Reviewerly/exHarmony huggingface dataset:
Security Analysis on Practices of Certificate Authorities in the HTTPS Phishing Ecosystem

Phishing attacks are causing substantial damage albeit extensive effort in academia and industry. Recently, a large volume of phishing attacks transit toward adopting HTTPS, leveraging TLS certificates issued from Certificate Authorities (CAs), to make the attacks more effective. In this paper, we present a comprehensive study on the security practices of CAs in the HTTPS phishing ecosystem. We focus on the CAs, critical actors under-studied in previous literature, to better understand the importance of the security practices of CAs and thwart the proliferating HTTPS phishing. In particular, we first present the current landscape and effectiveness of HTTPS phishing attacks comparing to traditional HTTP ones. Then, we conduct an empirical experiment on the CAs' security practices in terms of the issuance and revocation of the certificates. Our findings highlight serious conflicts between the expected security practices of CAs and reality, raising significant security concerns. We further validate our findings using a longitudinal dataset of abusive certificates used for real phishing attacks in the wild. We confirm that the security concerns of CAs prevail in the wild and these concerns can be one of the main contributors to the recent surge of HTTPS phishing attacks.
A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

&lt;span&gt;Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.&lt;/span&gt;
Community Energy Groups: Can They Shield Consumers from the Risks of Using Blockchain for Peer-to-Peer Energy Trading?

Peer-to-peer (P2P) energy trading is emerging as a new mechanism for settling the exchange of energy between renewable energy generators and consumers. P2P provides a mechanism for local balancing when it is facilitated through distributed ledgers (‘blockchains’). Energy communities across Europe have uncovered the potential of this technology and are currently running pilots to test its applicability in P2P energy trading. The aim of this paper is to assess, using legal literature and legislation, whether the legal forms available to energy communities in the United Kingdom (UK) can help resolve some of the uncertainties around the individual use of blockchain for P2P energy trading. This includes the legal recognition of ‘prosumers’, the protection of their personal data, as well as the validity of ‘smart contracts’ programmed to trade energy on the blockchain network. The analysis has shown that legal entities, such as Limited Liability Partnerships and Co-operative Societies, can play a crucial role in providing the necessary framework to protect consumers engaging in these transactions. This is particularly the case for co-operatives, given that they can hold members liable for not respecting the rules set out in their (compulsory) governing document. These findings are relevant to other European countries, where the energy co-operative model is also used.
Application of Virtual Reality Based on 3D-CTA in Intracranial Aneurysm Surgery

As a popular technology in the field of human-computer interaction, virtual reality (VR) brings a brand new sensory experience to users by generating the environment. In recent years, while introducing the application of virtual reality technology, researchers have done a lot of work around virtual reality in many fields, such as the application of virtual reality technology in medical procedures. Combining the immersive and expandable features of virtual reality can improve the safety and accuracy of surgery. This article mainly introduces the application of 3D-CTA virtual reality technology in intracranial aneurysm surgery and aims to provide some ideas and directions for the improvement and progress of intracranial aneurysm surgery. This paper presents a research method based on virtual reality technology 3D-CTA in intracranial aneurysm surgery, including the application overview of 3D-CTA in intracranial aneurysm surgery and the virtual reality algorithm based on 3D-CTA for intracranial arteries. In addition, there is also the application of virtual reality CTA technology in the design of the intracranial aneurysm application system. Experimental results show that the average accuracy of 3D-CTA diagnosis based on virtual reality is 90.81%, and it can be put into use in the next step.
Weakly Supervised Named Entity Tagging with Learnable Logical Rules

We study the problem of building entity tagging systems by using a few rules as weak supervision.Previous methods mostly focus on disambiguating entity types based on contexts and expert-provided rules, while assuming entity spans are given.In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner.Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels.We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger.Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules.Our method can serve as a tool for rapidly building taggers in emerging domains and tasks.Case studies show that learned rules can potentially explain the predicted entities.
PROMPT: Probabilistic Motion Primitives based Trajectory Planning

We present a novel approach to motion planning for autonomous ground vehicles by formulating motion primitives as probabilistic distributions of trajectories (aka probabilistic motion primitives -ProMP) and performing stochastic optimisation on them for finding an optimal path.We show that compared to the traditional approach of using discrete motion primitives or direct stochastic optimisation of the whole path, incorporating ProMPs enables higher quality of paths by enabling constraint conditioning, combination and blending of probability distributions.We present two motion planners utilizing this approach: feasibility based trajectory sampling (PROMPT-S) and stochastic gradient-based trajectory optimisation (PROMPT-O).We show simulation results of our approach outperforming state of the art optimisation as well as discrete motion primitives based planners.We additionally illustrate the versatility of our approach by showing PROMPT's ability to handle significantly skewed motion primitives, e.g, as induced by steering failure in AGVs as well as composition of motion primitives to perform complex manoeuvres.Finally, we demonstrate the practicality of these planners by implementing them on a real self-driving vehicle navigating on structured and unstructured off-road terrains.
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking

Entity linking (EL) for the rapidly growing short text (e.g. search queries and news titles) is critical to industrial applications. Most existing approaches relying on adequate context for long text EL are not effective for the concise and sparse short text. In this paper, we propose a novel framework called Multi-turn Multiple-choice Machine reading comprehension (M3) to solve the short text EL from a new perspective: a query is generated for each ambiguous mention exploiting its surrounding context, and an option selection module is employed to identify the golden entity from candidates using the query. In this way, M3 framework sufficiently interacts limited context with candidate entities during the encoding process, as well as implicitly considers the dissimilarities inside the candidate bunch in the selection stage. In addition, we design a two-stage verifier incorporated into M3 to address the commonly existed unlinkable problem in short text. To further consider the topical coherence and interdependence among referred entities, M3 leverages a multi-turn fashion to deal with mentions in a sequence manner by retrospecting historical cues. Evaluation shows that our M3 framework achieves the state-of-the-art performance on five Chinese and English datasets for the real-world short text EL.
Modified Focal Loss in Imbalanced XGBoost for Credit Card Fraud Detection

The development of credit card use in Indonesia has not been matched by the security provided by credit card service providers.This resulted in significant losses both in terms of banking and customers.The difficulty in finding the characteristics of credit card fraud is one of the biggest challenges.Currently, many are developing machine learning models that can identify credit card fraud to help banks.Unfortunately, the model created is mostly biased towards the class which has more dominant data.This problem is caused by the imbalance of the data on the available dataset.In the previous research, Wang et al found the implementation of Focal loss in XGBoost improve the precision, recall of imbalanced data.However according to Qin et al, the parameter loss from Focal loss have poor judgement in several case of imbalanced data and to handle this weakness, they proposed Weighted -Cross Entropy Loss (W-CEL) loss in Focal loss.According to previous research, we propose a Modified Focal Loss method for Imbalanced XGBoost by entering another parameter from W-CEL loss to Focal Loss to improve the ability of Focal Loss.Focal loss itself is a method that is often used to give weight to classes that are often misinterpreted, so that with the use of imbalance parameters () from W-CEL loss is expected to improve the weighted value of the Focal Loss.We tested our proposed method in credit card fraud dataset from Université Libre de Bruxelles (ULB) machine learning group.Our proposed method produced 100 % accuracy, 0.97 precision, 0.56 recall and 0.72 MCC score in scenario 1 with extreme imbalanced data, in scenario 2 with the mild imbalanced data, the result delivered 99% accuracy, 0.88 precision, 0.87 recall and 0.89 MCC and in scenario 3 with the medium imbalance data, the result delivered 100% accuracy, 0.97 precision score, 0.72 recall score and 0.83 MCC score.The results obtained in this study proved that the proposed method makes machine learning models valid and unbiased, especially in mild-imbalanced data.However, many improvements still need to be made to medium and extreme imbalanced data.
Weight Distillation: Transferring the Knowledge in Neural Network Parameters

Knowledge distillation has proven to be effective in model acceleration and compression.It transfers knowledge from a large neural network to a small one by using the large neural network predictions as targets of the small neural network.But this way ignores the knowledge inside the large neural networks, e.g., parameters.Our preliminary study as well as the recent success in pre-training suggests that transferring parameters are more effective in distilling knowledge.In this paper, we propose Weight Distillation to transfer the knowledge in parameters of a large neural network to a small neural network through a parameter generator.On the WMT16 En-Ro, NIST12 Zh-En, and WMT14 En-De machine translation tasks, our experiments show that weight distillation learns a small network that is 1.88∼2.94×faster than the large network but with competitive BLEU performance.When fixing the size of the small networks, weight distillation outperforms knowledge distillation by 0.51∼1.82BLEU points.
Safe Reinforcement Learning via Statistical Model Predictive Shielding

Reinforcement learning is a promising approach to solving hard robotics tasks.An important challenge is ensuring safety-e.g., that a walking robot does not fall over or an autonomous car does not crash into an obstacle.We build on an approach that composes the learned policy with a backup policy-it uses the learned policy on the interior of the region where the backup policy is guaranteed to be safe, and switches to the backup policy on the boundary of this region.The key challenge is checking when the backup policy is guaranteed to be safe.Our algorithm, statistical model predictive shielding (SMPS), uses sampling-based verification and linear systems analysis to perform this check.We prove that SMPS ensures safety with high probability, and empirically evaluate its performance on several benchmarks.
UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning

Despite the success of various text generation metrics such as BERTScore, it is still difficult to evaluate the image captions without enough reference captions due to the diversity of the descriptions.In this paper, we introduce a new metric UMIC, an Unreferenced Metric for Image Captioning which does not require reference captions to evaluate image captions.Based on Vision-and-Language BERT, we train UMIC to discriminate negative captions via contrastive learning.Also, we observe critical problems of the previous benchmark dataset (i.e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions.We validate UMIC on four datasets, including our new dataset, and show that UMIC has a higher correlation than all previous metrics that require multiple references.We release the benchmark dataset and pre-trained models to compute the UMIC 1 .1 https://github.com/hwanheelee1993/UMICRef 1: A dog standing in the snow with a stick in its mouth.Ref 2: A little dog holding sticks in its mouth.Candidate: A dog standing on the snow with a dog CIDEr with Ref 1: 3.166 CIDEr with Ref 2: 0.281 Human Judgments : 1.875 out of 5 References -two giraffe standing next to each other in a field.-two giraffes are climbing a hill with mountains in the background.
Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries. In this work we address the more ambitious challenge of predicting the answers of conjunctive queries with multiple missing entities. We propose Bidirectional Query Embedding (BiQE), a method that embeds conjunctive queries with models based on bi-directional attention mechanisms. Contrary to prior work, bidirectional self-attention can capture interactions among all the elements of a query graph. We introduce two new challenging datasets for studying conjunctive query inference and conduct experiments on several benchmark datasets that demonstrate BiQE significantly outperforms state of the art baselines.
Machine Translation into Low-resource Language Varieties

State-of-the-art machine translation (MT) systems are typically trained to generate "standard" target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language.Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included.We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (sourcevariety) data.This also includes adaptation of MT systems to low-resource typologicallyrelated target languages. 1We experiment with adapting an English-Russian MT system to generate Ukrainian and Belarusian, an English-Norwegian Bokmål system to generate Nynorsk, and an English-Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications

Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention.Current RE approaches achieve fantastic results on common datasets, while they still struggle on practical applications.In this paper, we analyze the above performance gap, the underlying reason of which is that practical applications intrinsically have more hard cases.To make RE models more robust on such practical hard cases, we propose a case-oriented construction framework to build a Hard Case Relation Extraction Dataset (HacRED).The proposed HacRED consists of 65,225 relational facts annotated from 9,231 documents with sufficient and diverse hard cases.Notably, HacRED is one of the largest Chinese document-level RE datasets and achieves a high 96% F1 score on data quality.Furthermore, we apply the stateof-the-art RE models on this dataset and conduct a thorough evaluation.The results show that the performance of these models is far lower than humans, and RE applying on practical hard cases still requires further efforts.
Generating Query Focused Summaries from Query-Free Resources

The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents.In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available.We propose to decompose QFS into (1) query modeling (i.e., finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation).We introduce MARGE, a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model.Experiments across QFS benchmarks and query types show that our model achieves state-of-the-art performance despite learning from weak supervision. 1
Tri-level Robust Clustering Ensemble with Multiple Graph Learning

Clustering ensemble generates a consensus clustering result by integrating multiple weak base clustering results. Although it often provides more robust results compared with single clustering methods, it still suffers from the robustness problem if it does not treat the unreliability of base results carefully. Conventional clustering ensemble methods often use all data for ensemble, while ignoring the noises or outliers on the data. Although some robust clustering ensemble methods are proposed, which extract the noises on the data, they still characterize the robustness in a single level, and thus they cannot comprehensively handle the complicated robustness problem. In this paper, to address this problem, we propose a novel Tri-level Robust Clustering Ensemble (TRCE) method by transforming the clustering ensemble problem to a multiple graph learning problem. Just as its name implies, the proposed method tackles robustness problem in three levels: base clustering level, graph level and instance level. By considering the robustness problem in a more comprehensive way, the proposed TRCE can achieve a more robust consensus clustering result. Experimental results on benchmark datasets also demonstrate it. Our method often outperforms other state-of-the-art clustering ensemble methods. Even compared with the robust ensemble methods, ours also performs better.
Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories

Many robotic applications involve interactions between multiple agents where an agent's decisions affect the behavior of other agents.Such behaviors can be captured by the equilibria of differential games which provide an expressive framework for modeling the agents' mutual influence.However, finding the equilibria of differential games is in general challenging as it involves solving a set of coupled optimal control problems.In this work, we propose to leverage the special structure of multi-agent interactions to generate interactive trajectories by simply solving a single optimal control problem, namely, the optimal control problem associated with minimizing the potential function of the differential game.Our key insight is that for a certain class of multi-agent interactions, the underlying differential game is indeed a potential differential game for which equilibria can be found by solving a single optimal control problem.We introduce such an optimal control problem and build on single-agent trajectory optimization methods to develop a computationally tractable and scalable algorithm for planning multi-agent interactive trajectories.We will demonstrate the performance of our algorithm in simulation and show that our algorithm outperforms the state-of-the-art game solvers.To further show the real-time capabilities of our algorithm, we will demonstrate the application of our proposed algorithm in a set of experiments involving interactive trajectories for two quadcopters.
Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR

Simultaneous speech-to-text translation is widely useful in many scenarios.The conventional cascaded approach uses a pipeline of streaming ASR followed by simultaneous MT, but suffers from error propagation and extra latency.To alleviate these issues, recent efforts attempt to directly translate the source speech into target text simultaneously, but this is much harder due to the combination of two separate tasks.We instead propose a new paradigm with the advantages of both cascaded and endto-end approaches.The key idea is to use two separate, but synchronized, decoders on streaming ASR and direct speech-to-text translation (ST), respectively, and the intermediate results of ASR guide the decoding policy of (but is not fed as input to) ST.During training time, we use multitask learning to jointly learn these two tasks with a shared encoder.En-to-De and En-to-Es experiments on the MuST-C dataset demonstrate that our proposed technique achieves substantially better translation quality at similar levels of latency.