Reviewerly/exHarmony huggingface dataset:
Few-Shot Object Detection in Unseen Domains

Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data by transferring knowledge gained on abundant base classes. FSOD approaches commonly assume that both the scarcely provided examples of novel classes and test-time data belong to the same domain. However, this assumption does not hold in various industrial and robotics applications, where a model can learn novel classes from a source domain while inferring on classes from a target domain. In this work, we address the task of zero-shot domain adaptation, also known as domain generalization, for FSOD. Specifically, we assume that neither images nor labels of the novel classes in the target domain are available during training. Our approach for solving the domain gap is two-fold. First, we leverage a meta-training paradigm, where we learn the domain shift on the base classes, then transfer the domain knowledge to the novel classes. Second, we propose various data augmentations techniques on the few shots of novel classes to account for all possible domain-specific information. To constraint the network into encoding domain-agnostic class-specific representations only, a contrastive loss is proposed to maximize the mutual information between foreground proposals and class embeddings and reduce the network's bias to the background information from target domain. Our experiments on the T-LESS, PASCAL-VOC, and ExDark datasets show that the proposed approach succeeds in alleviating the domain gap considerably without utilizing labels or images of novel categories from the target domain.
Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations

Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information. In this context, tensor decomposition has successfully modeled interactions between entities and relations. Their effectiveness in static knowledge graph completion motivates us to introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER. Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features, which is model-agnostic and offers a more generalized representation of time. We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing. The experiments show that our proposed methods perform on par or better than the state-of-the-art semantic matching models on two benchmarks.
ASR in German: A Detailed Error Analysis

The amount of freely available systems for automatic speech recognition (ASR) based on neural networks is growing steadily, with equally increasingly reliable predictions. However, the evaluation of trained models is typically exclusively based on statistical metrics such as WER or CER, which do not provide any insight into the nature or impact of the errors produced when predicting transcripts from speech input. This work presents a selection of ASR model architectures that are pretrained on the German language and evaluates them on a benchmark of diverse test datasets. It identifies cross-architectural prediction errors, classifies those into categories and traces the sources of errors per category back into training data as well as other sources. Finally, it discusses solutions in order to create qualitatively better training datasets and more robust ASR systems.
Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding

This paper proposes a simple and effective approach for automatic recognition of Cued Speech (CS), a visual communication tool that helps people with hearing impairment to understand spoken language with the help of hand gestures that can uniquely identify the uttered phonemes in complement to lipreading. The proposed approach is based on a pre-trained hand and lips tracker used for visual feature extraction and a phonetic decoder based on a multistream recurrent neural network trained with connectionist temporal classification loss and combined with a pronunciation lexicon. The proposed system is evaluated on an updated version of the French CS dataset CSF18 for which the phonetic transcription has been manually checked and corrected. With a decoding accuracy at the phonetic level of 70.88%, the proposed system outperforms our previous CNN-HMM decoder and competes with more complex baselines.
About Opposition and Duality in Paraconsistent Type Theory

A paraconsistent type theory (an extension of a fragment of intuitionistic type theory by adding opposite types) is here extended by adding co-function types. It is shown that, in the extended paraconsistent type system, the opposite type constructor can be viewed as an involution operation that transforms each type into its dual type. Moreover, intuitive interpretations of opposite and co-function types under different interpretations of types are discussed.
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture

This paper introduces a novel non-parametric deep model for estimating time-to-event (survival analysis) in presence of censored data and competing risks. The model is designed based on the sequence-to-sequence (Seq2Seq) architecture, therefore we name it Survival Seq2Seq. The first recurrent neural network (RNN) layer of the encoder of our model is made up of Gated Recurrent Unit with Decay (GRU-D) cells. These cells have the ability to effectively impute not-missing-at-random values of longitudinal datasets with very high missing rates, such as electronic health records (EHRs). The decoder of Survival Seq2Seq generates a probability distribution function (PDF) for each competing risk without assuming any prior distribution for the risks. Taking advantage of RNN cells, the decoder is able to generate smooth and virtually spike-free PDFs. This is beyond the capability of existing non-parametric deep models for survival analysis. Training results on synthetic and medical datasets prove that Survival Seq2Seq surpasses other existing deep survival models in terms of the accuracy of predictions and the quality of generated PDFs.
Marvelous Agglutinative Language Effect on Cross Lingual Transfer Learning

As for multilingual language models, it is important to select languages for training because of the curse of multilinguality. (Conneau et al., 2020). It is known that using languages with similar language structures is effective for cross lingual transfer learning (Pires et al., 2019). However, we demonstrate that using agglutinative languages such as Korean is more effective in cross lingual transfer learning. This is a great discovery that will change the training strategy of cross lingual transfer learning.
School and University Closures in COVID-19: Impacts on Educational Processes and Systems

Schools and universities have been closed for over 6 months, almost, all over the world. Millions of school children and university students are not able to attend their educational institutions and forced to stay home either in isolation or in quarantine. Many of the educational institutions, particularly in developed countries, have been running their classes online; however, a large number of students in South Asia, including Nepal, are not able to attend either face to face classes because of lockdown or virtual classes because of hardware and software technologies inaccessibility. Social and physical distancing and lockdown have been considered effective intermediate measures until humanity will have developed vaccines to control/fight against the novel coronavirus. These measures have been able to control the disease spread to some extent; however, they have also deprived millions of students from their fundamental rights of acquiring education. To continue classes during COVID-19, there is a buzzword—online classes—in the market now, and many of the schools and universities have started to offer courses through online platforms. However, no study has been conducted thus far to understand the challenges and prospects of pandemic such as COVID-19 and the shift of teaching-learning from face-to-face to online modes. In this chapter, we document existing online teaching and learning 444practices in Nepal and beyond, identify the research and practice-informed best practices, expecting them they can play catalyst roles during COVID-19 and similar pandemic in the future.
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement

As an emerging technology, federated learning (FL) involves training machine learning models over distributed edge devices, which attracts sustained attention and has been extensively studied. However, the heterogeneity of client data severely degrades the performance of FL compared with that in centralized training. It causes the locally trained models of clients to move in different directions. On the one hand, it slows down or even stalls the global updates, leading to inefficient communication. On the other hand, it enlarges the distances between local models, resulting in an aggregated global model with poor performance. Fortunately, these shortcomings can be mitigated by reducing the angle between the directions that local models move in. Based on this fact, we propose FedCos, which reduces the directional inconsistency of local models by introducing a cosine-similarity penalty. It promotes the local model iterations towards an auxiliary global direction. Moreover, our approach is auto-adapt to various non-IID settings without an elaborate selection of hyperparameters. The experimental results show that FedCos outperforms the well-known baselines and can enhance them under a variety of FL scenes, including varying degrees of data heterogeneity, different number of participants, and cross-silo and cross-device settings. Besides, FedCos improves communication efficiency by 2 to 5 times. With the help of FedCos, multiple FL methods require significantly fewer communication rounds than before to obtain a model with comparable performance.
Small cycle structure for words in conjugation invariant random permutations

We study the cycle structure of words in several random permutations. We assume that the permutations are independent and that their distribution is conjugation invariant, with a good control on their short cycles. If, after successive cyclic simplifications, the word w still contains at least two different letters, then we get a universal limiting joint law for small cycles for the word in these permutations. These results can be seen as an extension of our previous work [Kammoun and Ma\"ida, 2020] from the product of permutations to any non-trivial word in the permutations and also as an extension of the results of [Nica, 1994] from uniform permutations to general conjugation invariant random permutations.
Design and application of distributed photovoltaic transaction consensus mechanism based on blockchain

Abstract In recent years, distributed photovoltaic power generation has developed rapidly, but it has also brought many problems, such as poor equipment reliability, complex operation control, and many management subjects. Under the situation of the development of the electricity market, it is necessary to solve the problem of ensuring the data security of participating in the electricity market. Blockchain technology has the characteristics of traceability, non-tampering of information, openness and transparency, etc., which can satisfy massive transactions and reduce the burden of traditional power transactions. Therefore, based on the typical characteristics of blockchain, this paper analyzes the common attributes of blockchain and electricity market transactions, and adopts the DPOS (delegated proof of stake) consensus mechanism to realize distributed photovoltaic transactions. On this basis, this paper utilizes the consensus method based on node credit value and applies it to the actual microgrid distributed photovoltaic power station. The experimental results show that the transaction consensus mechanism proposed in this paper has certain advantages in transaction efficiency and transaction cost, which can provide a practical basis for blockchain to realize distributed photovoltaic transactions.
Axion-induced oscillations in Josephson qubits

An effective quantum description of the axion is proposed as a two-level dynamic system together with a spin-spin Hamiltonian for a coupled axion-Josephson qubit system. The interaction between axion and Josephson Junction can be responsible for a resonance effect, when the axion and the Josephson frequencies match. Thus, experimentally detectable oscillations induced in the Josephson qubit by its interaction with the axion are clearly highlighted. This phenomenon, causing a periodic magnetization reversal in the junction, can be exploited for the axion detection in the quantum limit of low noise.
LiDAR-Based 3D Object Detection using Self-ensembling Feature Preservation PointPillars in Urban Environments

Object detection, which is essential for configuring autonomous driving, generally includes 2D, bird’s eye view (BEV), and 3D detection. Among these detection methods, 3D object detection is becoming more active to be compatible with real environments. However, most of the currently released deep learning networks for 3D object detection have been developed using the KITTI dataset, which is labeled only for objects in the front camera angle. To solve this problem, this paper presents a method of 3D object detection using a point cloud dataset collected by a 128-channel LiDAR sensor installed on a vehicle for driving data collection and labeled on objects of 360 degrees. The proposed method based on PointPillars, which currently boasts the highest speed, is designed to be trained on groups of related features and uses self-ensembling in SE-SSD, which shows the best accuracy on the KITTI dataset. This deep learning model demonstrates better accuracy for R40 evaluation indicators than other deep learning networks, and the practicality of the model is validated by verifying the performance of an actual autonomous vehicle.
Integration of knowledge and data in machine learning

Scientific research's mandate is to comprehend and explore the world, as well as to improve it based on experience and knowledge. Knowledge embedding and knowledge discovery are two significant methods of integrating knowledge and data. Through knowledge embedding, the barriers between knowledge and data can be eliminated, and machine learning models with physical common sense can be established. Meanwhile, humans' understanding of the world is always limited, and knowledge discovery takes advantage of machine learning to extract new knowledge from observations. Knowledge discovery can not only assist researchers to better grasp the nature of physics, but it can also support them in conducting knowledge embedding research. A closed loop of knowledge generation and usage are formed by combining knowledge embedding with knowledge discovery, which can improve the robustness and accuracy of models and uncover previously unknown scientific principles. This study summarizes and analyzes extant literature, as well as identifies research gaps and future opportunities.
Extending the Scope of Out-of-Domain: Examining QA models in multiple subdomains

Past works that investigate out-of-domain performance of QA systems have mainly focused on general domains (e.g. news domain, wikipedia domain), underestimating the importance of subdomains defined by the internal characteristics of QA datasets. In this paper, we extend the scope of "out-of-domain" by splitting QA examples into different subdomains according to their several internal characteristics including question type, text length, answer position. We then examine the performance of QA systems trained on the data from different subdomains. Experimental results show that the performance of QA systems can be significantly reduced when the train data and test data come from different subdomains. These results question the generalizability of current QA systems in multiple subdomains, suggesting the need to combat the bias introduced by the internal characteristics of QA datasets.
Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data

Transfer learning for GANs successfully improves generation performance under low-shot regimes. However, existing studies show that the pretrained model using a single benchmark dataset is not generalized to various target datasets. More importantly, the pretrained model can be vulnerable to copyright or privacy risks as membership inference attack advances. To resolve both issues, we propose an effective and unbiased data synthesizer, namely Primitives-PS, inspired by the generic characteristics of natural images. Specifically, we utilize 1) the generic statistics on the frequency magnitude spectrum, 2) the elementary shape (i.e., image composition via elementary shapes) for representing the structure information, and 3) the existence of saliency as prior. Since our synthesizer only considers the generic properties of natural images, the single model pretrained on our dataset can be consistently transferred to various target datasets, and even outperforms the previous methods pretrained with the natural images in terms of Fr'echet inception distance. Extensive analysis, ablation study, and evaluations demonstrate that each component of our data synthesizer is effective, and provide insights on the desirable nature of the pretrained model for the transferability of GANs.
Linear Complexity Randomized Self-attention Mechanism

Recently, random feature attentions (RFAs) are proposed to approximate the softmax attention in linear time and space complexity by linearizing the exponential kernel. In this paper, we first propose a novel perspective to understand the bias in such approximation by recasting RFAs as self-normalized importance samplers. This perspective further sheds light on an \emph{unbiased} estimator for the whole softmax attention, called randomized attention (RA). RA constructs positive random features via query-specific distributions and enjoys greatly improved approximation fidelity, albeit exhibiting quadratic complexity. By combining the expressiveness in RA and the efficiency in RFA, we develop a novel linear complexity self-attention mechanism called linear randomized attention (LARA). Extensive experiments across various domains demonstrate that RA and LARA significantly improve the performance of RFAs by a substantial margin.
Review: Trubnikova, Victoriya, &amp; Garofolin, Benedetta. (2020). Lingua e interazione. Insegnare la pragmatica a scuola [Language and interaction. Teaching pragmatics at school]. Edizioni ETS.

This volume presents a theoretical and practical approach to the teaching of pragmatics in primary and secondary educational contexts. Targeting first language (L1), second language (L2), and foreign language (FL) teachers, the volume starts by introducing key concepts in pragmatics, providing an overview of theoretical notions in communicative competence, and drawing pedagogical implications from these notions. The book then reports findings from a survey conducted among language teachers on their views on and experience with the teaching of linguistic pragmatics. Finally, a 5-step inductive-explicit pedagogical model is put forward for raising metapragmatic awareness and developing receptive and productive pragmatic skills among learners of varied age groups. A rich set of sample activities illustrates how to put the model into practice and to adapt it to learners’ specific needs.
User Interface (UI) Design as A Visual Comunication Medium for Composting Waste Management Education

As the fourth leading country with the largest population, the demand for needs in Indonesia has increased significantly, especially in primary needs such as food and household materials. With the massive amount of waste and lack of awareness, the waste we produce is thrown away to the landfill, without knowing the effects it might cause. In this situation, there are many people throwing garbage in the wrong place. The awareness amongst youth in Indonesia in regard to excessive waste and composting as a solution to act on it is, still low. In the effort of collecting data, literature studies from different sources are used, this research also conducts in-depth interviews with experts in the field. A creative solution of creating a digital platform in the form of a website that called Feed the Soil is made to help raise the awareness to solve the problem. With an attractive visual and interactive activity, It would be easier for the targeted audience to be able to increase their interest and boost their knowledge. As a result, the awareness is raised, and information can be easily understood and received in a fun and unordinary way. Moreover, further research is still needed to provide more depth to the platform and implementation.
BankNote-Net: Open dataset for assistive universal currency recognition

Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recognition, scene identification, person recognition, and currency recognition. This last task, the recognition of banknotes from different denominations, has been addressed by the use of computer vision models for image recognition. However, the datasets and models available for this task are limited, both in terms of dataset size and in variety of currencies covered. In this work, we collect a total of 24,826 images of banknotes in variety of assistive settings, spanning 17 currencies and 112 denominations. Using supervised contrastive learning, we develop a machine learning model for universal currency recognition. This model learns compliant embeddings of banknote images in a variety of contexts, which can be shared publicly (as a compressed vector representation), and can be used to train and test specialized downstream models for any currency, including those not covered by our dataset or for which only a few real images per denomination are available (few-shot learning). We deploy a variation of this model for public use in the last version of the Seeing AI app developed by Microsoft. We share our encoder model and the embeddings as an open dataset in our BankNote-Net repository.
Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts

Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discriminative representations with auto-annotation pretext(s), then 2) transferring the representations to assist downstream task(s). Such two stages are usually implemented separately, making the learned representation learned agnostic to the downstream tasks. Currently, most works are devoted to exploring the first stage. Whereas, it is less studied on how to learn downstream tasks with limited labeled data using the already learned representations. Especially, it is crucial and challenging to selectively utilize the complementary representations from diverse pretexts for a downstream task. In this paper, we technically propose a novel solution by leveraging the attention mechanism to adaptively squeeze suitable representations for the tasks. Meanwhile, resorting to information theory, we theoretically prove that gathering representation from diverse pretexts is more effective than a single one. Extensive experiments validate that our scheme significantly exceeds current popular pretext-matching based methods in gathering knowledge and relieving negative transfer in downstream tasks.