Reviewerly/exHarmony huggingface dataset:
An Integrated Secure Blockchain and Deep Neural Network Framework for Better Agricultural Business Outcomes

Agriculture is vital to the survival of humanity, as it involves manufacturing, security, traceability, and long-term resource management. With resources shrinking, it is more crucial than ever to develop technology that helps agriculture survive. Agriculture will gain greater autonomy and intelligence as a result of the integration of blockchain and deep learning for agricultural applications, which will be more efficient and optimal. Blockchain has various applications for agriculture business: food supply chain, logistics, and crowdsourcing in addition to others. The data imply that using blockchain for agricultural business reasons has a lot of advantages. IoT devices generate a lot of data, and a decentralized data structure is provided by blockchain for storing and retrieving this data, which is shared with a lot of untrustworthy parties. Deep learning is a powerful analytic tool that provides scalable and accurate data analysis. This combination allows for secure data and resource exchange across the network's numerous devices in agriculture. We propose a secure framework based on deep neural networks and blockchain for securing and enhancing business benefits in agriculture through this research. This system can also reward farmers via smart contracts if they solve an issue that is shared by other farmers in the farmer community.
Method of Tabular Implementation of the Arithmetic Operation of Multiplying Two Numbers Represented in the System of Residual Classes

The article discusses the method of tabular implementation of the arithmetic operation of multiplying two numbers represented in the system of residual classes (SRC). The existing methods of tabular implementation of the arithmetic operation of multiplying two numbers in the SRC allow you to implement this operation only in a positive numerical range. This circumstance significantly narrows the area of effective application of tabular arithmetic (TA) in the SRC. In order to further develop the scope of TA, the method of tabular implementation of the arithmetic operation of multiplication in the SRC has been improved in the article due to the additional possibility of simultaneously performing operations of both arithmetic and algebraic multiplication. For this, a mathematical model (MM) of the process of tabular implementation of the operation of modular multiplication in positive and negative numerical ranges is synthesized. The MM developed in the article is the basis for the method of tabular implementation of the arithmetic operation of multiplication. An example of the application of this method of multiplication of two numbers in the SRC in the negative numerical range is given. In the future, MM and an improved method for multiplying two numbers can be used as the basis for a generalized method for fast processing of arithmetic operations of modular addition, subtraction, and multiplication in the SRC.
Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study

Reusing Electronic Health Records (EHRs) for Machine Learning (ML) leads on many occasions to extremely incomplete and sparse tabular datasets, which can hinder the model development processes and limit their performance and generalization. In this study, we aimed to characterize the most effective data imputation techniques and ML models for dealing with highly missing numerical data in EHRs, in the case where only a very limited number of data are complete, as opposed to the usual case of having a reduced number of missing values. We used a case study including full blood count laboratory data, demographic and survival data in the context of COVID-19 hospital admissions and evaluated 30 processing pipelines combining imputation methods with ML classifiers. The imputation methods included missing mask, translation and encoding, mean imputation, k-nearest neighbors’ imputation, Bayesian ridge regression imputation and generative adversarial imputation networks. The classifiers included k-nearest neighbors, logistic regression, random forest, gradient boosting and deep multilayer perceptron. Our results suggest that in the presence of highly missing data, combining translation and encoding imputation—which considers informative missingness—with tree ensemble classifiers—random forest and gradient boosting—is a sensible choice when aiming to maximize performance, in terms of area under curve. Based on our findings, we recommend the consideration of this imputer-classifier configuration when constructing models in the presence of extremely incomplete numerical data in EHR.
Why Should I Trust Your Code?

For Confidential Computing to become ubiquitous in the cloud, in the same way that HTTPS became the default for networking, a different, more flexible approach is needed. Although there is no guarantee that every malicious code behavior will be caught upfront, precise auditability can be guaranteed: Anyone who suspects that trust has been broken by a confidential service should be able to audit any part of its attested code base, including all updates, dependencies, policies, and tools. To achieve this, we propose an architecture to track code provenance and to hold code providers accountable. At its core, a new Code Transparency Service (CTS) maintains a public, append-only ledger that records all code deployed for confidential services. Before registering new code, CTS automatically applies policies to enforce code-integrity properties. For example, it can enforce the use of authorized releases of library dependencies and verify that code has been compiled with specific runtime checks and analyzed by specific tools. These upfront checks prevent common supply-chain attacks.
Information Development and Student Talent Cultivation of Modern Chinese Language and Literature in the Context of Big Data

Abstract The combination of Chinese language and literature and information technology in the context of big data is an important way to cultivate all-around Chinese language talents. This paper constructs a teaching platform for modern Chinese language and literature based on a big data information fusion algorithm and first establishes a differential equation to express the development capability of Chinese language informatization. The quantitative recursive analysis is used to perform data fusion and teaching ability assessment for the platform, and the data classification is used to collect big data and quantitatively analyze the teaching ability assessment value of the platform, from which the utilization rate of teaching resource distribution is obtained. Finally, according to the feature vector of platform teaching ability assessment, clustering, and integration of index parameters are carried out to realize student talent cultivation and platform informatization development. The results show that the experimental and control groups were set up to teach through this platform for one semester, and the analysis of the students’ comprehensive final exam scores yielded a total average score of 86.8606 for the experimental class, which was significantly greater than the total average score of 81.4418 for the control class, thus indicating that the education system combining modern Chinese language and literature with informatization can enhance students’ talent ability.
Transferring Vision-Language Models for Visual Recognition: A Classifier Perspective

Abstract Transferring knowledge from pre-trained deep models for downstream tasks, particularly with limited labeled samples, is a fundamental problem in computer vision research. Recent advances in large-scale, task-agnostic vision-language pre-trained models, which are learned with billions of samples, have shed new light on this problem. In this study, we investigate how to efficiently transfer aligned visual and textual knowledge for downstream visual recognition tasks. We first revisit the role of the linear classifier in the vanilla transfer learning framework, and then propose a new paradigm where the parameters of the classifier are initialized with semantic targets from the textual encoder and remain fixed during optimization. To provide a comparison, we also initialize the classifier with knowledge from various resources. In the empirical study, we demonstrate that our paradigm improves the performance and training speed of transfer learning tasks. With only minor modifications, our approach proves effective across 17 visual datasets that span three different data domains: image, video, and 3D point cloud.
Entanglement purification on quantum networks

We present an approach to entanglement purification on complex quantum network architectures, that is, how a quantum network built from repeaters with limited processing fidelity can purify and distribute entanglement between users. In particular, we explore how noisy gates, depolarizing channels, and finite memory storage time influence the performance of entanglement purification in a quantum network. Finally, we apply the purification techniques we developed in the context of entanglement routing for different quantum network topologies in an effort to inform future design choices for quantum network configurations.
Biometric Recognition Based on Recurrence Plot and InceptionV3 Model Using Eye Movements

The ability to use eye movement signals as a feature in biometric recognition is a novel characteristic of biometric recognition technology. However, present technologies have not fully exploited the correlation features between eye movement signals. To address this, we propose an eye movement biometric recognition model that is based on recurrence plot encoding and the InceptionV3 model. We first encode the original eye movement signal using the recurrence plot to obtain a 2-D image that is then used as input for the InceptionV3 model to perform biometric recognition. Our experimental results using the GazeBaseV2.0 eye movement dataset demonstrate that our proposed model achieved a high biometric recognition accuracy of 96.58% ± 0.66% using the recurrence plot transformation of the horizontal gaze position signals and the InceptionV3 model, surpassing the accuracy achieved by other models. The use of horizontal gaze position eye movement signals for biometric recognition outperforms the use of vertical gaze position signals when using our proposed methods. Furthermore, the biometric recognition that is achieved through recurrent plot encoding is superior to that achieved using Markov transition fields and Gramian angular field transformations.
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers

While pre-trained language models (PLMs) have become a de-facto standard promoting the accuracy of text classification tasks, recent studies find that PLMs often predict over-confidently.Although calibration methods have been proposed, such as ensemble learning and data augmentation, most of the methods have been verified in computer vision benchmarks rather than in PLM-based text classification tasks. In this paper, we present an empirical study on confidence calibration for PLMs, addressing three categories, including confidence penalty losses, data augmentations, and ensemble methods. We find that the ensemble model overfitted to the training set shows sub-par calibration performance and also observe that PLMs trained with confidence penalty loss have a trade-off between calibration and accuracy. Building on these observations, we propose the Calibrated PLM (CALL), a combination of calibration techniques. The CALL complements shortcomings that may occur when utilizing a calibration method individually and boosts both classification and calibration accuracy. Design choices in CALL’s training procedures are extensively studied, and we provide a detailed analysis of how calibration techniques affect the calibration performance of PLMs.
Scalable Prompt Generation for Semi-supervised Learning with Language Models

Prompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding (NLU) datasets and tasks in the literature. However, manually designing multiple prompts and verbalizers requires domain knowledge and human effort, making it difficult and expensive to scale across different datasets. In this paper, we propose two methods to automatically design multiple prompts and integrate automatic verbalizer in SSL settings without sacrificing performance. The first method uses various demonstration examples with learnable continuous prompt tokens to create diverse prompt models. The second method uses a varying number of soft prompt tokens to encourage language models to learn different prompts. For the verbalizer, we use the prototypical verbalizer to replace the manual one. In summary, we obtained the best average accuracy of 71.5% (a relative improvement of 0.99% over even the previous state-of-the-art SSL method with manual prompts and verbalizers) in different few-shot learning settings.
LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution

Current state-of-the-art coreference systems are based on a single pairwise scoring component, which assigns to each pair of mention spans a score reflecting their tendency to corefer to each other. We observe that different kinds of mention pairs require different information sources to assess their score. We present LingMess, a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly improves the accuracy of the pairwise scorer as well as of the overall coreference performance on the English Ontonotes coreference corpus and 5 additional datasets.
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information

Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Sungjin Lee, Devamanyu Hazarika, Mahdi Namazifar, Di Jin, Yang Liu, Dilek Hakkani-Tur. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages

Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Rahmad Mahendra, Fajri Koto, Ade Romadhony, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Pascale Fung, Timothy Baldwin, Jey Han Lau, Rico Sennrich, Sebastian Ruder. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Detecting intersectionality in NER models: A data-driven approach

Ida Marie S. Lassen, Mina Almasi, Kenneth Enevoldsen, Ross Deans Kristensen-McLachlan. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 2023.
Large Scale Multi-Lingual Multi-Modal Summarization Dataset

Significant developments in techniques such as encoder-decoder models have enabled us to represent information comprising multiple modalities. This information can further enhance many downstream tasks in the field of information retrieval and natural language processing; however, improvements in multi-modal techniques and their performance evaluation require large-scale multi-modal data which offers sufficient diversity. Multi-lingual modeling for a variety of tasks like multi-modal summarization, text generation, and translation leverages information derived from high-quality multi-lingual annotated data. In this work, we present the current largest multi-lingual multi-modal summarization dataset (M3LS), and it consists of over a million instances of document-image pairs along with a professionally annotated multi-modal summary for each pair. It is derived from news articles published by British Broadcasting Corporation(BBC) over a decade and spans 20 languages, targeting diversity across five language roots, it is also the largest summarization dataset for 13 languages and consists of cross-lingual summarization data for 2 languages. We formally define the multi-lingual multi-modal summarization task utilizing our dataset and report baseline scores from various state-of-the-art summarization techniques in a multi-lingual setting. We also compare it with many similar datasets to analyze the uniqueness and difficulty of M3LS. The dataset and code used in this work are made available at “https://github.com/anubhav-jangra/M3LS”.
Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College

Abstract: Research on data use and school Early Warning Systems (EWS) notes a central practice of researchers and practitioners is to search for patterns in student data to predict outcomes so schools can support success when students experience challenges. Yet, the domain lacks a means to visualize the rich longitudinal data that schools collect. Here, we use visual data analytic hierarchical cluster analysis (HCA) heatmaps to pattern and visualize entire longitudinal grading histories of a national sample of n =14,290 students from grade 9 to college in every enrolled subject and year, visualizing 6,728,920 individual datapoints. We provide both the open access code in R and an open-access online tool allowing anyone to upload their data and create a HCA heatmap, providing support for visual data analytic and data science practice for both education researchers and schooling organizations.
Saliency-Driven Hand Gesture Recognition Incorporating Histogram of Oriented Gradients (HOG) and Deep Learning

Hand gesture recognition is a vital means of communication to convey information between humans and machines. We propose a novel model for hand gesture recognition based on computer vision methods and compare results based on images with complex scenes. While extracting skin color information is an efficient method to determine hand regions, complicated image backgrounds adversely affect recognizing the exact area of the hand shape. Some valuable features like saliency maps, histogram of oriented gradients (HOG), Canny edge detection, and skin color help us maximize the accuracy of hand shape recognition. Considering these features, we proposed an efficient hand posture detection model that improves the test accuracy results to over 99% on the NUS Hand Posture Dataset II and more than 97% on the hand gesture dataset with different challenging backgrounds. In addition, we added noise to around 60% of our datasets. Replicating our experiment, we achieved more than 98% and nearly 97% accuracy on NUS and hand gesture datasets, respectively. Experiments illustrate that the saliency method with HOG has stable performance for a wide range of images with complex backgrounds having varied hand colors and sizes.
A Survey : Deep Learning Approaches for Signature Verification

Since a person's signature serves as the primary authentication and authorization method in legal transactions, there is a greater need than ever for effective auto-mated signature verification solutions. The fact that signatures are already recognized as a popular way of identity verification gives signature verification systems a significant edge over other types of technologies. The methods used to solve this problem and a signature verification system can be categorized into two categories: online and offline. An electronic tablet and pen that are connected to a computer are used in the online technique to extract information about a signature and collect dynamic data for verification purposes, such as pressure, velocity, and writing speed. Offline signature verification, on the other hand, employs signature images that have been recorded by a scanner or camera and involve less electronic management. Extracted features taken from the scanned signature image are used in an offline signature verification system. The main contribution of this study is how we can use deep learning approaches/networks for the task of offline signature verification systems.
Thresholds of external flows in financial development for environmental sustainability in sub-Saharan Africa

Purpose The study aims to complement extant literature by assessing linkages between financial development, external flows and CO 2 emissions in 27 sub-Saharan African countries for the period 2002 to 2018. Design/methodology/approach The empirical evidence is based on interactive quantile regressions and external flows consist of remittances, foreign aid, trade openness and foreign investment. Findings The findings show minimum levels of external flows that should be reached in order for the interaction between external flows and financial development to promote environmental sustainability in terms of reducing CO 2 emissions. The minimum thresholds are critical levels of external flows that should be reached before financial development promotes environmental sustainability. Research limitations/implications Policy implications – The disclosed external flow (i.e. FDI, foreign aid, trade and remittances) thresholds are actionable policy thresholds that the government can act upon in order to influence environmental sustainability by means of financial development. Theoretical implications – The findings below the external flow thresholds are consistent with the dependency theory in that external flows are harmful to socio-economic progress and environmental sustainability. When external flows are consolidated to the established critical masses or thresholds in the long run, the corresponding findings are in line with the extant neoclassical and endogenous growth theories, not least, because in the long run, external flows are associated with technological progress and adoption of stronger environmental legislation at the domestic level which are worthwhile in promoting environmental performance. Practical implications To reach the minimum trade and FDI levels that are worthwhile for the promotion of environmental sustainability, corporations should set targets on exports and imports as well as foreign investment levels that they have to attain in contributing to the national target of external flows needed to reduce CO 2 emissions. Such trade and FDI targets should be set in industries of various economic sectors. Originality/value The study complements the extant literature by assessing how external flows interact with financial development to influence CO 2 emissions.