Reviewerly/exHarmony huggingface dataset:
Open-Vocabulary Argument Role Prediction For Event Extraction

The argument role in event extraction refers to the relation between an event and an argument participating in it.Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts.These studies expose obvious weakness when extending to emerging event types or new domains without available roles.Therefore, more attention and effort needs to be devoted to automatically customizing argument roles.In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction.The goal of this task is to infer a set of argument roles for a given event type.We propose a novel unsupervised framework, ROLEPRED for this task.Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles.By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected.To standardize the research of this task, we collect a new event extraction dataset from WikiPpedia including 142 customized argument roles with rich semantics.On this dataset, ROLEPRED outperforms the existing methods by a large margin.Source code and dataset are available on our GitHub repository 1 .
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection

While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance.Instead of training a dedicated multiintent detection model, we propose DialogUSR, a dialogue utterance splitting and reformulation task that first splits multi-intent user query into several single-intent sub-queries and then recovers all the coreferred and omitted information in the sub-queries.DialogUSR can serve as a plug-in and domain-agnostic module that empowers the multi-intent detection for the deployed chatbots with minimal efforts.We collect a high-quality naturally occurring dataset that covers 23 domains with a multi-step crowdsouring procedure.To benchmark the proposed dataset, we propose multiple action-based generative models that involve end-to-end and twostage training, and conduct in-depth analyses on the pros and cons of the proposed baselines.
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning

Image captioning models require the high-level generalization ability to describe the contents of various images in words.Most existing approaches treat the image-caption pairs equally in their training without considering the differences in their learning difficulties.Several image captioning approaches introduce curriculum learning methods that present training data with increasing levels of difficulty.However, their difficulty measurements are either based on domain-specific features or prior model training.In this paper, we propose a simple yet efficient difficulty measurement for image captioning using cross-modal similarity calculated by a pretrained vision-language model.Experiments on the COCO and Flickr30k datasets show that our proposed approach achieves superior performance and competitive convergence speed to baselines without requiring heuristics or incurring additional training costs.Moreover, the higher model performance on difficult examples and unseen data also demonstrates the generalization ability. References:-A pizza with multiple toppings including an egg.-A white plate holding a pizza next to plate of fries.-A pizza sits on top of a pan covered in vegetables.-A pizza pie with vegetables of some sort on it.-A full veggie pizza is ready to be eaten.
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion

This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion.Instead of finetuning, which modifies all LM parameters, we only tune a few new parameters while keeping the original LM parameters fixed.We establish this via reformulating KG completion as a "fill-in-the-blank" task, and introducing a parameter-lite encoder on top of the original LMs.We show that, by tuning far fewer parameters than finetuning, LMs transfer non-trivially to most tasks and reach competitiveness with prior state-of-theart approaches.For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval

Recent multilingual pre-trained models have shown better performance in various multilingual tasks.However, these models perform poorly on multilingual retrieval tasks due to lacking multilingual training data.In this paper, we propose to mine and generate selfsupervised training data based on a large-scale unlabeled corpus.We carefully design a mining method which combines the sparse and dense models to mine the relevance of unlabeled queries and passages.And we introduce a query generator to generate more queries in target languages for unlabeled passages.Through extensive experiments on Mr. TYDI dataset and an industrial dataset from a commercial search engine, we demonstrate that our method performs better than baselines based on various pre-trained multilingual models.Our method even achieves on-par performance with the supervised method on the latter dataset.
Composing Ci with Reinforced Non-autoregressive Text Generation

Composing Ci (also widely known as Song Ci), a special type of classical Chinese poetry, requires to follow particular format once their tune patterns are given.To automatically generate a well-formed Ci, text generation systems should strictly take into account predefined rigid formats (e.g., length and rhyme).Yet, most existing approaches regard Ci generation as a conventional sequence-to-sequence task and use autoregressive models, while it is challenging for such models to properly handle the constraints (according to tune patterns) of Ci during the generation process.Moreover, consider that with the format prepared, Ci generation can be operated by an efficient synchronous process, where autoregressive models are limited in doing so since they follow the character-by-character generation protocol.Therefore, in this paper, we propose to compose Ci through a non-autoregressive approach, which not only ensure that the generation process accommodates tune patterns by controlling the rhythm and essential meaning of each sentence, but also allow the model to perform synchronous generation.In addition, we further improve our approach by applying reinforcement learning to the generation process with the rigid constraints of Ci as well as the diversity in content serving as rewards, so as to further maintain the format and content requirement.Experiments on a collected Ci dataset confirm that our proposed approach outperforms strong baselines and previous studies in terms of both automatic evaluation metrics and human judgements. 1
Efficient Adversarial Training with Robust Early-Bird Tickets

Adversarial training is one of the most powerful methods to improve the robustness of pretrained language models (PLMs).However, this approach is typically more expensive than traditional fine-tuning because of the necessity to generate adversarial examples via gradient descent.Delving into the optimization process of adversarial training, we find that robust connectivity patterns emerge in the early training phase (typically 0.15 ∼ 0.3 epochs), far before parameters converge.Inspired by this finding, we dig out robust early-bird tickets (i.e., subnetworks) to develop an efficient adversarial training method: (1) searching for robust tickets with structured sparsity in the early stage; (2) fine-tuning robust tickets in the remaining time.To extract the robust tickets as early as possible, we design a ticket convergence metric to automatically terminate the searching process.Experiments show that the proposed efficient adversarial training method can achieve up to 7× ∼ 13× training speedups while maintaining comparable or even better robustness compared to the most competitive state-of-the-art adversarial training methods.
P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training

Conventional autoregressive left-to-right (L2R) sequence generation faces two issues during decoding: limited to unidirectional target sequence modeling, and constrained on strong local dependencies.To address the aforementioned problem, we propose P 3 LM, a probabilistically permuted prophet language model, which strengthens the modeling of bidirectional information and long token dependencies for sequence generation.Specifically, P 3 LM learns to generate tokens in permuted order upon an order-aware transformer decoder, as well as to generate the corresponding future N tokens with a multi-stream attention mechanism.Extensive experiments are conducted on the GLGE benchmark, which includes four datasets for summarization, two for question generation, one for conversational question answering, and one for dialog response generation, where P 3 LM achieves state-of-the-art results compared with strong publicly available generative pre-training methods.
Anomalous Taxi Trajectory Detection using Popular Routes in Different Traffic Periods

Anomalous trajectory detection is an important approach to detecting taxi fraud behaviors in urban traffic systems.The existing methods usually ignore the integration of the trajectory access location with the time and trajectory structure, which incorrectly detects normal trajectories that bypass the congested road as anomalies and ignores circuitous travel of trajectories.Therefore, this study proposes an anomalous trajectory detection algorithm using the popular routes in different traffic periods to solve this problem.First, to obtain popular routes in different time periods, this study divides the time according to the time distribution of the traffic trajectories.Second, the spatiotemporal frequency values of the nodes are obtained by combining the trajectory point moments and time span to exclude the interference of the temporal anomaly trajectory on the frequency.Finally, a gridded distance measurement method is designed to quantitatively measure the anomaly between the trajectory and the popular routes by combining the trajectory position and trajectory structure.Extensive experiments are conducted on real taxi trajectory datasets; the results show that the proposed method can effectively detect anomalous trajectories.Compared to the baseline algorithms, the proposed algorithm has a shorter running time and a significant improvement in F-Score, with the highest improvement rate of 7.9%, 5.6%, and 10.7%, respectively.
Research on Drilling Data Exchanging Technology Based on Landmark EDM

As know Landmark Software Suite is widely used in engineering of oil industry. So, learning and using of its data is necessary for our researching. Engineer's Data Model (EDM) as its solution for data management plays a very important role in this software suite. EDM is consisted of two parts, data storage and data exchange. EDM use SQL Server as its storage media which makes data storage secure. Data is stored in a bunch of well-organized data tables of SQL Server. EDM covers data storage of all applications. As we know, XML is mostly used technology in data exchanging. In order to sharing data between customers, EDM use a xml file to exchange data. We name this XML file as EDM.XML. In this paper we mainly talk about this data exchanging technology, as this xml file a direct reflection of EDM's data structure. We can get the overview of the data storage, furthermore we can also learn much about the management of Survey, Geodetic, Coordinate Reference System, Magnetic, Survey Tool etc. EDM.XML is organized as a node tree, so we will introduce EDM.XML from the tree root to leaf and traverse the primary leaf nodes in detail. After that we will make a well understanding of the principle of its data storage design and some basic aspect of well design.
Cover letter for Journal Submition: Structuring and Functional Content

Introduction. The author(s), in order to increase the effectiveness of communication with the editors of the journal, should be familiar with the mechanisms for its optimization. Proper use of these mechanisms promotes the acceleration of the editorial and publishing cycle of the manuscript. Сover Letter in the Structure of the Author’s Communication with the Editors. A correctly structured pre-publication request is able to demonstrate the level of the author’s expertise in the topic and the level of development of his academic culture. This editorial highlights the structure and strategies for constructing a cover letter. Conclusion. Authors using the proposed structure of the cover letter will be able to achieve maximum effectiveness of the cover letter .
An Overview of Intellectual Property Rights (IPR) in the Digital Environment in India

In the current environment, IPR awareness is essential for technical breakthroughs, and in the developing knowledge-based economy, its significance is anticipated to increase. Because it is become harder to demonstrate rights violations whenever they happen in the digital realm, it is crucial that information makers are aware of and knowledgeable about IPR. The current study addresses the subject of intellectual property rights (IPR) in the digital environment. We are concentrating on an overview of IPR in the digital environment of India.
Central limit theorem for the stratified resampling mechanism

The stratified resampling mechanism is one of the resampling schemes commonly used in the resampling steps of particle filters. In the present paper, we prove a central limit theorem for this mechanism under the assumption that the initial positions are independent and identically distributed and the weights proportional to a positive function of the positions such that the image of their common distribution by this function has a non zero component absolutely continuous with respect to the Lebesgue measure. This result relies on the convergence in distribution of the fractional part of partial sums of the normalized weights to some random variable uniformly distributed on $[0,1]$, which is established in the companion paper \cite{CLTfract} by overcoming the difficulty raised by the coupling through the normalization. Under the conjecture that a similar convergence in distribution remains valid at the next steps of a particle filter which alternates selections according to the stratified resampling mechanism and mutations according to Markov kernels, we provide an inductive formula for the asymptotic variance of the resampled population after $n$ steps. We perform numerical experiments which support the validity of this formula.
A Survey of Spanish Clinical Language Models

This survey focuses in encoder Language Models for solving tasks in the clinical domain in the Spanish language. We review the contributions of 17 corpora focused mainly in clinical tasks, then list the most relevant Spanish Language Models and Spanish Clinical Language models. We perform a thorough comparison of these models by benchmarking them over a curated subset of the available corpora, in order to find the best-performing ones; in total more than 3000 models were fine-tuned for this study. All the tested corpora and the best models are made publically available in an accessible way, so that the results can be reproduced by independent teams or challenged in the future when new Spanish Clinical Language models are created.
Sistem Informasi Geografis Pencarian Studio Foto Terdekat dan Jalur Terpendek Menggunakan Metode Dijkstra (Studi Kasus Kota Bandar Lampung)

AbstractThe very rapid development of information, especially in the internet sector, indirectly facilitates the dissemination of information. One of the information needed by the community is GIS (Geographic Information System) technology, which is a system that is oriented to geographic position and is capable of manipulating, processing and displaying spatial data and attributes. In the city of Bandar Lampung, there are several photo studios scattered at several locations with different facilities and services in each location. However, there are still many who do not know the location and facilities of the Photo Studio and people usually look for a photo studio by asking friends or colleagues. In addition, the search model on Google is inefficient because it will automatically first visit the Google website before finding a location that is sometimes not updated. Therefore, the Geographical Information System is one solution to dealing with the problem by proposing to design a web-based geographic information system for the search for the nearest Photo Studio using the Dijkstra method. The results of the research on the GIS Photo Studio application system have been running well and in accordance with what is expected, where the system can help users find location points on the map, travel routes, distance and travel time.
Solving Abalone on Small Boards

Abalone is a 2-player board game with perfect information. The game is played on a 5x5x5 hexagonal grid and ends when a player pushes 6 of their opponents' pieces off the board. Abalone is similar to games like chess and Go in that all three games have high branching factors, making it difficult for a computer to determine the outcome of a game. However, solving smaller, simplified versions of Abalone can offer insight into how to play the full-size game optimally. In this paper, we strongly solve a variation of Abalone played on a 2x2x2 hexagonal board. We also weakly solve an Abalone variation on a 2x2x3 hexagonal board.
Practical EMI Attacks on Smartphones with Users' Commands Cancelled

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users' operations and thus taking full control over victim devices. Expelliarmus neutralizes users' touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users' commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.
Analysis of Search Engine Optimization Application on Markas Gamers' Website

Objective – Along with the rapid development of information technology and the increasing number of websites, search engines like Google will be increasingly strict in sorting out the best websites that have useful information for their users. This causes website owners to increasingly aggressively improve the quality and popularity of their websites in various ways, such as using advertisements, branding on social media, to the use of SEO (Search Engine Optimization) to support the quality of the website so that it can compete in search engine ranking results.&#x0D; Methodology – Method used in this research is by redesigning the user interface and applying SEO methods.&#x0D; Findings – Being able to improve the metrics of the website can be significantly improved both from its UR and DR and increase visitor traffic as a source of profit for a website.&#x0D; Novelty – This research was conducted on a new website that initially had not implemented SEO and had not been maximized in designing its User Interface.&#x0D; Keywords: Google; Markas Gamers; Redesign; SEO; Website Optimization
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction

Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understanding and knowledge discovery, recent works in this domain have largely focused on binary classification of a text segment as causal or non-causal. In this regard, we perform a thorough analysis of three sequence tagging models for causal knowledge extraction and compare it with a span based approach to causality extraction. Our experiments show that embeddings from pre-trained language models (e.g. BERT) provide a significant performance boost on this task compared to previous state-of-the-art models with complex architectures. We observe that span based models perform better than simple sequence tagging models based on BERT across all 4 data sets from diverse domains with different types of cause-effect phrases.
An Ethereum-based Product Identification System for Anti-counterfeits

Fake products are items that are marketed and sold as genuine, high-quality products but are counterfeit or low-quality knockoffs. These products are often designed to closely mimic the appearance and branding of the genuine product to deceive consumers into thinking they are purchasing the real thing. Fake products can range from clothing and accessories to electronics and other goods and can be found in a variety of settings, including online marketplaces and brick-and-mortar stores. Blockchain technology can be used to help detect fake products in a few different ways. One of the most common ways is through the use of smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. This allows for a high level of transparency and traceability in supply chain transactions, making it easier to identify and prevent the sale of fake products and the use of unique product identifiers, such as serial numbers or QR codes, that are recorded on the blockchain. This allows consumers to easily verify the authenticity of a product by scanning the code and checking it against the information recorded on the blockchain. In this study, we will use smart contracts to detect fake products and will evaluate based on Gas cost and ethers used for each implementation.
Syntax and Semantics Meet in the “Middle”: Probing the Syntax-Semantics Interface of LMs Through Agentivity

Recent advances in large language models have prompted researchers to examine their abilities across a variety of linguistic tasks, but little has been done to investigate how models handle the interactions in meaning across words and larger syntactic forms—i.e. phenomena at the intersection of syntax and semantics. We present the semantic notion of agentivity as a case study for probing such interactions. We created a novel evaluation dataset by utilitizing the unique linguistic properties of a subset of optionally transitive English verbs. This dataset was used to prompt varying sizes of three model classes to see if they are sensitive to agentivity at the lexical level, and if they can appropriately employ these word-level priors given a specific syntactic context. Overall, GPT-3 text-davinci-003 performs extremely well across all experiments, outperforming all other models tested by far. In fact, the results are even better correlated with human judgements than both syntactic and semantic corpus statistics. This suggests that LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery than select corpora for certain tasks.