Reviewerly/exHarmony huggingface dataset:
Enhancing Vietnamese Question Generation with Reinforcement Learning

Along with automatic question answering and machine reading comprehension, question generation has become a popular yet challenging task of natural language understanding in recent years. However, as far as we are concerned, there was no study being conducted with a concentration on method for question generation in Vietnamese known as a low-resource language. In this paper, we evaluate different powerful question generation systems in two benchmark Vietnamese datasets: UIT-ViNewsQA and UIT-ViQuAD. First, we conduct experiments on deep neural network and sequence-to-sequence approaches, based on a context and an answer to generate a question. In addition, in order to investigate several powerful approaches, we utilize two strong language models (LM): the monolingual language model PhoBERT and a massively multilingual pre-trained language model mT5. To obtain higher performance, we enhance LM-based methods with reinforcement learning during the decoding process. Our experiments show that the best model achieves the BLEU 4 scores of 19.77 on UIT-ViNewsQA and 20.43 on UIT-ViQuAD.
Variational-Based Nonlinear Bayesian Filtering With Biased Observations

State estimation of dynamical systems is crucial for providing new decision-making and system automation information in different applications. However, the assumptions on the standard computational models for sensor measurements can be violated in practice due to different types of data abnormalities such as outliers and biases. In this work, we focus on the occurrence of measurement biases and propose a robust filter for their detection and mitigation during state estimation of nonlinear dynamical systems. We model the presence of bias in each dimension within the generative structure of the state-space models. Subsequently, employing the theory of Variational Bayes and general Gaussian filtering, we devise a recursive filter which we call the Bias Detecting and Mitigating (BDM) filter. As the error detection mechanism is embedded within the filter structure its dependence on any external detector is obviated. Simulations verify the performance gains of the proposed BDM filter compared to similar Kalman filtering-based approaches in terms of robustness to temporary and persistent bias presence.
Cryptocurrencies and G7 capital markets integrate in periods of extreme volatility?

The purpose of this study is to examine the synchronism between the US capital markets (DJ, S&amp;P 500), the United Kingdom (FTSE 100), Canada (S&amp;P/TSX), Germany (DAX 30), France (CAC 40), Japan (Nikkei 225), Italy (Italy Ds Market and major cryptocurrencies such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and the Crypto 10 index, from February 2018 to November 2021. Based on the findings, we found that BTC and ETH cryptocurrencies drastically reduced their level of integration with their peers over the 2020 worldwide pandemic era, whereas LTC maintained. We also discovered that the Dow Jones, S&amp;P 500, and DAX 30 stock indexes lowered their level of integration when compared to the pre-covid subperiod. For the UK capital market (FTSE 100), Canada (S&amp;P/TSX), Japan (Nikkei 225), France (CAC 40), and Italy (Italy Ds Market) the level of integration increased significantly. These findings support, in part, our research question, that during periods of stress and uncertainty in the global economy capital markets tend towards integration, thus calling into question the hypothesis of efficient portfolio diversification.
Depth Field Networks For Generalizable Multi-view Scene Representation

Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matching. These architectures are specialized according to the particular problem, and thus require significant task-specific tuning, often leading to poor domain generalization performance.Recently, generalist Transformer architectures have achieved impressive results in tasks such as optical flow and depth estimation by encoding geometric priors as inputs rather than as enforced constraints.In this paper, we extend this idea and propose to learn an implicit, multi-view consistent scene representation, introducing a series of 3D data augmentation techniques as a geometric inductive prior to increase view diversity. We also show that introducing view synthesis as an auxiliary task further improves depth estimation. Our Depth Field Networks (DeFiNe) achieve state-of-the-art results in stereo and video depth estimation without explicit geometric constraints, and improve on zero-shot domain generalization by a wide margin. Project page: https://sites.google.com/view/tri-define .
A Multi-modal Knowledge Graph Platform Based on Medical Data Lake

In recent years, the application of Knowledge Graphs (KGs) in the medical domain has developed rapidly. Embedding medical knowledge graphs into low-dimensional vectors to implement certain applications is the focus of recent research. However, current approaches focus on using structured data while ignoring external information from prior knowledge and multi-modal data, such as visual and textual data. In this paper, we propose a multi-modal knowledge graph platform based on medical data lake. It enhances Knowledge Graph Embedding (KGE) from structural data by using prior knowledge like doctors' advice. After the generation of embedding of knowledge graphs, we use a fully-connected layer to combine the structural vectors and multi-modal embedding from unstructured data for link prediction. Moreover, we provide recommendations to help doctors diagnose and research based on the knowledge graph embedding model. Our platform has been used in a clinical decision support system, which is capable of discovering unveiled relations between entities and giving suitable advice for doctors.
Enhancing Multi-modal Features Using Local Self-attention for 3D Object Detection

LiDAR and Camera sensors have complementary properties: LiDAR senses accurate positioning, while camera provides rich texture and color information. Fusing these two modalities can intuitively improve the performance of 3D detection. Most multi-modal fusion methods use networks to extract features of LiDAR and camera modality respectively, then simply add or concancate them together. We argue that these two kinds of signals are completely different, so it is not proper to combine these two heterogeneous features directly. In this paper, we propose EMMF-Det to do multi-modal fusion leveraging range and camera images. EMMF-Det uses self-attention mechanism to do feature re-weighting on these two modalities interactively, which can enchance the features with color, texture and localiztion information provided by LiDAR and camera signals. On the Waymo Open Dataset, EMMF-Det acheives the state-of-the-art performance. Besides this, evaluation on self-built dataset further proves the effectiveness of our method.
Egocentric Prediction of Action Target in 3D

We are interested in anticipating as early as possible the target location of a person's object manipulation action in a 3D workspace from egocentric vision. It is important in fields like human-robot collaboration, but has not yet received enough attention from vision and learning communities. To stimulate more research on this challenging egocentric vision task, we propose a large multimodality dataset of more than 1 million frames of RGB-D and IMU streams, and provide evaluation metrics based on our high-quality 2D and 3D labels from semi-automatic annotation. Meanwhile, we design baseline methods using recurrent neural networks and conduct various ablation studies to validate their effectiveness. Our results demonstrate that this new task is worthy of further study by researchers in robotics, vision, and learning communities.
An Efficient and Robust Multidimensional Data Aggregation Scheme for Smart Grid Based on Blockchain

In order to analyze real-time power data without revealing users' privacy, privacy-preserving data aggregation schemes have been extensively researched in smart grid.However, most of the existing schemes either can only allow stationary users, or require a trusted center.In this paper, we propose an efficient and robust multidimensional data aggregation scheme based on blockchain.In our scheme, a leader election algorithm in Raft protocol is used to select a mining node from all smart meters to aggregate data.A dynamically verifiable secret sharing homomorphism scheme is adopted to realize flexible dynamic user management.In addition, our scheme can not only resist internal and external attacks but also support multidimensional data aggregation and fault tolerance.The security analysis shows that our proposed scheme is IND-CPA secure and can meet stronger security features.The experimental results show that compared with other schemes, our scheme can be implemented with lower computation and communication overhead.
Sediments, Serpentinites, and Subduction: Halogen Recycling from the Surface to the Deep Earth

Halogens are important elements that participate in a variety of biogeo-chemical processes and influence the solubility of metals in subduction-zone fluids. Halogens are powerful tracers of subducted volatiles in the Earth’s mantle because they have high abundances in seawater, sediments, and altered oceanic lithosphere but low concentrations in the mantle. Additionally, Br/Cl and I/Cl ratios, as well as Cl-isotope ratios, have characteristic ranges in different surface reservoirs that are not easily fractionated in the mantle. Current data suggest that subduction of serpentinised lithosphere is a major source of halogens in the Earth’s mantle.
A Web-Based Tool for Collaborative Modelling and Analysis in Human-Computer Interaction and Cognitive Science

Human-computer interaction and cognitive science are interdisciplinary areas in which computer scientists and mathematicians often work together with social scientists, such as psychologists and sociologists, as well as with more focussed practitioners such as usability experts and system analysts. In order to work effectively, interdisciplinary teams need to agree on a common communication language as a compromise between the computer scientists and mathematicians’ formal modelling approach and the conceptual models normally used by social scientists for describing their domain-related theories and frameworks. Moreover, even when proper communication is established within a specific research team, the next challenge is the presentation of the result to a heterogeneous international community, to allow for cross-fertilisation, exchanges of ideas, sharing of models, results and data, work replication and review. This tool paper presents ColMASC, a web-based tool and portal for the collaborative modelling and analysis of human cognition and behaviour as well as interactive systems. ColMASC aim is for researchers in human-computer interaction and cognitive science to freely use the tool provided by the web portal in order to collaborate in the development of models of computer/physical systems as well as human behaviour, run in silico experiments, compare the results of in silico experiments and experiments with human beings, perform simulations, analyse systems consisting of computer/physical components and human components, as well as download and upload datasets and models. Domain oriented modelling and visualisation interfaces ease the modelling and analysis processes by hiding the simulation and formal analysis engines.
PRkNN: Efficient and Privacy-Preserving Reverse kNN Query Over Encrypted Data

The advance of cloud computing has driven an emerging trend of outsourcing the rapidly growing data and query services to a powerful cloud for easing the local storage and computing pressure. Meanwhile, when taking data privacy into account, data are usually outsourced to the cloud in an encrypted form. As a result, query services have to be performed over the encrypted data. Among all kinds of query services, the reverse kNN query is highly popular in various applications, such as taxi dispatching and targeted push of multimedia information, but its privacy has not received sufficient attention. To our best knowledge, many existing privacy-preserving reverse kNN query schemes still have some limitations on the query result accuracy, dataset privacy, and flexible support for the choice of the query object and the parameter k. Aiming at addressing these limitations, in this paper, we propose an efficient and privacy-preserving reverse kNN query scheme over encrypted data, named PRkNN. Specifically, we first design a modified M-tree (MM-tree) to index the dataset and further present an MM-Tree based reverse kNN query algorithm in the filter and refinement framework. Then, we leverage the lightweight matrix encryption to carefully design a filter predicate encryption scheme (FPE) and a refinement predicate encryption scheme (RPE); and propose our PRkNN scheme by applying them to protect the privacy of the MM-Tree based reverse kNN query algorithm. Detailed security analysis shows that FPE and RPE schemes are selectively secure, and our PRkNN scheme can preserve both query privacy and dataset privacy. In addition, we conduct extensive experiments to evaluate the performance of our scheme, and the results demonstrate that our scheme is efficient.
Theorem Proving for Maude Specifications Using Lean

Maude is a specification language based on rewriting logic whose programs can be executed, model checked, and analyzed with other techniques, but not easily theorem proved. On the other hand, Lean is a modern proof assistant based on the calculus of inductive constructions with a wide library of reusable proofs and definitions. This paper presents a translation from the first formalism to the second, and a tool that derives a Lean program from a Maude module in a predictable way. Hence, theorems can be proved in Lean about Maude specifications.
Randomized Switching SAR (RS-SAR) ADC for Power and EM Side-Channel Security

Successive Approximation Register (SAR) Analog to Digital Converters (ADCs) are used in many edge Internet of Things (IoT) devices to digitize analog inputs before further processing. These inputs can be private, requiring physical side channel security to protect the data being converted. Previous work jeongs2adc2020-1, mikirandom2020 has found vulnerabilities in the power side channels of SAR ADCs and proposed limited fixes. Whereas power side channel attacks require cutting the power trace and introducing a shunt resistor for measurement, electromagnetic (EM) side channel attacks are possible to perform non-invasively external to packaging. In this work, we present a randomized switching SAR (RS-SAR) ADC protection that is effective against both power and EM side channel attacks, reducing the attack efficacy by 32x for EM attacks and 82x for power attacks.
Human Face Detection and Recognition in eHealth Implications for Blockchain Data Theory

This Facial recognition is a broad category of technologies used largely for surveillance. Now a days universities and multinational companies have already begun to use these incredibly important technologies to track attendance and identify individuals. The primary goal of a facial recognition system is to confirm a person’s identity by using a training database. A large number of training samples are required by the facial recognition system, which must be maintained in every storage site. As a result, storing facial photos on the blockchain creates a secure platform that is impervious to data breaches and cyber-attacks while also ensuring data availability. The facial recognition software system and the blockchain function are discussed, and a real-time facial recognition system has been built. Blockchain is also utilised to manage databases in an eHealth data management system. Patient facial data is saved on a distributed server in a secure manner.
SICM: A Supervised-Based Identification and Classification Model for Chinese Jargons Using Feature Adapter Enhanced BERT

In recent years, cybercriminals in darknet markets are becoming increasingly rampant and they usually converse in jargons to avoid surveillance. These jargons distort the original meaning of innocent-looking words, which makes it difficult for network regulators to understand the true meaning of dialogues content. Existing studies mainly explored jargon detection based on unsupervised methods. However, those solutions generally face significant challenges in setting appropriate jargon evaluation thresholds. To the best of our knowledge, we are the first to propose a Supervised-based jargon Identification and Classification Model (SICM). Specifically, we transform jargon detection into a sequence labeling problem. In order to better represent the unique characteristics of Chinese jargon and facilitate more effective feature fusion, we innovatively propose a Chinese jargon identification and classification model based on Feature Adapter enhanced BERT, which uses attention mechanism to integrate phonetic, glyph and lexical features into the lower layers of BERT. The experimental results demonstrate that our model outperforms existing state-of-the-art jargon detection methods, with an F1-score of 91.99%. This study provides a brand-new research idea for the Chinese jargon detection in the darknet marketplace.
Enhancing Cryptocurrency Blocklisting: A Secure, Trustless, and Effective Realization

The flourishing development of blockchain and cryptocurrency has made it a hotbed for cyber-criminals to implement virtually untraceable scams. Consequently, the blockchain ecosystem urgently needs an effective method to help users stay away from scams in order to create an enticing investment environment. Despite the massive deployment of blocklist query APIs for malicious and scam domains/URLs in the industry, we identify two core reasons why existing blocklist services find it difficult to thrive in the cryptocurrency paradigm: 1) the compelling need to protect a user query due to sensitivity and high value of query content, i.e., payment addresses; 2) the thorny issue of evaluating the quality of blocklists effectively, in the face of common practices of incompetent providers.To this end, we first provide a private and highly efficient blocklist query scheme as a basic design, which conveniently achieves backward compatibility with current blockchain payment systems at a considerably low cost. Based on this design, we propose a new framework for shareholders to evaluate the quality of blocklists. Our framework provides stronger security guarantees than other similar works, as it is capable of suppressing both individual biasing and coercive manipulation at the same time. We provide a complete game-theoretic analysis and demonstrate comprehensive evaluation results to confirm the effectiveness and efficiency of our solutions, under the settings of a practical number of shareholders.
Convolutional Neural Networks and Support Vector Machines for Five-Year Survival Analysis of Metastatic Rectal Cancer

Rectal or colorectal cancer is one of the leading causes of cancer-related death. With the advancement in surgical techniques, the survival rate has been improved. Predicting the survival rate is an important factor for enabling optimal treatments to prolong rectal-cancer patients' lives. Methods of artificial intelligence and machine learning have been applied for assisting physicians in cancer research. In this study, we investigated the use of pretrained convolutional neural networks and support vector machines for predicting the survival rate of a cohort of rectal-cancer patients using metastatic immunohistochemistry samples staining for protein RhoB. The combination of convolutional neural networks and support vector machines achieved better classification results than using individual pretrained deep networks in most cases, and where manual pathological analysis is encountered with great difficulty. In particular, the combination of ResNet-101 and SVM produced an average accuracy of 86% for non-radiotherapy, and Inception-v3 and SVM resulted in an average accuracy of 85% for radiotherapy.
Deep Ensemble Learning by Diverse Knowledge Distillation for Fine-Grained Object Classification

Ensemble of networks with bidirectional knowledge distillation does not significantly improve on the performance of ensemble of networks without bidirectional knowledge distillation. We think that this is because there is a relationship between the knowledge in knowledge distillation and the individuality of networks in the ensemble. In this paper, we propose a knowledge distillation for ensemble by optimizing the elements of knowledge distillation as hyperparameters. The proposed method uses graphs to represent diverse knowledge distillations. It automatically designs the knowledge distillation for the optimal ensemble by optimizing the graph structure to maximize the ensemble accuracy. Graph optimization and evaluation experiments using Stanford Dogs, Stanford Cars, CUB-200-2011, CIFAR-10, and CIFAR-100 show that the proposed method achieves higher ensemble accuracy than conventional ensembles.
DSE-RB: A Privacy-preserving Dynamic Searchable Encryption Framework on Redactable Blockchain

With the development of various applications of blockchain, blockchain-assisted searchable encryption technology has received wide attention as it can eliminate misbehaviours of malicious servers through the verification and incentive mechanism of blockchain. However, most existing solutions update the encrypted data by means of appending new transactions, which does not scale and wastes resources. In this paper, we explore the potential of redactable blockchain and propose a privacy-preserving dynamic searchable encryption framework (DSE-RB), which is a general scheme that guarantees reliable queries and updates on encrypted data. In particular, we first use transaction-level editing technology to achieve a more flexible update operation of encrypted data without additional transactions while avoiding the waste of storage on the chain. To better support practical applications, we use an index partition method to divide the traditional binary tree index into a plurality of sub-indexes and introduce the concept of polynomials to simplify the whole access control mechanism. We define the security model and conduct repeated experiments on real data sets to test the efficiency. Experimental results and theoretical analysis show the practicability and security of our scheme.