Reviewerly/exHarmony huggingface dataset:
Application of Distributed Ledger Technology (Blockchain) To the Farming Sector

Blockchain to everyone seems to comprehend, a rapidly growing business, and it is safe to assume that it has already had an effect on related sectors like banking and cryptocurrency. The success of this endeavor will go a long way toward guaranteeing blockchain technology's widespread adoption in the farming/agricultural sector. However, it is our responsibility to educate and train farmers in this regard, since this is likely to be one of the developing team's most time-consuming challenges.Once our work is up and running, clients will be able to purchase things at fair prices, and we will be able to quickly cut out the middleman who is making a profit just by delivering or selling the goods.The lending institution that this initiative is based on will allow farmers to borrow money at a reduced interest rate. All analyses are, thus, based on the most recent data and evidence at this time. In order to have a true image of the advantages of blockchain technology in agriculture/farming company, more study has to be done on large-scale data processing and established blockchain infrastructure.This article aims to work on a website that will allow farmers to enter information about their operations and produce, while also allowing retailers to get the goods they need for their stores or homes at competitive prices. By eliminating the need for middlemen, the use of this technology will cut out the people who are just interested in making money off of the sale of goods.
ENGLISH LEARNING NEEDS FOR THE MANAGEMENT DEPARTMENT

The process of using ESP (English for Specific Purposes) to learn English in universities usually does not achieve the best results. One of the contributing factors is the lack of learning programs that are not related to the scientific field they are engaged in. Therefore, it is necessary to formulate a study plan, first analyzing the needs of students to learn English with specific objectives. Therefore, this study aims to find and describe the needs of ESP English learners. This must be done so that the learning of English received is by the scientific field he masters and the field of work he will be involved in. This study used the descriptive qualitative method. The purpose of this study was to determine the need for learning English for the Management study program. The data collection technique used is a questionnaire survey. The data obtained are then classified to identify and describe the needs of students to learn English. The results of this study will be used as a reference for further research, namely syllabus planning and development of ESP English books or teaching materials.
Bitcoin Gold, Litecoin Silver:An Introduction to Cryptocurrency's Valuation and Trading Strategy

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transaction outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.
Automatic identification of misogynistic sentiments on social networks

The number of social media users is constantly growing. Automatic sentiment analysis in unstructured text using artificial intelligence is a tool that allows organizations to identify areas for improvement based on users' opinions. Natural language processing enables computational treatment of these opinions through emotion analysis and polarity identification in texts. This work focuses on the automatic identification of misogyny in unstructured texts using different classification scenarios and machine learning methods, as well as the use of meta classifiers, with the aim of identifying the pre-processing and processing techniques that lead to the best performance in this task. The results obtained show the effectiveness of automatic sentiment analysis tools on Twitter and its importance in better understanding complex social phenomena.
P‐4.2: An Image Pre‐distortion Algorithm for Near‐eye Display

With the development of near-eye display technology, optical distortion is often introduced into the design of near-eye optical system to meet people's requirements for high definition and large field of view. However, the larger the field of view, the greater the distortion of the projected image. In order to correct distorted images, this paper proposes an image pre-distortion algorithm based on nonlinear fitting and forward mapping interpolation. The radial basis function fitting algorithm is used to get a more precise mapping relationship between the original image and the pre-distorted image. The compactly supported radial basis function ( CSRBF ) interpolation algorithm based on forward mapping is used for image gray reconstruction to obtain better correction effect of image distortion. The experimental results show that the fitting error of the radial basis function fitting algorithm can be reduced to less than 1 pixel, reaching sub-pixel level. The pre-distortion algorithm has a good correction effect on the non-rotational symmetry distortion produced by the near-eye optical system, which makes the display image clear and complete.
Breaking and Fixing Garbled Circuits When a Gate has Duplicate Input Wires

Abstract Garbled circuits are a fundamental cryptographic primitive that allows two or more parties to securely evaluate an arbitrary Boolean circuit without revealing any information beyond the output using a constant number of communication rounds. Garbled circuits have been introduced by Yao (FOCS’86) and generalized to the multi-party setting by Beaver, Micali and Rogaway (STOC’90). Since then, several works have improved their efficiency by providing different garbling schemes and several implementations exist. Starting with the seminal Fairplay compiler (USENIX Security’04), several implementation frameworks decoupled the task of compiling the function to be evaluated into a Boolean circuit from the engine that securely evaluates that circuit, e.g., using a secure two-party computation protocol based on garbled circuits. In this paper, we show that this decoupling of circuit generation and evaluation allows a subtle attack on several prominent garbling schemes. It occurs when violating the implicit assumption on the circuit that gates have different input wires which is most often not explicitly specified in the respective papers. The affected garbling schemes use separate calls to a deterministic encryption function for the left and right input wire of a gate to derive pseudo-random encryption pads that are XORed together. When a circuit contains a gate where the left and right input wire are the same, these two per-wire encryption pads cancel out and we demonstrate that this can result in a complete break of privacy. We show how the vulnerable garbling schemes can be fixed easily.
PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task

This paper presents PlayGround's submission to the AmericasNLP 2023 shared task on machine translation (MT) into indigenous languages.We finetuned NLLB-600M, a multilingual MT model pre-trained on Flores-200, on 10 low-resource language directions and examined the effectiveness of weight averaging and back translation.Our experiments showed that weight averaging, on average, led to a 0.0169 improvement in the ChrF++ score.Additionally, we found that back translation resulted in a 0.008 improvement in the ChrF++ score.
Distillation-Resistant Watermarking for Model Protection in NLP

How can we protect the intellectual property of trained NLP models?Modern NLP models are prone to stealing by querying and distilling from their publicly exposed APIs.However, existing protection methods such as watermarking only work for images but are not applicable to text.We propose Distillation-Resistant Watermarking (DRW), a novel technique to protect NLP models from being stolen via distillation.DRW protects a model by injecting watermarks into the victim's prediction probability corresponding to a secret key and is able to detect such a key by probing a suspect model.We prove that a protected model still retains the original accuracy within a certain bound.We evaluate DRW on a diverse set of NLP tasks including text classification, part-of-speech tagging, and named entity recognition.Experiments show that DRW protects the original model and detects stealing suspects at 100% mean average precision for all four tasks while the prior method fails on two 1 .
Rethinking Task-Specific Knowledge Distillation: Contextualized Corpus as Better Textbook

Knowledge distillation has been proven effective when customizing small language models for specific tasks.Here, a corpus as 'textbook' plays an indispensable role, only through which the teacher can teach the student.Prevailing methods adopt a two-stage distillation paradigm: general distillation first with taskagnostic general corpus and task-specific distillation next with augmented task-specific corpus.We argue that such a paradigm may not be optimal.In general distillation, it's extravagant to let the diverse but desultory general knowledge overwhelms the limited model capacity of the student.While in task-specific distillation, the task corpus is usually limited and narrow, preventing the student from learning enough knowledge.To mitigate the issues in the two gapped corpora, we present a better textbook for the student to learn: contextualized corpus that contextualizes task corpus with large-scale general corpus through relevance-based text retrieval.Experimental results on GLUE benchmark demonstrate that contextualized corpus is the better textbook compared with jointly using general corpus and augmented task-specific corpus.Surprisingly, it enables task-specific distillation from scratch without general distillation while maintaining comparable performance, making it more flexible to customize the student model with desired model size under various computation constraints.
RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models

To better support information retrieval tasks such as web search and open-domain question answering, growing effort is made to develop retrieval-oriented language models, e.g., RetroMAE (Xiao et al., 2022b) and many others (Gao and Callan, 2021; Wang et al., 2021a).Most of the existing works focus on improving the semantic representation capability for the contextualized embedding of the [CLS] token.However, recent study shows that the ordinary tokens besides [CLS] may provide extra information, which help to produce a better representation effect (Lin et al., 2022).As such, it's necessary to extend the current methods where all contextualized embeddings can be jointly pre-trained for the retrieval tasks.In this work, we propose a novel pre-training method called Duplex Masked Auto-Encoder, a.k.a.DupMAE.It is designed to improve the quality of semantic representation where all contextualized embeddings of the pre-trained model can be leveraged.It takes advantage of two complementary auto-encoding tasks: one reconstructs the input sentence with the [CLS] embedding; the other one predicts the bagof-words feature of the input sentence with the ordinary tokens' embeddings.The two tasks are jointly conducted to train a unified encoder, where the whole contextualized embeddings are aggregated in a compact way to produce the final semantic representation.DupMAE is simple but empirically competitive: it substantially improves the pre-trained model's representation capability and transferability, where superior retrieval performances can be achieved on popular benchmarks, like MS MARCO and BEIR.Our code is released at: https://github.com/staoxiao/RetroMAE.
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations.Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts.However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge.To address this, we propose a novel Treestructure Reasoning schEmA named TREA.TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results.Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.Our code is available at https: //github.com/WindyLee0822/TREA
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension

Open-retrieval conversational machine reading comprehension (OCMRC) simulates reallife conversational interaction scenes.Machines are required to make a decision of Yes/No/Inquire or generate a follow-up question when the decision is Inquire based on retrieved rule texts, user scenario, user question and dialogue history.Recent studies try to reduce the information gap between decision-making and question generation, in order to improve the performance of generation.However, the information gap still persists because these methods are still limited in pipeline framework, where decision-making and question generation are performed separately, making it hard to share the entailment reasoning used in decision-making across all stages.To tackle the above problem, we propose a novel one-stage end-to-end framework, called Entailment Fused-T5 (EFT), to bridge the information gap between decisionmaking and question generation in a global understanding manner.The extensive experimental results demonstrate that our proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark.Our model and code are publicly available 1 .
A Diffusion Model for Event Skeleton Generation

Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task.Existing methods effectively address this task from a graph generation perspective but suffer from noise-sensitive and error accumulation, e.g., the inability to correct errors while generating schema.We, therefore, propose a novel Diffusion Event Graph Model (DEGM) to address these issues.Our DEGM is the first workable diffusion model for event skeleton generation, where the embedding and rounding techniques with a custom edge-based loss are introduced to transform a discrete event graph into learnable latent representation.Furthermore, we propose a denoising training process to maintain the model's robustness.Consequently, DEGM derives the final schema, where error correction is guaranteed by iteratively refining the latent representation during the schema generation process.Experimental results on three IED bombing datasets demonstrate that our DEGM achieves better results than other stateof-the-art baselines.Our code and data are available at
Learning the Character Inventories of Undeciphered Scripts Using Unsupervised Deep Clustering

A crucial step in deciphering a text is to identify what set of characters were used to write it.This requires grouping character tokens according to visual and contextual features, which can be challenging for human analysts when the number of tokens or underlying types is large.Prior work has shown that this process can be automated by clustering dense representations of character images, in a task which we call "script clustering".In this work, we present novel architectures which exploit varying degrees of contextual and visual information to learn representations for use in script clustering.We evaluate on a range of modern and ancient scripts, and find that our models produce representations which are more effective for script recovery than the current state-of-theart, despite using just 2% as many parameters.Our analysis fruitfully applies these models to assess hypotheses about the character inventory of the partially-deciphered proto-Elamite script.
Best-k Search Algorithm for Neural Text Generation

Modern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model.Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high variance and sometimes low quality.In this work, we propose a deterministic search algorithm balancing both quality and diversity.We first investigate the vanilla best-first search (BFS) algorithm and then propose the best-k search algorithm.Inspired by BFS, we greedily expand the top k nodes, instead of the first node, to boost efficiency and diversity.Upweighting recently discovered nodes accompanied by heap pruning ensures the completeness of the search procedure.Experiments on four NLG tasks show that best-k search yields more diverse and natural outputs compared to strong baselines, while our approach maintains high text quality.The proposed algorithm is parameter-free, lightweight, efficient, and easy-to-use. 1
ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction using Sequence-To-Sequence Transformers

Early identification of Adverse Drug Events (ADE) is critical for taking prompt actions while introducing new drugs into the market. These ADEs information are available through various unstructured data sources like clinical study reports, patient health records, social media posts, etc. Extracting ADEs and the related suspect drugs using machine learning is a challenging task due to the complex linguistic relations between drug ADE pairs in textual data and unavailability of large corpus of labelled datasets. This paper introduces ADEQA, a question- answer(QA) based approach using quasi supervised labelled data and sequence-to-sequence transformers to extract ADEs, drug suspects and the relationships between them. Unlike traditional QA models, natural language generation (NLG) based models don’t require extensive token level labelling and thereby reduces the adoption barrier significantly. On a public ADE corpus, we were able to achieve state-of-the-art results with an F1 score of 94% on establishing the relationships between ADEs and the respective suspects.
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections

Experts across diverse disciplines are often interested in making sense of large text collections.Traditionally, this challenge is approached either by noisy unsupervised techniques such as topic models, or by following a manual theme discovery process.In this paper, we expand the definition of a theme to account for more than just a word distribution, and include generalized concepts deemed relevant by domain experts.Then, we propose an interactive framework that receives and encodes expert feedback at different levels of abstraction.Our framework strikes a balance between automation and manual coding, allowing experts to maintain control of their study while reducing the manual effort required.
Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport

Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learningbased models and simulating logical reasoning with set operators. Previous works focus on specific forms of embeddings, but scoring functions between embeddings are underexplored. In contrast to existing scorning functions motivated by local comparison or global transport, this work investigates the local and global trade-off with unbalanced optimal transport theory. Specifically, we embed sets as bounded measures in R endowed with a scoring function motivated by the Wasserstein-Fisher-Rao metric. Such a design also facilitates closed-form set operators in the embedding space. Moreover, we introduce a convolution-based algorithm for linear time computation and a block diagonal kernel to enforce the trade-off. Results show that WFRE is capable of outperforming existing query embedding methods on standard datasets, evaluation sets with combinatorially complex queries, and hierarchical knowledge graphs. Ablation study shows that finding a better local and global trade-off is essential for performance improvement.
Encoding Discourse Structure: Comparison of RST and QUD

We present a quantitative and qualitative comparison of the discourse trees defined by the Rhetorical Structure Theory and Questions under Discussion models.Based on an empirical analysis of parallel annotations for 28 texts (blog posts and podcast transcripts), we conclude that both discourse frameworks capture similar structural information.The qualitative analysis shows that while complex discourse units often match between analyses, QUD structures do not indicate the centrality of segments.
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length.Many researchers have focused on designing new forms of self-attention or introducing new parameters to overcome this limitation, however a large portion of them prohibits the model to inherit weights from large pretrained models.In this work, the transformer's inefficiency has been taken care of from another perspective.We propose Fourier Transformer, a simple yet effective approach by progressively removing redundancies in hidden sequence using the ready-made Fast Fourier Transform (FFT) operator to perform Discrete Cosine Transformation (DCT).Fourier Transformer is able to significantly reduce computational costs while retain the ability to inherit from various large pretrained models.Experiments show that our model achieves state-of-the-art performances among all transformer-based models on the long-range modeling benchmark LRA with significant improvement in both speed and space.For generative seq-to-seq tasks including CNN/DailyMail and ELI5, by inheriting the BART weights our model outperforms the standard BART and other efficient models. 1