Reviewerly/exHarmony huggingface dataset:
CRITERIA FOR SELECTING A CLOUD-BASED LEARNING MANAGEMENT SYSTEM FOR A HIGHER EDUCATION INSTITUTION

The paper explores the essence of the criteria and indicators which can be used to select a cloud-oriented learning support system for a higher education institution. The following criteria with corresponding indicators are identified: design criterion (reliability, accessibility, multilinguality, security, adaptability, ease of use and administration, free use); technological criterion (user access rights differentiation, cloud storage of data, integration with other cloud-based services, ability to download different types of files); communication criterion (user registration, communication between registered users, creating groups, creating forums and chats); information-didactic criterion (structuredness, calendar, assessment of student achievement, file sharing, testing and surveys, group and individual modes of work; analytics for a particular course). The most downloaded LMS are shown based on the results published by LMS Market Share. The paper offers an analysis of a number of cloud-based learning management systems (Google Classroom, Moodle, Edmodo, Studyboard, Oracle, Learner Nation, iSpring, Canvas, Schoology, Blackboard, NeoLms) in terms of the above-mentioned criteria and indicators. The systems were selected based on the method of expert evaluation. The expert evaluation showed that the most convenient and high-quality cloud-based learning management system for building a cloud-oriented learning environment of a higher education institution which best meets all the criteria are NeoLMS, Canvas and Google Classroom. These LMS offer all the functionalities which are essential in the educational process. We see the development of methodological recommendations for higher education regarding the high-quality and successful implementation of such learning management systems in the educational process as prospects for further research.
VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models

We introduce VAST, the Valence-Assessing Semantics Test, a novel intrinsic evaluation task for contextualized word embeddings (CWEs). Despite the widespread use of contextualizing language models (LMs), researchers have no intrinsic evaluation task for understanding the semantic quality of CWEs and their unique properties as related to contextualization, the change in the vector representation of a word based on surrounding words; tokenization, the breaking of uncommon words into subcomponents; and LM-specific geometry learned during training. VAST uses valence, the association of a word with pleasantness, to measure the correspondence of word-level LM semantics with widely used human judgments, and examines the effects of contextualization, tokenization, and LM-specific geometry. Because prior research has found that CWEs from OpenAI's 2019 English-language causal LM GPT-2 perform poorly on other intrinsic evaluations, we select GPT-2 as our primary subject, and include results showing that VAST is useful for 7 other LMs, and can be used in 7 languages. GPT-2 results show that the semantics of a word are more similar to the semantics of context in layers closer to model output, such that VAST scores diverge between our contextual settings, ranging from Pearson’s rho of .55 to .77 in layer 11. We also show that multiply tokenized words are not semantically encoded until layer 8, where they achieve Pearson’s rho of .46, indicating the presence of an encoding process for multiply tokenized words which differs from that of singly tokenized words, for which rho is highest in layer 0. We find that a few neurons with values having greater magnitude than the rest mask word-level semantics in GPT-2’s top layer, but that word-level semantics can be recovered by nullifying non-semantic principal components: Pearson’s rho in the top layer improves from .32 to .76. Downstream POS tagging and sentence classification experiments indicate that the GPT-2 uses these principal components for non-semantic purposes, such as to represent sentence-level syntax relevant to next-word prediction. After isolating semantics, we show the utility of VAST for understanding LM semantics via improvements over related work on four word similarity tasks, with a score of .50 on SimLex-999, better than the previous best of .45 for GPT-2. Finally, we show that 8 of 10 WEAT bias tests, which compare differences in word embedding associations between groups of words, exhibit more stereotype-congruent biases after isolating semantics, indicating that non-semantic structures in LMs also mask social biases.
MLink: Linking Black-Box Models for Collaborative Multi-Model Inference

The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices. A typical dilemma is: in order to provide complex intelligent services (e.g. smart city), we need inference results of multiple ML models, but the cost budget (e.g. GPU memory) is not enough to run all of them. In this work, we study underlying relationships among black-box ML models and propose a novel learning task: model linking. Model linking aims to bridge the knowledge of different black-box models by learning mappings (dubbed model links) between their output spaces. Based on model links, we developed a scheduling algorithm, named MLink. Through collaborative multi-model inference enabled by model links, MLink can improve the accuracy of obtained inference results under the cost budget. We evaluated MLink on a multi-modal dataset with seven different ML models and two real-world video analytics systems with six ML models and 3,264 hours of video. Experimental results show that our proposed model links can be effectively built among various black-box models. Under the budget of GPU memory, MLink can save 66.7% inference computations while preserving 94% inference accuracy, which outperforms multi-task learning, deep reinforcement learning-based scheduler and frame filtering baselines.
Self-Adaptive Imitation Learning: Learning Tasks with Delayed Rewards from Sub-optimal Demonstrations

Reinforcement learning (RL) has demonstrated its superiority in solving sequential decision-making problems. However, heavy dependence on immediate reward feedback impedes the wide application of RL. On the other hand, imitation learning (IL) tackles RL without relying on environmental supervision by leveraging external demonstrations. In practice, however, collecting sufficient expert demonstrations can be prohibitively expensive, yet the quality of demonstrations typically limits the performance of the learning policy. To address a practical scenario, in this work, we propose Self-Adaptive Imitation Learning (SAIL), which, provided with a few demonstrations from a sub-optimal teacher, can perform well in RL tasks with extremely delayed rewards, where the only reward feedback is trajectory-wise ranking. SAIL bridges the advantages of IL and RL by interactively exploiting the demonstrations to catch up with the teacher and exploring the environment to yield demonstrations that surpass the teacher. Extensive empirical results show that not only does SAIL significantly improve the sample efficiency, but it also leads to higher asymptotic performance across different continuous control tasks, compared with the state-of-the-art.
Barely-Supervised Learning: Semi-supervised Learning with Very Few Labeled Images

This paper tackles the problem of semi-supervised learning when the set of labeled samples is limited to a small number of images per class, typically less than 10, problem that we refer to as barely-supervised learning. We analyze in depth the behavior of a state-of-the-art semi-supervised method, FixMatch, which relies on a weakly-augmented version of an image to obtain supervision signal for a more strongly-augmented version. We show that it frequently fails in barely-supervised scenarios, due to a lack of training signal when no pseudo-label can be predicted with high confidence. We propose a method to leverage self-supervised methods that provides training signal in the absence of confident pseudo-labels. We then propose two methods to refine the pseudo-label selection process which lead to further improvements.The first one relies on a per-sample history of the model predictions, akin to a voting scheme. The second iteratively up-dates class-dependent confidence thresholds to better explore classes that are under-represented in the pseudo-labels. Our experiments show that our approach performs significantly better on STL-10 in the barely-supervised regime,e.g. with 4 or 8 labeled images per class.
CorED

Event detection (ED) is a pivotal task for information retrieval, which aims at identifying event triggers and classifying them into pre-defined event types. In real-world applications, events are usually annotated with numerous fine-grained types, which often arises long-tail type nature and co-occurrence event nature. Existing studies explore the event correlations without full utilization, which may limit the capability of event detection. This paper simultaneously incorporates both the type-level and instance-level event correlations, and proposes a novel framework, termed as CorED. Specifically, we devise an adaptive graph-based type encoder to capture instance-level correlations, learning type representations not only from their training data but also from their relevant types, thus leading to more informative type representations especially for the low-resource types. Besides, we devise an instance interactive decoder to capture instance-level correlations, which predicts event instance types conditioned on the contextual typed event instances, leveraging co-occurrence events as remarkable evidence in prediction. We conduct experiments on two public benchmarks, MAVEN and ACE-2005 dataset. Empirical results demonstrate the unity of both type-level and instance-level correlations, and the model achieves effectiveness performance on both benchmarks.
Exploring Modular Task Decomposition in Cross-domain Named Entity Recognition

Cross-domain Named Entity Recognition (NER) aims to transfer knowledge from the source domain to the target, alleviating expensive labeling costs in the target domain. Most prior studies acquire domain-invariant features under the end-to-end sequence-labeling framework where each token is assigned a compositional label (e.g., B-LOC). However, the complexity of cross-domain transfer may be increased over this complicated labeling scheme, which leads to sub-optimal results, especially when there are significantly distinct entity categories across domains. In this paper, we aim to explore the task decomposition in cross-domain NER. Concretely, we suggest a modular learning approach in which two sub-tasks (entity span detection and type classification) are learned by separate functional modules to perform respective cross-domain transfer with corresponding strategies. Compared with the compositional labeling scheme, the label spaces are smaller and closer across domains especially in entity span detection, leading to easier transfer in each sub-task. And then we combine two sub-tasks to achieve the final result with modular interaction mechanism, and deploy the adversarial regularization for generalized and robust learning in low-resource target domains. Extensive experiments over 10 diverse domain pairs demonstrate that the proposed method is superior to state-of-the-art cross-domain NER methods in an end-to-end fashion (about average 6.4% absolute F1 score increase). Further analyses show the effectiveness of modular task decomposition and its great potential in cross-domain NER.
ReMix: A General and Efficient Framework for Multiple Instance Learning Based Whole Slide Image Classification

Whole slide image (WSI) classification often relies on deep weakly supervised multiple instance learning (MIL) methods to handle gigapixel resolution images and slide-level labels. Yet the decent performance of deep learning comes from harnessing massive datasets and diverse samples, urging the need for efficient training pipelines for scaling to large datasets and data augmentation techniques for diversifying samples. However, current MIL-based WSI classification pipelines are memory-expensive and computation-inefficient since they usually assemble tens of thousands of patches as bags for computation. On the other hand, despite their popularity in other tasks, data augmentations are unexplored for WSI MIL frameworks. To address them, we propose ReMix, a general and efficient framework for MIL based WSI classification. It comprises two steps: reduce and mix. First, it reduces the number of instances in WSI bags by substituting instances with instance prototypes, i.e., patch cluster centroids. Then, we propose a “Mix-the-bag” augmentation that contains four online, stochastic and flexible latent space augmentations. It brings diverse and reliable class-identity-preserving semantic changes in the latent space while enforcing semantic-perturbation invariance. We evaluate ReMix on two public datasets with two state-of-the-art MIL methods. In our experiments, consistent improvements in precision, accuracy, and recall have been achieved but with orders of magnitude reduced training time and memory consumption, demonstrating ReMix’s effectiveness and efficiency. Code is available at https://github.com/TencentAILabHealthcare/ReMix .
Relation-Guided Few-Shot Relational Triple Extraction

In few-shot relational triple extraction (FS-RTE), one seeks to extract relational triples from plain texts by utilizing only few annotated samples. Recent work first extracts all entities and then classifies their relations. Such an entity-then-relation paradigm ignores the entity discrepancy between relations. To address it, we propose a novel task decomposition strategy, Relation-then-Entity, for FS-RTE. It first detects relations occurred in a sentence and then extracts the corresponding head/tail entities of the detected relations. To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relation-relevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities. Experimental results show that our model outperforms previous work by an absolute gain (18.98%, 28.85% in F1 in two few-shot settings).
A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness

Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.
B-spline surface based 3D reconstruction method for deflectometry

In the field of optical three-dimension (3-D) measurement, reconstruction usually is completed by the integration of a two-dimensional (2-D) gradient data set. Position and posture of camera and shape of the surface under test determine the location of gradient data which usually is on quadrilateral grids. This paper proposes a B-spline surface-based 3D reconstruction method for deflectometry, which reconstructs the surface under test with its 2-D gradient data set. The 2-D gradient data set consists of gradient data and the 2-D location of the gradient data in the camera coordinate system. The 2-D gradient data set is first transferred to the cameras' virtual image plane, so it locates on rectangular grids. Then, based on the properties of the B-spline basis function and characteristics of the camera, linear equations are derived to solve control points along the virtual image plane. The solved control points reconstruct the surface under test in the camera coordinate system. The property of the B-spline basis function determines the relationship between the depth of the surface and its derivative. The characteristic of the camera determines the relationship between the depth of the surface and the 2-D location of the gradient data. Meanwhile, the accuracy of the 2-D location can also be improved by the linear equations. Finally, simulated and actual experiments show that the proposed method is accurate and efficient at reconstructing surfaces in deflectometry.
Extreme Multistability in a Hopfield Neural Network Based on Two Biological Neuronal Systems

Based on different biological neuronal systems, various memristive neuron and neuron network models are generated. In this brief, a three-neurons-based Hopfield neural network with two biological neural mechanisms is investigated, with one of three neurons exposed to electromagnetic radiation and one of the synaptic weights replaced by a memristor. The effects of electromagnetic radiation and memristive synaptic weight on neural networks are investigated. It is found that coexisting behaviors exist in the neural network by adjusting the memristive synaptic weight. Moreover, infinitely many coexisting behaviors are observed by changing the initial electromagnetic radiation. That is, both of them are critical to the system and have the potential to complicate it. Furthermore, circuits of the neural network are implemented in Pspice and DSP board, and the experimental results are in agreement with numerical ones.
Distributed Fog Computing and Federated-Learning-Enabled Secure Aggregation for IoT Devices

Federated learning (FL), as a prospective way to process and analyze the massive data from the Internet of Things (IoT) devices, has attracted increasing attention from academia and industry. However, considering the unreliable nature of IoT devices, ensuring the efficiency of FL while protecting the privacy of devices’ input data is a challenging task. To address these issues, we propose a secure aggregation protocol based on efficient additive secret sharing in the fog-computing (FC) setting. As the secure aggregation is performed frequently in the training process of FL, the protocol should have low communication and computation overhead. First, we use a fog node (FN) as an intermediate processing unit to provide local services which can assist the cloud server aggregated the sum during the training process. Second, we design a light Request-then-Broadcast method to ensure our protocol has the robustness to dropped-out clients. Our protocol also provides two simple new client selection methods. The security and performance of our protocol are analyzed and compared with existed schemes. We conduct experiments on high-dimensional inputs, and our experimental results demonstrate about 24– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$168\times $ </tex-math></inline-formula> improvement in computation overhead and 87– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$287\times $ </tex-math></inline-formula> improvement in communication overhead compared to Google’s secure aggregation protocol (Bonwatiwz et al. CCS17).
Using Neural Machine Translation Methods for Sign Language Translation

We examine methods and techniques, proven to be helpful for the text-to-text translation of spoken languages in the context of gloss-to-text translation systems, where the glosses are the written representation of the signs.We present one of the first works that include experiments on both parallel corpora of the German Sign Language (PHOENIX14T and the Public DGS Corpus).We experiment with two NMT architectures with optimization of their hyperparameters, several tokenization methods and two data augmentation techniques (back-translation and paraphrasing).Through our investigation we achieve a substantial improvement of 5.0 and 2.2 BLEU scores for the models trained on the two corpora respectively.Our RNN models outperform our Transformer models, and the segmentation method we achieve best results with is BPE, whereas back-translation and paraphrasing lead to minor but not significant improvements.
Deep Learning Approach for Masked Face Identification

Covid-19 is a global health emergency and a major concern in the industrial and residential sectors.It has the ability to spread leading to health problems or death.Wearing a mask in public locations and busy areas is the most effective COVID-19 prevention measure.Face recognition provides an accurate method that overcomes uncertainties such as false prediction, high cost, and time consumption, as it is understood that the primary identification for every human being is his face.As a result, masked face identification is required to solve the issue of recognizing individuals with masks in several applications such as door access systems and smart attendance systems.This paper offers an important and intelligent method to solve this issue.We propose deep transfer learning approach for masked face human identification.We created a dataset of masked-face images and examined six convolutional neural network (CNN) models on this dataset.All models show great performance in terms of very high face recognition accuracy and short training time.
Security analysis of hybrid one time password generation algorithm for IoT data

The growing reality today is the application of Internet of Things (IoT) in day-to-day. Though there are lot of complexities around this environment such as security, energy consumption and heterogeneity, this giant network of connecting devices with internet is sky rocketing. The mounting increase in the sharing of information made this connected network of devices is mandatory in everyday life. This powerful IoT platform suffers a lot by the security breaches as more people uses it. Authentication of IoT users using One Time Password (OTP) is one kind of resolution to solve many security issues. The OTP has added an additional coating to the traditional username-password authentication system. So, this research proposes a hybrid One-time Password generation algorithm, AroSheb_Jo, for IoT data and presents a security analysis of that algorithm. Additionally, this paper presents a comparison of its lightweight characteristics and resistance against security attacks. Experimental and performance analysis of that algorithm is also elaborated in this paper.