domain-variant image style bias. Experiments on two commonly used cross-modality medical image segmentation datasets show that our proposed method achieves superior performance compared to state-of-the-art methods.