Reviewerly/exHarmony huggingface dataset:
An empirical comparison of the security and performance characteristics of topology formation algorithms for Bitcoin networks

There is an increasing demand for digital crypto-currencies to be more secure and robust to meet the following business requirements: (1) low transaction fees and (2) the privacy of users. Nowadays, Bitcoin is gaining traction and wide adoption. Many well-known businesses have begun accepting bitcoins as a means of making financial payments. However, the susceptibility of Bitcoin networks to information propagation delay, increases the vulnerability to attack of the Bitcoin network, and decreases its throughput performance. This paper introduces and critically analyses new network clustering methods, named Locality Based Clustering (LBC), Ping Time Based Approach (PTBC), Super Node Based Clustering (SNBA), and Master Node Based Clustering (MNBC). The proposed methods aim to decrease the chances of performing a successful double spending attack by reducing the information propagation delay of Bitcoin. These methods embody proximity-aware extensions to the standard Bitcoin protocol, where proximity is measured geographically and in terms of latency. We validate our proposed methods through a set of simulation experiments and the findings show how the proposed methods run and their impact in optimising the transaction propagation delay. Furthermore, these new methods are evaluated from the perspective of the Bitcoin network’s resistance to partitioning attacks. Numerical results, which are established via extensive simulation experiments, demonstrate how the extensions run and also their impact in optimising the transaction propagation delay. We draw on these findings to suggest promising future research directions for the optimisation of transaction propagation delays.
Double-X: Towards Double-Cross-Based Unlock Mechanism on Smartphones

Thanks to the convenience and the increasing functionalities, mobile devices especially smartphones are becoming an essential electronic device in people’s daily lives. Users can take the smartphone for online shopping and payment, as well as chatting with friends. However, with more private and sensitive information stored on such devices, how to secure the phone data becomes an open challenge. To protect a smartphone from unauthorized access, a direct and intuitive approach is to deploy an unlock mechanism, which requires users to input a correct pattern and unlock the phone. In the literature, combining behavioral biometrics can further enhance the security of unlock mechanisms, e.g., Android unlock patterns. In this work, we develop Double-X, a double-cross-based unlock scheme that requires users to unlock the phone by inputting two cross shapes on the selected dots. To authenticate the user, Double-X has to check the selected dots and the behavioral features when drawing the cross shapes. To examine the scheme performance, we perform two user studies with 80 participants with several typical supervised algorithms. The results indicate that participants can achieve a good success rate (e.g., 95%) under our scheme compared with two similar schemes.
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion

Text-to-SQL parsers map natural language questions to programs that are executable over tables to generate answers, and are typically evaluated on large-scale datasets like SPIDER (Yu et al., 2018).We argue that existing benchmarks fail to capture a certain out-of-domain generalization problem that is of significant practical importance: matching domain specific phrases to composite operations over columns.To study this problem, we propose a synthetic dataset and a re-purposed train/test split of the SQUALL dataset (Shi et al., 2020) as new benchmarks to quantify domain generalization over column operations.Our results indicate that existing state-of-the-art parsers struggle in these benchmarks.We propose to address this problem by incorporating prior domain knowledge by preprocessing table schemas, and design a method that consists of two components: schema expansion and schema pruning.This method can be easily applied to multiple existing base parsers, and we show that it significantly outperforms baseline parsers on this domain generalization problem, boosting the underlying parsers' overall performance by up to 13.8% relative accuracy gain (5.1% absolute) on the new SQUALL data split.
Anomaly Detection of Metro Station Tracks Based on Sequential Updatable Anomaly Detection Framework

The intrusion of foreign objects in tracks, one of the sources of injuries and fatalities in metro stations, can be solved as an anomaly detection task. However, the existing anomaly detection methods rarely consider the importance of updating the new knowledge from false alarm data, resulting in repeated mistakes. These methods are also impractical for edge devices that cannot afford the high calculation cost. A sequential updatable anomaly detection (SUAD) framework is proposed to tackle these problems. This framework is based on the Robbin-Monro algorithm and a fast version of Mahalanobis distance. A well-trained model of SUAD can continue to learn new knowledge through the sequential knowledge update module based on the Robbin-Monro algorithm without reviewing the old data. SUAD utilizes a new Mahalanobis distance calculation method based on principal component analysis. This new method exhibits a fast inference speed with a lighter model size than before. SUAD is evaluated on a self-built Metro Anomaly Detection (MAD) dataset and three public datasets. SUAD achieves an average area under the receiver operating characteristic curve score of 99.4% at image level and 99.6% at pixel level on MAD. SUAD also reduces at least 78% model size and 60% memory usage. Competitive results are also achieved in public datasets, including MVTec AD, beanTech Anomaly Detection, and CIFAR-10.
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge

Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pretrained language models (PLMs) into neural topic modeling.However, we found that employing PWEs and PLMs for topic modeling only achieved limited performance improvements but with huge computational overhead.In this paper, we propose a novel strategy to incorporate external knowledge into neural topic modeling where the neural topic model is pretrained on a large corpus and then fine-tuned on the target dataset.Experiments have been conducted on three datasets and results show that the proposed approach significantly outperforms both current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs.Moreover, further study shows that the proposed approach greatly reduces the need for the huge size of training data.
Understanding the Customization of Dependency Bots: The Case of Dependabot

Dependency bots are becoming a de facto solution to automate dependency management, ensuring the quality and trustworthiness of software supply chains. We analyze how dependency bots are customized. We provide designers of dependency bots and software developers with insights into the compromises that should carefully be considered regarding customization features.
CoCoLM: Complex Commonsense Enhanced Language Model with Discourse Relations

Large-scale pre-trained language models have demonstrated strong knowledge representation ability.However, recent studies suggest that even though these giant models contain rich simple commonsense knowledge (e.g., bird can fly and fish can swim.),they often struggle with complex commonsense knowledge that involves multiple eventualities (verbcentric phrases, e.g., identifying the relationship between "Jim yells at Bob" and "Bob is upset").To address this issue, in this paper, we propose to help pre-trained language models better incorporate complex commonsense knowledge.Unlike direct finetuning approaches, we do not focus on a specific task and instead propose a general language model named CoCoLM.Through the careful training over a large-scale eventuality knowledge graph ASER, we successfully teach pre-trained language models (i.e., BERT and RoBERTa) rich discourse-level commonsense knowledge among eventualities.Experiments on multiple commonsense tasks that require the correct understanding of eventualities demonstrate the effectiveness of CoCoLM.
A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns

Computational approaches in historical linguistics have been increasingly applied during the past decade and many new methods that implement parts of the traditional comparative method have been proposed.Despite these increased efforts, there are not many easy-to-use and fast approaches for the task of phonological reconstruction.Here we present a new framework that combines state-of-the-art techniques for automated sequence comparison with novel techniques for phonetic alignment analysis and sound correspondence pattern detection to allow for the supervised reconstruction of word forms in ancestral languages.We test the method on a new dataset covering six groups from three different language families.The results show that our method yields promising results while at the same time being not only fast but also easy to apply and expand.
Multigraph-Based Low-Rank Tensor Approximation for Hyperspectral Image Restoration

Low-rank-tensor-approximation (LRTA)-based hyperspectral image (HSI) restoration has drawn increasing attention. However, most of the methods construct a hidden low-rank tensor by utilizing the non-local self-similarity (NLSS) and global spectral correlation (GSC) inherited by HSIs. Although achieving state-of-the-art (SOTA) restoration performance, NLSS and GSC have limitations. NLSS is introduced from natural image denoising to remove spatially independent identically distributed (i.i.d.) Gaussian and impulse noise. While GSC, which is naturally possessed by HSIs, is adopted to maintain the spectral integrity and remove spectrally, i.i.d., degradations. Therefore, NLSS and GSC may not be successfully used for complex HSI restoration tasks, such as destriping, cloud removal and recovery of atmospheric absorption bands. To solve the issue, borrowing the idea from manifold learning, the geometry information characterized by proximity relationship, is integrated with the LRTA to solve the above issue, named as multi-graph-based LRTA (MGLRTA). Different with most of the existing methods, the proposed MGLRTA directly models an HSI as a low-rank tensor and efficiently explores the extra proximity information on the defined graphs that are not only inherited by the low-rank constraints but also naturally possessed in HSIs. A well-posed iterative algorithm is designed to solve the restoration problem. Experimental results on different datasets that cover several severe degradation scenarios demonstrate that the proposed MGLRTA outperforms the SOTA HSI restoration methods.
Zero- and Few-Shot NLP with Pretrained Language Models

The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where data collection is costly or otherwise difficult. This is a challenging setting both academically and practically—particularly because training neutral models typically require large amount of labeled data. More recently, advances in pretraining on unlabelled data have brought up the potential of better zero-shot or few-shot learning (Devlin et al., 2019; Brown et al., 2020). In particular, over the past year, a great deal of research has been conducted to better learn from limited data using large-scale language models. In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for zero- and few-shot learning with pretrained language models. Additionally, our goal is to reveal new research opportunities to the audience, which will hopefully bring us closer to address existing challenges in this domain.
A novel user behavior prediction model based on automatic annotated behavior recognition in smart home systems

User behavior prediction has become a core element to Internet of Things (IoT) and received promising attention in the related fields. Many existing IoT systems (e.g. smart home systems) have been deployed various sensors and the user's behavior can be predicted through the sensor data. However, most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction. Therefore, it is a challenge to provide an automatic behavior prediction model based on the original sensor data. To solve the problem, this paper proposed a novel automatic annotated user behavior prediction (AAUBP) model. The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining (DVSM) behavior recognition model and behavior prediction model based on the Long Short Term Memory (LSTM) network. To evaluate the model, we performed several experiments on a real-world dataset tuning the parameters. The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.
Practical Sensitivity Bound for Multiple Phase Estimation with Multi‐Mode N00N$N00N$ States

Abstract Quantum enhanced multiple phase estimation is essential for various applications in quantum sensors and imaging. For multiple phase estimation, the sensitivity enhancement is dependent on both quantum probe states and measurement. It is known that multi‐mode states can outperform other probe states for estimating multiple phases. However, it is generally not feasible in practice to implement an optimal measurement to achieve the quantum Cramer–Rao bound (QCRB) under a practical measurement scheme using a multi‐mode beam splitter in interferometric phase estimation. Here, a strategy to achieve the best practical sensitivity by optimizing both mode‐amplitudes of multi‐mode states and a split ratio of a multi‐mode beam splitter is investigated. Then, it is experimentally demonstrated that the best sensitivity is achieved when an amplitude‐balanced multi‐mode state and a multi‐mode beam splitter with an unbalanced ratio are used in three‐mode interferometric phase estimation. The results show that the lower QCRB cannot guarantee better sensitivity under a practical measurement scheme, thus it is more desirable to enhance the practical sensitivity rather than the QCRB. It is believed that this strategy can provide a powerful tool for practical applications in multiple phase estimation.
Deep Machine Learning Histopathological Image Analysis for Renal Cancer Detection

Renal cancer is one of the top causes of cancer-related deaths among men globally. Early detection of renal cancer is crucial because it can significantly improve the probability of survival rate. However, assessing the histopathological renal tissues is a labor-intensive job and traditionally, this is done manually by a pathologist, leading to a high possibility of misdetection and/or misdiagnosis especially in the early stages and prone to inter-pathologist variations. The development of an automatic histopathological diagnosis of renal cancer can greatly reduce the bias and provide accurate characterization of diseases even though the nature of pathology and microscopy are highly complex and complicated. This paper investigated the use of deep learning methods to develop a binary histopathological image classification model (cancer or normal). 783 whole slide images of renal tissue were processed into patches using PyHIST tool at 5x magnification power before feeding them to the deep learning models. Five pre-trained deep learning architectures, namely VGG, ResNet, DenseNet, MobileNet, and EfficientNet, were trained with transfer learning on the CPTAC-CCRCC dataset and their performances were evaluated. EfficientNetB0 achieved the state-of-the-art accuracy (97%), specificity (94%), F1-score (98%) and AUC (96%) but slightly inferior recall (98%) when compared to the best published results in the literature. These findings showed that the proposed deep learning approach can effectively classify the histopathological images of renal tissue into tumor and non-tumor classes to make pathology diagnosis more efficient and less labor intensive.
Students’ Perception on the Utilization of Learningapps.org for Self-Study Materials

Learning and teaching process in online learning setting needs to be more flexible especially in connecting the students with the learning sources resources in order to keep up their independent and self-regulated learning.Learningapps.org is a free online learning platform for teachers to create numerous learning activities such as matching, fill in the blanks, word search, crosswords, maps, voting, ordering, and many others.In addition, this platform has a template for exercises with elements of games which makes it suitable for all types of students.This study uses a descriptive quantitative approach to aim at identifying the senior high school students' perceptions of the implementation of self-study materials in online learning using learningapps.org.It involved 37 students of Islamic senior high school in Indonesia.Data collection was facilitated using questionnaires, and interviews.The data analysis was undertaken through then used SPSS 22 for detailed quantification.The findings revealed the students perceived the ease of use and usefulness of learningapps.orghighly positively in online class.Factors such as clear and easy language, various and attractive amount of the exercises, relevant level of the exercises, and automatic feedback of this application made them learn English more independently.The unstable internet connectivity and low motivation were challenges that most of the students had during the self-study.The result of this study can be used by English teachers to help students improve their self-regulated learning.
Don’t Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning

One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments. Recently, data augmentation techniques aiming at enhancing data diversity have demonstrated proven performance in improving the generalization ability of learned policies. However, due to the sensitivity of RL training, naively applying data augmentation, which transforms each pixel in a task-agnostic manner, may suffer from instability and damage the sample efficiency, thus further exacerbating the generalization performance. At the heart of this phenomenon is the diverged action distribution and high-variance value estimation in the face of augmented images. To alleviate this issue, we propose Task-aware Lipschitz Data Augmentation (TLDA) for visual RL, which explicitly identifies the task-correlated pixels with large Lipschitz constants, and only augments the task-irrelevant pixels for stability. We verify the effectiveness of our approach on DeepMind Control suite, CARLA and DeepMind Manipulation tasks. The extensive empirical results show that TLDA improves both sample efficiency and generalization; it outperforms previous state-of-the-art methods across 3 different visual control benchmarks.
TransModE: Translational Knowledge Graph Embedding Using Modular Arithmetic

In order to improve link prediction process in a knowledge graph, we tackle the problem of learning representations of entities and relations. The strength of the existing models in this domain mainly relies on its ability of modeling and inferring the patterns of the relations. In this paper, we propose a new knowledge graph embedding approach called TransModE that has the ability to represent all simple and complex relation patterns. Inspired by TransE, TransModE represents the relations between the entities of a knowledge graph as a transition in the modulus space. Experimenting our model on multiple benchmark knowledge graphs shows the simplicity and the scalability of TransModE. It also shows that TransModE outperforms existing state-of-the-art models by being able to infer and model all types of relations.
An efficient quantum hash-based CP-ABE framework on cloud storage data

With the exponential growth of cloud data and storage space, cloud security has become one of the interesting research areas of cloud computing servers. Attribute-based encryption is a public key encryption algorithm that allows cloud users to secure their sensitive information in the public cloud servers. Quantum key distribution (QKD) is required to improve the security of communication systems. Quantum cryptographic scheme completely depends on quantum mechanics. The major objective of quantum key distribution is to generate a key that takes part in encryption. Traditional attribute-based encryption models are insecure and possible of key distribution attacks using man-in-the-middle attacks. Also, as the size of the input data increases, traditional ABE models failed to compute efficient secret key due to computational time and network overhead. To overcome these issues, a novel chaotic integrity and quantum key distribution (QKD)-based cipher text policy ABE model is implemented in cloud environment. Experimental results proved that the proposed model has high computation speed, storage overhead and secured key distribution compared to traditional CP-ABE, KPABE and QKD-ABE models.
Adaptively Dynamic RRT*-Connect: Path Planning for UAVs Against Dynamic Obstacles

Path planning for Unmanned Aerial Vehicles (UAVs), especially in three-dimensional environments with dynamic obstacles, is an active area of research. In recent years, RRT-based algorithms have been attracting significant interest, and various improvements are introduced to RRT to make it applicable in such scenarios. Some methods leverage re-planning mechanisms to avoid collision with the dynamic threats, however, most of them fail to fully reuse historical information, hence having limitations in saving the planning costs. This paper presents Adaptively Dynamic RRT*-Connect (ADRRT*-Connect), a novel RRT-based path planning algorithm that enables UAVs to fly safely in three-dimensional environments with dynamic threats. To improve efficiency in sampling new nodes, we propose a strategy to automatically adjust the heuristic factor based on feedback from the sampling results. For avoiding collision with dynamic threats, we introduce a pruning-reconnecting mechanism to repair the path when new obstacles emerge. Our approach is economical in the consumption of tree nodes. In comparison to existing benchmarks, simulations have shown that our proposed algorithm only requires 3.5% new nodes to repair the path in re-planning.
Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context

Physical measurements constitute a large portion of numbers in academic papers, engineering reports, and web tables.Current benchmarks fall short of properly evaluating numeracy of pretrained language models on measurements, hindering research on developing new methods and applying them to numerical tasks.To that end, we introduce a novel task, Masked Measurement Prediction (MMP), where a model learns to reconstruct a number together with its associated unit given masked text.MMP is useful for both training new numerically informed models as well as evaluating numeracy of existing systems.To address this task, we introduce a new Generative Masked Measurement (GeMM) model that jointly learns to predict numbers along with their units.We perform fine-grained analyses comparing our model with various ablations and baselines.We use linear probing of traditional pretrained transformer models (RoBERTa) to show that they significantly underperform jointly trained number-unit models, highlighting the difficulty of this new task and the benefits of our proposed pre-training approach.We hope this framework accelerates progress towards building more robust numerical reasoning systems in the future. 1