Reviewerly/exHarmony huggingface dataset:
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation

In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study.We assume a strict setting with no access to parallel data or machine translation and find that common transfer learning approaches struggle in this setting, as a generative multilingual model fine-tuned purely on English catastrophically forgets how to generate non-English.Given the recent rise of parameter-efficient adaptation techniques, we conduct the first investigation into how one such method, prompt tuning (Lester et al., 2021), can overcome catastrophic forgetting to enable zero-shot cross-lingual generation.Our experiments show that parameter-efficient prompt tuning provides gains over standard fine-tuning when transferring between lessrelated languages, e.g., from English to Thai.However, a significant gap still remains between these methods and fully-supervised baselines.To improve cross-lingual transfer further, we explore several approaches, including: (1) mixing in unlabeled multilingual data, and (2) explicitly factoring prompts into recombinable language and task components.Our approaches can provide further quality gains, suggesting that robust zero-shot crosslingual generation is within reach.
Control Prefixes for Parameter-Efficient Text Generation

Prefix-tuning is a parameter-efficient and powerful technique for adapting a pre-trained language model to a downstream application.However, it uses the same dataset-level tuned set of parameters for all examples in the dataset.We extend the framework with a dynamic method, CONTROL PREFIXES, which allows for the effective inclusion of input-dependent information, thereby demonstrating how prefixtuning can be used for controlled text generation tasks.The method incorporates attributelevel learnable representations into different layers of a pre-trained Transformer, enabling the generated text to be guided in a particular direction.We provide a systematic evaluation of the technique and apply it to five datasets from the GEM benchmark for natural language generation (NLG).Using only 0.1-2% additional trainable parameters, we show CON-TROL PREFIXES can even outperform full finetuning methods, and present state-of-the-art results on several data-to-text datasets, including WebNLG.We also examine the common case where input-dependent information is unavailable at test time and show CONTROL PREFIXES can excel in this setting also. Prefix-tuning Single Task Batch Control PrefixesControl Prefixes (70k -400k params each) 1 Pre-trained Model (0.4B params)i) guidance
Prompt Consistency for Zero-Shot Task Generalization

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zeroshot setting.To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the predicted output.Nonetheless, the performance in such settings often lags far behind its supervised counterpart, suggesting a large space for potential improvement.In this paper, we explore methods to utilize unlabeled data to improve zero-shot performance.Specifically, we take advantage of the fact that multiple prompts can be used to specify a single task, and propose to regularize prompt consistency, encouraging consistent predictions over this diverse set of prompts.Our method makes it possible to fine-tune the model either with extra unlabeled training data, or directly on test input at inference time in an unsupervised manner.In experiments, our approach outperforms the state-of-the-art zeroshot learner, T0 (Sanh et al., 2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points in terms of accuracy.The gains are often attained with a small number of unlabeled examples. 1
Comparison of Feature Extraction with PCA and LTP Methods and Investigating the Effect of Dimensionality Reduction in the Bat Algorithm for Face Recognition

Face recognition is one of the challenging subjects of image processing. Facial recognition is often a biometric method that basically uses faces to recognize people. The face recognition system consists of three main steps: finding the face in the image, feature extraction and classification. The face recognition system faces challenges such as changes in lighting, changes in age, changes in facial expressions, etc. One of the important issues in this system is the algorithm execution speed. For this purpose, the dimensions of the feature vectors should be small enough, especially when the database is large. Since the face recognition system must be performed on a wide range of databases, dimensionality reduction techniques are required to reduce time and increase accuracy. Dimension reduction methods are used for this purpose. Two methods of dimensionality reduction, including LTP and PCA, are given in this research. In this research, first, the LTP feature vectors are extracted from the face image, and then the effective features are selected using the Bat algorithm. Therefore, this algorithm has three main phases of feature extraction, feature selection and classification. This algorithm is implemented on the ORL database, which contains 400 images of 40 different people with a size of 112×92 pixels. In addition to reducing the time required for testing, the proposed method has provided a very good accuracy of 99%.
Libraries in the metaverse: the need for metaliteracy for digital librarians and digital age library users

Purpose As the metaverse gains popularity, libraries have the potential to play a vital role in this virtual world. However, digital librarians and digital age library users need to develop metaliteracy skills to effectively navigate and evaluate digital information in the metaverse. The main purpose of this paper is to explore the link between libraries and the metaverse, define metaliteracy and highlight its importance for librarians and library users. The challenges of developing metaliteracy skills in the metaverse are discussed, as well as the need for ongoing training and support. This paper also explores the role of libraries and librarians in the metaverse and provides recommendations for enhancing metaliteracy skills. Design/methodology/approach Through a review of the literature, this paper analyzes various library websites and consulting literature relating to the link between libraries and the metaverse, metaliteracy and its importance for librarians and library users and the challenges of developing metaliteracy skills in the metaverse. Findings As the metaverse continues to evolve, libraries and librarians must adapt and develop the necessary skills to continue providing valuable resources and services to their communities in virtual environments. Originality/value To the best of authors’ knowledge, this paper is the original idea that highlights the importance of metaliteracy for librarians and library users. The challenges of developing metaliteracy skills in the metaverse.
The Impact of Artificial Intelligence on Chatbot Technology: A Study on the Current Advancements and Leading Innovations

Artificial intelligence (AI) has had a profound impact on various industries, and one prominent domain where its influence is evident is in chatbot technology. Chatbots, computer programs designed to simulate human conversation, have evolved significantly through the advancements in AI, becoming more sophisticated and intelligent. This research paper aims to explore the current state of AI-powered chatbot technology, focusing on the latest advancements and leading innovations. The study delves into the application of natural language processing (NLP) algorithms, machine learning models, and deep learning techniques in chatbot development to gain insights into their capabilities and limitations. The research also highlights leading innovations in AI-powered chatbot technology, such as virtual assistants and voice-enabled chatbots. These conversational agents have transformed various industries, providing innovative solutions to virtual reference services and customer-company interactions. The study delves into the contextual understanding and personalized responses that chatbots can provide, offering tailored interactions to meet users' specific needs and preferences. Furthermore, the integration of other technologies, including speech recognition and sentiment analysis, enhances chatbot capabilities, improving user satisfaction and engagement. However, while AI-powered chatbots have enhanced user experiences, customer satisfaction, and efficiency in industries like customer support and service, they also raise potential ethical and privacy concerns. Medical chatbots, in particular, pose legal and ethical challenges that require careful management and the development of appropriate ethical frameworks. Understanding the advancements, innovations, and impact of AI on chatbot technology is essential for recognizing the potential benefits and challenges these systems present. By addressing ethical and privacy concerns, chatbots can responsibly shape the future of human-computer interactions, further contributing to the broader understanding of AI's role in transforming industries and enhancing user experiences.
Data-driven predictions of complex organic mixture permeation in polymer membranes

Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic molecules such as crude oil. Determining the separation performance of a polymer membrane when challenged with a complex mixture has thus far occurred in an ad hoc manner, and methods to predict the performance based on mixture composition and polymer chemistry are unavailable. Here, we combine physics-informed machine learning algorithms (ML) and mass transport simulations to create an integrated predictive model for the separation of complex mixtures containing up to 400 components via any arbitrary linear polymer membrane. We experimentally demonstrate the effectiveness of the model by predicting the separation of two crude oils within 6-7% of the measurements. Integration of ML predictors of diffusion and sorption properties of molecules with transport simulators enables for the rapid screening of polymer membranes prior to physical experimentation for the separation of complex liquid mixtures.
Blockchain and Machine Learning-Based Hybrid IDS to Protect Smart Networks and Preserve Privacy

The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide a medium for expression and communication. Malware, phishing, ransomware, and distributed denial-of-service attacks pose a threat to individuals and organisations. To detect and predict cyber threats effectively and accurately, an intelligent system must be developed. Cybercriminals can exploit Internet of Things devices and endpoints because they are not intelligent and have limited resources. A hybrid decision tree method (HIDT) is proposed in this article that integrates machine learning with blockchain concepts for anomaly detection. In all datasets, the proposed system (HIDT) predicts attacks in the shortest amount of time and has the highest attack detection accuracy (99.95% for the KD99 dataset and 99.72% for the UNBS-NB 15 dataset). To ensure validity, the binary classification test results are compared to those of earlier studies. The HIDT’s confusion matrix contrasts with previous models by having low FP/FN rates and high TP/TN rates. By detecting malicious nodes instantly, the proposed system reduces routing overhead and has a lower end-to-end delay. Malicious nodes are detected instantly in the network within a short period. Increasing the number of nodes leads to a higher throughput, with the highest throughput measured at 50 nodes. The proposed system performed well in terms of the packet delivery ratio, end-to-end delay, robustness, and scalability, demonstrating the effectiveness of the proposed system. Data can be protected from malicious threats with this system, which can be used by governments and businesses to improve security and resilience.
Enhancing Digital Capacity for Students at Higher Education Institutions Under the Ministry of Home Affairs in the Context of Digital Transformation

Purpose: The article is conducted to assess the digital competence of students, survey students at higher education institutions under the Ministry of Home Affairs. From there, propose solutions to improve the digital capacity of students at higher education institutions under the Ministry of Home Affairs in the coming time. Theoretical framework: The article is made on the basis of synthesizing and developing the theoretical basis on the context of digital transformation, digital capacity of students at higher education institutions. Methods: Secondary document research method is used to collect, analyze and exploit information from available sources related to the research topic, including the views of the Party, legal documents. gorvernment's; documents directing the implementation of public higher education institutions, research works, reports and statistics directly or indirectly related to the digital competence of students at higher education institutions learn. The research team conducted a survey of 113 cadres, lecturers and 619 students in the form of purposeful random sampling to analyze the digital competencies of students at higher education institutions under the Ministry of Home Affairs. Results and conclusions: Research results show that there are 4 factors affecting students' digital transformation capacity, including information competence, communication capacity, digital security capacity and innovation capacity. Creativity has a positive and meaningful influence on research. Finding implications: The research results are also a reference for administrators, lecturers and students in improving the digital transformation capacity of students at higher education institutions under the Ministry of Home Affairs in the future. next time. Uniqueness/value: The article contributes to providing scientific and practical arguments for management levels and advisory agencies to develop regulations related to digital competence for students at educational institutions. university in the context of digital transformation. The article contributes to giving some viewpoints and solutions to improve the digital capacity of students at higher education institutions under the Ministry of Home Affairs (National Academy of Public Administration).
URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation

This work aims to estimate a high-quality depth map from a single RGB image. Due to the lack of depth clues, making full use of the long-range correlation and local information is critical for accurate depth estimation. To this end, we introduce an uncertainty rectified cross-distillation between the Transformer and convolutional neural network (CNN) to achieve a comprehensive depth estimator. Specifically, we utilize the depth estimates from the Transformer branch and CNN branch as pseudo labels to teach each other. At the same time, the pixel-wise depth uncertainty is modeled to mitigate the negative impact of noisy pseudo labels. To avoid the large capacity gap induced by the strong Transformer branch deteriorating the cross-distillation, we transfer the feature maps from the Transformer to the CNN and develop coupling units to assist the weak CNN branch in leveraging the transferred features. Furthermore, we introduce CutFlip, a surprisingly simple yet highly effective data augmentation technique, which forces the model to focus on more valuable depth reasoning clues apart from the vertical image position. Extensive experiments demonstrate that our model, termed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">URCDC-Depth</b> , exceeds in performance previous state-of-the-art approaches on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets, with no additional computational burden in the evaluation phase. The source code will be publicly available upon acceptance.
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.
Metaverse: open possibilities

To create a completely immersive, highly spatiotemporal, and self-sustaining virtual shared place for humans to play, work, and interact, the Metaverse is an emerging paradigm of the next-generation Internet. Metaverse is transitioning from science fiction to a near-future reality thanks to recent developments in new technologies like extended reality, artificial intelligence, and blockchain.In this study, we investigate the Metaverse, a shared, fully immersive virtual environment that could serve as a model for the Internet’s future generation. Safety, education, games, entertainment, social effects, the economy, ethics, and future issues are just few of the many areas covered. The Metaverse’s innovative tools for communication and collaboration may have a profound impact on people’s daily lives and the way society operates as a whole. However, there are obstacles that must be addressed before it can be implemented. If the Metaverse is accepted, a far different digital future is possible.
Quantum computing for fluids: Where do we stand?

Abstract We present a pedagogical introduction to the current state of quantum computing algorithms for the simulation of classical fluids. Different strategies, along with their potential merits and liabilities, are discussed and commented on.
EANTrack: An Efficient Attention Network for Visual Tracking

Recently, Siamese trackers have gained widespread attention in visual tracking due to their exceptional performance. However, many trackers still suffer from limitations in challenging scenarios, such as fast motion and scale variation, which hinder the full exploitation of target features. Consequently, the accuracy and efficiency of the trackers are limited. Therefore, this paper proposes an efficient attention network, called EAN, to improve tracking performance. The EAN comprises three primary components, namely a Transformer-s subnetwork, a Transformer-t subnetwork, and a Feature-Fused Attention Module (FFAM). The designed Transformer-s and Transformer-t subnetworks adopt complementary structures and functions to fully integrate and emphasize the relevant feature information, including channel and spatial features. The FFAM is responsible for fusing the multi-level features from both subnetworks, which establishes the global dependencies between the templates and search regions and enhances the discriminative power of the model. To further improve the tracking accuracy, a novel Feature-Aware Attention Module (FAAM) is introduced into the tracking prediction head to enhance the feature representation capability of the model. Finally, we propose an efficient EANTrack tracker based on EAN for robust tracking in complex scenarios, which exhibits significant advantages in challenging attributes. Experimental results on multiple benchmarks indicate that our approach achieves remarkable tracking performance with a real-time running speed of 55.6fps. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Siamese trackers have garnered considerable attention in the field of visual tracking due to their impressive performance. However, these trackers often face limitations in challenging scenarios, which impede the complete exploitation of target features. As a result, the accuracy and efficiency of many trackers are compromised. To address these issues, we propose an efficient tracker called EANTrack to enable robust tracking in complex scenarios. Our EANTrack exhibits significant advantages in handling challenging attributes. Please refer to our complete paper for detailed information on the EANTrack tracker and experimental results. Practitioners in the field can benefit from our research by leveraging our findings and methodologies in their work. We encourage further exploration and experimentation to enhance the performance and applicability of visual tracking systems.