Reviewerly/exHarmony huggingface dataset:
Implementation of Online Learning during the Covid-19 Pandemic on Madura Island, Indonesia

This study aimed to determine the online learning process and the associated obstacles experienced by students. With the background of the ongoing Covid-19 pandemic outbreak, this study sought to uncover what social constructions the students engage in related to the new policy for online learning that has recently been enforced by the Indonesian government. A quantitative study was conducted with a descriptive research approach. There were 274 college student respondents from Madura, Indonesia. This study found that not all of the students prefer online learning, inclusive of expressing their disapproval that online learning is effective. Social, economic and cultural factors are important indicators that online learning has not been able to be effectively carried out in a number of regions in Indonesia. Rural communities are not ready to welcome the latest learning methods and they are still comfortable using conventional methods. Online learning on Madura Island is not only constrained by technical problems and facilities but also by the human resources that need to be encouraged to accept the latest learning models. Students consider this learning model to not be beneficial due to the presence of several obstacles, including the geographical area. This is because rural areas make it difficult to access the internet. They also have to buy an internet quota. Internet access in some applications requires a large quota so this becomes problem for some students, especially for those who come from lower-middle income families. Therefore the availability of supporting facilities and infrastructure as well as the facilitated internet access among rural communities, financial assistance and the socialisation of the importance of online learning is necessary. https://doi.org/10.26803/ijlter.19.8.13
GLUCOSE: GeneraLized and COntextualized Story Explanations

When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why.As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal minitheories about the world, each grounded in a narrative context.To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions.Each GLUCOSE entry includes a storyspecific causal statement paired with an inference rule generalized from the statement.This paper details two concrete contributions.First, we present our platform for effectively crowdsourcing GLUCOSE data at scale, which uses semi-structured templates to elicit causal explanations.Using this platform, we collected a total of ˜670K specific statements and general rules that capture implicit commonsense knowledge about everyday situations.Second, we show that existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE's rich inferential content.However, when state-of-the-art neural models are trained on this knowledge, they can start to make commonsense inferences on unseen stories that match humans' mental models.
Simple quantum key distribution with qubit-based synchronization and a self-compensating polarization encoder

Quantum key distribution (QKD) relies on quantum communication to allow distant parties to share a secure cryptographic key. Widespread adoption of QKD in current telecommunication networks will require the development of simple, low-cost, and stable systems. However, current QKD implementations usually include additional hardware that perform auxiliary tasks such as temporal synchronization and polarization basis tracking. Here we present a polarization-based QKD system operating at 1550 nm that performs synchronization and polarization compensation by exploiting only the hardware already needed for the quantum communication task. Polarization encoding is performed by a self-compensating Sagnac loop modulator that exhibits high temporal stability and the lowest intrinsic quantum bit error rate reported so far. The QKD system was tested over a fiber-optic link, demonstrating tolerance up to about 40 dB of channel losses. Due to its reduced hardware requirements and the quality of the source, this work represents an important step towards technologically mature QKD systems.
Video Multimethod Assessment Fusion (VMAF) on 360VR Contents

This paper describes the subjective experiments and subsequent analysis carried out to validate the application of one of the most robust and influential video quality metrics, Video Multimethod Assessment Fusion (VMAF), to 360VR contents. VMAF is a full reference metric initially designed to work with traditional 2D contents. Hence, at first, it cannot be assumed to be compatible with the particularities of the scenario where omnidirectional content is visualized using a Head-Mounted Display (HMD). Therefore, through a complete set of tests, we prove that this metric can be successfully used without any specific training or adjustments to obtain the quality of 360VR sequences actually perceived by users.
Cyber Ranges and TestBeds for Education, Training, and Research

In recent years, there has been a growing demand for cybersecurity experts, and, according to predictions, this demand will continue to increase. Cyber Ranges can fill this gap by combining hands-on experience with educational courses, and conducting cybersecurity competitions. In this paper, we conduct a systematic survey of ten Cyber Ranges that were developed in the last decade, with a structured interview. The purpose of the interview is to find details about essential components, and especially the tools used to design, create, implement and operate a Cyber Range platform, and to present the findings.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning

Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance.In many application scenarios, however, such contexts are not available.In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query.We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.Furthermore, we can improve the model performance of both input views by Cooperative Learning, a training method that encourages the two input views to produce similar contextual representations or output label distributions.Experiments show that our approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains. 1
FedRec++: Lossless Federated Recommendation with Explicit Feedback

With the marriage of federated machine learning and recommender systems for privacy-aware preference modeling and personalization, there comes a new research branch called federated recommender systems aiming to build a recommendation model in a distributed way, i.e., each user is represented as a distributed client where his/her original rating data are not shared with the server or the other clients. Notice that, besides the sensitive information of a specific rating score assigned to a certain item by a user, the information of a user's rated set of items shall also be well protected. Some very recent works propose to randomly sample some unrated items for each user and then assign some virtual ratings, so that the server can not identify the scores and the set of rated items easily during the server-client interactions. However, the virtual ratings assigned to the randomly sampled items will inevitably introduce some noise to the model training process, which will then cause loss in recommendation performance. In this paper, we propose a novel lossless federated recommendation method (FedRec++) by allocating some denoising clients (i.e., users) to eliminate the noise in a privacy-aware manner. We further analyse our FedRec++ in terms of security and losslessness, and discuss its generality in the context of existing works. Extensive empirical studies clearly show the effectiveness of our FedRec++ in providing accurate and privacy-aware recommendation without much additional communication cost.
Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Abstract It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learning models for few-shot classification elaborately design various task-shared inductive bias (meta-knowledge) to solve such tasks, and achieve impressive performance. However, when there exists the domain shift between the training tasks and the test tasks, the obtained inductive bias fails to generalize across domains, which degrades the performance of the meta-learning models. In this work, we aim to improve the robustness of the inductive bias through task augmentation. Concretely, we consider the worst-case problem around the source task distribution, and propose the adversarial task augmentation method which can generate the inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple plug-and-play module for various meta-learning models, and improve their cross-domain generalization capability. We conduct extensive experiments under the cross-domain setting, using nine few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and ChestX. Experimental results show that our method can effectively improve the few-shot classification performance of the meta-learning models under domain shift, and outperforms the existing works. Our code is available at https://github.com/Haoqing-Wang/CDFSL-ATA.
A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer

Abstract Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive’s unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists’ annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into ‘low risk’ (benign, inflammation) and ‘high risk’ (dysplasia, malignancy) categories. We further trained the composite AI-model’s performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.
SOTR: Segmenting Objects with Transformers

Most recent transformer-based models show impressive performance on vision tasks, even better than Convolution Neural Networks (CNN). In this work, we present a novel, flexible, and effective transformer-based model for high-quality instance segmentation. The proposed method, Segmenting Objects with TRansformers (SOTR), simplifies the segmentation pipeline, building on an alternative CNN backbone appended with two parallel subtasks: (1) predicting per-instance category via transformer and (2) dynamically generating segmentation mask with the multi-level upsampling module. SOTR can effectively extract lower-level feature representations and capture long-range context dependencies by Feature Pyramid Network (FPN) and twin transformer, respectively. Meanwhile, compared with the original transformer, the proposed twin transformer is time- and resource-efficient since only a row and a column attention are involved to encode pixels. Moreover, SOTR is easy to be incorporated with various CNN backbones and transformer model variants to make considerable improvements for the segmentation accuracy and training convergence. Extensive experiments show that our SOTR performs well on the MS COCO dataset and surpasses state-of-the-art instance segmentation approaches. We hope our simple but strong framework could serve as a preferment baseline for instance-level recognition. Our code is available at https://github.com/easton-cau/SOTR.
Deep Graph Clustering via Dual Correlation Reduction

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition

Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of tasks recently. While achieving excellent performance, they still require relatively intensive computational cost that scales up drastically as the numbers of patches, self-attention heads and transformer blocks increase. In this paper, we argue that due to the large variations among images, their need for modeling long-range dependencies between patches differ. To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition. Optimized jointly with a transformer backbone in an end-to-end manner, a light-weight decision network is attached to the backbone to produce decisions on-the-fly. Extensive experiments on ImageNet demonstrate that our method obtains more than 2 × improvement on efficiency compared to state-of-the-art vision transformers with only 0.8% drop of accuracy, achieving good efficiency/accuracy trade-offs conditioned on different computational budgets. We further conduct quantitative and qualitative analysis on learned usage polices and provide more insights on the redundancy in vision transformers. Code is available at ht tps: / / gi thub. com/MengLcool/AdaVi T.
Dude, srsly?: The Surprisingly Formal Nature of Twitter's Language

Twitter has become the de facto information sharing and communication platform. Given the factors that influence language on Twitter - size limitation as well as communication and content-sharing mechanisms - there is a continuing debate about the position of Twitter's language in the spectrum of language on various established mediums. These include SMS and chat on the one hand (size limitations) and email (communication), blogs and newspapers (content sharing) on the other. To provide a way of determining this, we propose a computational framework that offers insights into the linguistic style of all these mediums. Our framework consists of two parts. The first part builds upon a set of linguistic features to quantify the language of a given medium. The second part introduces a flexible factorization framework, soclin, which conducts a psycholinguistic analysis of a given medium with the help of an external cognitive and affective knowledge base. Applying this analytical framework to various corpora from several major mediums, we gather statistics in order to compare the linguistics of Twitter with these other mediums via a quantitative comparative study. We present several key insights: (1) Twitter's language is surprisingly more conservative, and less informal than SMS and online chat; (2) Twitter users appear to be developing linguistically unique styles; (3) Twitter's usage of temporal references is similar to SMS and chat; and (4) Twitter has less variation of affect than other more formal mediums. The language of Twitter can thus be seen as a projection of a more formal register into a size-restricted space.
Secure and Efficient Two-Party Signing Protocol for the Identity-Based Signature Scheme in the IEEE P1363 Standard for Public Key Cryptography

Mobile device and application (app) security are increasingly important, partly due to the constant and fast-paced cyberthreat evolution. To ensure the security of communication (e.g., data-in-transit), a number of identity-based signature schemes have been designed to facilitate authorization identification and validation of messages. However, in many of these schemes, a user's private key may leak when a new signature is generated since the private keys are stored on the device. Seeking to improve the security of the private key, we propose the first two-party distributed signing protocol for the identity-based signature scheme in the IEEE P1363 standard. This protocol requires that two devices separately store one part of the user's private key, and allows these two devices to generate a valid signature without revealing the entire private key of the user. We formally prove that the security of the protocol in the random oracle model. Then, we implement the protocol using the MIRACL library and evaluate the protocol on two mobile devices. Compared with the protocol of Lindell (CRYPTO'17) that uses the zero-knowledge proof for its security, our protocol is more suitable for deployment in the mobile environment.
Mitigating algorithmic errors in a Hamiltonian simulation

Quantum computers can efficiently simulate many-body systems. As a widely used Hamiltonian simulation tool, the Trotter-Suzuki scheme splits the evolution into the number of Trotter steps $N$ and approximates the evolution of each step by a product of exponentials of each individual term of the total Hamiltonian. The algorithmic error due to the approximation can be reduced by increasing $N$, which however requires a longer circuit and hence inevitably introduces more physical errors. In this work, we first study such a trade-off and numerically find the optimal number of Trotter steps ${N}_{\mathrm{opt}}$ given a physical error model in a near-term quantum hardware. Practically, physical errors can be suppressed using recently proposed error mitigation methods. We then extend physical error mitigation methods to suppress the algorithmic error in Hamiltonian simulation. By exploiting the simulation results with different numbers of Trotter steps $N\ensuremath{\le}{N}_{\mathrm{opt}}$, we can infer the exact simulation result within a higher accuracy and hence mitigate algorithmic errors. We numerically test our scheme with a five-qubit system and show significant improvements in the simulation accuracy by applying both physical and algorithmic error mitigations.