Reviewerly/exHarmony huggingface dataset:
Skeleton MixFormer: Multivariate Topology Representation for Skeleton-based Action Recognition

Vision Transformer, which performs well in various vision tasks, encounters a bottleneck in skeleton-based action recognition and falls short of advanced GCN-based methods. The root cause is that the current skeleton transformer depends on the self-attention mechanism of the complete channel of the global joint, ignoring the highly discriminative differential correlation within the channel, so it is challenging to learn the expression of the multivariate topology dynamically. To tackle this, we present Skeleton MixFormer, an innovative spatio-temporal architecture to effectively represent the physical correlations and temporal interactivity of the compact skeleton data. Two essential components make up the proposed framework: 1) Spatial MixFormer. The channel-grouping and mix-attention are utilized to calculate the dynamic multivariate topological relationships. Compared with the full-channel self-attention method, Spatial MixFormer better highlights the channel groups' discriminative differences and the joint adjacency's interpretable learning. 2) Temporal MixFormer, which consists of Multiscale Convolution, Temporal Transformer and Sequential Holding Module. The multivariate temporal models ensure the richness of global difference expression and realize the discrimination of crucial intervals in the sequence, thereby enabling more effective learning of long and short-term dependencies in actions. Our Skeleton MixFormer demonstrates state-of-the-art (SOTA) performance across seven different settings on four standard datasets, namely NTU-60, NTU-120, NW-UCLA, and UAV-Human. Related code will be available on https://github.com/ElricXin/Skeleton-MixFormer.
INSTITUTIONAL POLICIES ON ARTIFICIAL INTELLIGENCE IN UNIVERSITY LEARNING, TEACHING AND RESEARCH

Generative artificial intelligence is rapidly transforming the educational process and the scientific work of students, lecturers, researchers and administrators of higher education institutions. There are limited and contradictory recommendations for the use of artificial intelligence in the educational process and in the educational programmes of higher education institutions. To define the scope of AI application, there is a need to develop institutional policies for higher education institutions, which will allow the academic community to determine the appropriate scope of AI application in the educational process and prevent the use of AI in areas where ethical norms are violated. The purpose of the article is to summarise and systematise the experience of forming institutional policies for the application of artificial intelligence in education, training and research in higher education institutions using the experience of Kherson State University. The article presents the developed institutional policies for students, lecturers and researchers with recommended and not recommended initiatives for the use of AI in the educational process and research at Kherson State University. The purpose of these institutional policies was to create an inclusive environment of modern digital tools for involving of lecturers of non-professional specialties and define the framework for the use of artificial intelligence in education, teaching, and research of all subjects (participants) of educational activity. The recommendations are aimed at empowering all participants to select educational components creatively and at improving the efficiency of the educational process and research activities through the use of AI tools. Issues requiring further research regarding generative platforms lie in three thematic areas: knowledge base; transparency and ethics; digital transformation of organizations and societies. It is also relevant to create an educational environment that encourages the interaction and dissemination of positive practice of digital transformation of all participants of the educational process.
Irregular Tensor Representation for Superpixel- Guided Hyperspectral Image Denoising

Recently, due to the ability of exploiting perceptual information, superpixel-based methods have received attention for hyperspectral image (HSI) denoising. However, existing superpixel-based denoising methods unfold the irregular 3D superpixels into the matrices along the spectral mode, which inevitably destroys the intrinsic structure of the irregular 3D superpixels. To tackle the irregular 3D superpixels, we introduce the irregular tensor representation for superpixels-guided HSI denoising. More concretely, by introducing the weighted tensor, we suggest a tensor representation of each irregular 3D superpixel, which can preserve the intrinsic structure of the irregular 3D superpixel. Equipped with the irregular tensor representation, we establish a superpixel-guided tensor optimization model for HSI denoising, which simultaneously exploits the perceptual information and low-rank structure within 3D superpixels. To solve the resulting tensor optimization problem, we develop an inexact augmented Lagrange multiplier (IALM) algorithm. Experimental results show that the proposed method outperforms other state-of-the-art HSI denoising methods, particularly matrix-based methods, on both simulated and real data.
Knowledge representation and reasoning (KRR)

Knowledge representation and reasoning (KRR) is a key research area in artificial intelligence that deals with the design and development of methods to represent, manipulate, and reason with knowledge in computer systems. KRR techniques are used to develop intelligent systems that can reason about complex domains, make decisions, and provide explanations for their actions. The present research aims to understand the process of representing knowledge and reasoning with different domains, including agriculture, education, healthcare, and business. Different techniques, such as ontologies, semantic networks, frames, rule-based systems, description logics, first-order logic, and Bayesian networks, have been developed to facilitate the representation and reasoning of knowledge. In the present research, different techniques for representing and reasoning with knowledge are used in two different domains: academic knowledge and farmer knowledge. The study examines the strengths and weaknesses of each technique and provides insights into which techniques are most suitable for different types of KRR tasks. The results of this study can help practitioners and researchers to choose the most appropriate technique for their specific KRR needs.
Interpretable Machine Learning for Discovery: Statistical Challenges and Opportunities

New technologies have led to vast troves of large and complex data sets across many scientific domains and industries. People routinely use machine learning techniques not only to process, visualize, and make predictions from these big data, but also to make data-driven discoveries. These discoveries are often made using interpretable machine learning, or machine learning models and techniques that yield human-understandable insights. In this article, we discuss and review the field of interpretable machine learning, focusing especially on the techniques, as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using interpretable machine learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation both from a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven discoveries. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Towards Cognitive Interoperability in Cyber-Physical Enterprises

This paper is about Cognitive Interoperability of Cyber-Physical Systems and Humans in an enterprise context where both are expected to have the capabilities to work collaboratively. We review the relevant state of the art, highlighting related concepts, propose a definition for Cognitive Interoperability in Cyber-Physical Enterprise context and list characteristics of Cognitive Systems, towards a Cognitive System-of-Systems.
‘What are you referring to?’ Evaluating the Ability of Multi-Modal Dialogue Models to Process Clarificational Exchanges

Referential ambiguities arise in dialogue when a referring expression does not uniquely identify the intended referent for the addressee. Addressees usually detect such ambiguities immediately and work with the speaker to repair it using meta-communicative, Clarificational Exchanges (CE): a Clarification Request (CR) and a response. Here, we argue that the ability to generate and respond to CRs imposes specific constraints on the architecture and objective functions of multi-modal, visually grounded dialogue models. We use the SIMMC 2.0 dataset to evaluate the ability of different state-of-the-art model architectures to process CEs, with a metric that probes the contextual updates that arise from them in the model. We find that language-based models are able to encode simple multi-modal semantic information and process some CEs, excelling with those related to the dialogue history, whilst multi-modal models can use additional learning objectives to obtain disentangled object representations, which become crucial to handle complex referential ambiguities across modalities overall.
Scalable Quantum Error Correction for Surface Codes Using FPGA

A fault-tolerant quantum computer must decode and correct errors faster than they appear. The faster errors can be corrected, the more time the computer can do useful work. The Union-Find (UF) decoder is promising with an average time complexity slightly higher than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(d^{3}$</tex> . We report a distributed version of the UF decoder that exploits parallel computing resources for further speedup. Using an FPGA-based implementation, we empirically show that this distributed UF decoder has <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> sublinear average time complexity with regard to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> given <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(d^{3}$</tex> parallel computing resources. The decoding time per measurement round decreases as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> increases, a first time for a quantum error decoder. The implementation employs a scalable architecture called Helios that organizes parallel computing resources into a hybrid tree-grid structure. We are able to implement <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> up to 21 with a Xilinx VCU129 FPGA, for which an average decoding time is 11.5 ns per measurement round under phenomenological noise of 0.1 %, significantly faster than any existing decoder implementation. Since the decoding time per measurement round of Helios decreases with d, Helios can decode a surface code of arbitrarily large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> without a growing backlog.
DeepInfer: Deep Type Inference from Smart Contract Bytecode

Smart contracts play an increasingly important role in Ethereum platform. It provides various functions implementing numerous services, whose bytecode runs on Ethereum Virtual Machine. To use services by invoking corresponding functions, the callers need to know the function signatures. Moreover, such signatures provide crucial information for many downstream applications, e.g., identifying smart contracts, fuzzing, detecting vulnerabilities, etc. However, it is challenging to infer function signatures from the bytecode due to a lack of type information. Existing work solving this problem depended heavily on limited databases or hard-coded heuristic patterns. However, these approaches are hard to be adapted to semantic differences in distinct languages and various compiler versions when developing smart contracts. In this paper, we propose a novel framework DeepInfer that first leverages deep learning techniques to automatically infer function signatures and returns. The novelties of DeepInfer are: 1) DeepInfer lifts the bytecode into the Intermediate Representation (IR) to preserve code semantics; 2) DeepInfer extracts the type-related knowledge (e.g., critical data flows, constant values, and control flow graphs) from the IR to recover function signatures and returns. We conduct experiments on Solidity and Vyper smart contracts and the results show that DeepInfer performs faster and more accurate than existing tools, while being immune to changes in different languages and various compiler versions.
Generative AI and Marketing Education: What the Future Holds

To understand why and how marketing educators can best use generative artificial intelligence (AI), such as ChatGPT, this article integrates a literature survey, interviews with both marketing educators and managers, and surveys of both marketing educators and students. In leveraging these inputs, the authors argue that generative AI can significantly shape and improve the future of marketing education. Specifically, by including ChatGPT in their lessons, marketing educators can both materially enhance learning experiences and better prepare students for future jobs with marketing firms that rely on ChatGPT in practice. Noting that ChatGPT has downsides, this research identifies several steps educators should take to minimize the risks. Finally, the authors propose an agenda for continued research into how marketing educators can and should use ChatGPT, with the explicit recognition that ChatGPT is evolving rapidly, so that, the research agenda will need to adapt as well.
Mastering Strategy Card Game (Hearthstone) with Improved Techniques

Strategy card game is a well-known genre that is demanding on the intelligent game-play and can be an ideal test-bench for AI. Previous work combines an end-to-end policy function and an optimistic smooth fictitious play, which shows promising performances on the strategy card game Legend of Code and Magic. In this work, we apply such algorithms to Hearthstone, a famous commercial game that is more complicated in game rules and mechanisms. We further propose several improved techniques and consequently achieve significant progress. For a machine-vs-human test we invite a Hearthstone streamer whose best rank was top 10 of the official league in China region that is estimated to be of millions of players. Our models defeat the human player in all Best-of-5 tournaments of full games (including both deck building and battle), showing a strong capability of decision making.
Improving the performance of twin-field quantum key distribution with advantage distillation technology

In this work, we apply the advantage distillation method to improve the performance of a practical twin-field quantum key distribution system under collective attack. Compared with the previous analysis result given by Maeda, Sasaki and Koashi [Nature Communication 10, 3140 (2019)], the maximal transmission distance obtained by our analysis method will be increased from 420 km to 470 km. By increasing the loss-independent misalignment error to 12%, the previous analysis method can not overcome the rate-distance bound. However, our analysis method can still overcome the rate-distance bound when the misalignment error is 16%. More surprisingly, we prove that twin-field quantum key distribution can generate positive secure key even if the misalignment error is close to 50%, thus our analysis method can significantly improve the performance of a practical twin-field quantum key distribution system.
Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) has emerged as a critical facet in the realm of machine learning and artificial intelligence, responding to the increasing complexity of models, particularly deep neural networks, and the subsequent need for transparent decisionmaking processes.
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such data, although straightforward, stands a great chance of leading to improved performance. This paper aims to push the upper bound of open-source models further. We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat, which does not involve human queries. Our objective is to capture the breadth of interactions between a human user and an AI assistant and employs a comprehensive framework to generate multi-turn conversation iteratively. UltraChat contains 1.5 million high-quality multi-turn dialogues and covers a wide range of topics and instructions. Our statistical analysis of UltraChat reveals its superiority in various key metrics, including scale, average length, diversity, coherence, etc., solidifying its position as a leading open-source dataset. Building upon UltraChat, we fine-tune a LLaMA model to create a powerful conversational model, UltraLM. Our evaluations indicate that UltraLM consistently outperforms other open-source models, including WizardLM and Vicuna, the previously recognized state-of-the-art open-source models.
Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*

To advance the neural encoding of Portuguese (PT), and a fortiori the technological preparation of this language for the digital age, we developed a Transformer-based foundation model that sets a new state of the art in this respect for two of its variants, namely European Portuguese from Portugal (PT-PT) and American Portuguese from Brazil (PT-BR). To develop this encoder, which we named Albertina PT-*, a strong model was used as a starting point, DeBERTa, and its pre-training was done over data sets of Portuguese, namely over a data set we gathered for PT-PT and over the brWaC corpus for PT-BR. The performance of Albertina and competing models was assessed by evaluating them on prominent downstream language processing tasks adapted for Portuguese. Both Albertina versions are distributed free of charge and under a most permissive license possible and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.
Non-Fungible Tokens: A Literature Review

Non-Fungible Tokens (NFTs) was a popular crypto asset in the year 2021-2022, where viral news involving a NFT with huge price being sold causes a big interest to NFTs and OpenSea, one of the biggest NFT market place, and creating supply and demand for buying and selling NFTs, especially people who are only aware of cryptocurrency becoming the one who buys cryptocurrency and crypto assets. Although NFTs has since lost its initial exposure, the literatures of NFTs remains scarce, and the research involving NFTs has since been lower. This research is aimed to reviews literatures involving NFTs, using literature review method with „NFTs“ or „Non-Fungible Tokens“ as keywords to search for relevant literatures and research articles. The result of the research gave a definitive definition of NFTs compiled from many authors, the method people use to create NFTs and ways to invest NFTs safely, opportunities and risks in investing NFTs and the technological aspect, and what the future has for NFTs, investing and technological wise.
Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos’ digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.
A hybrid algorithm of grey wolf optimizer and harris hawks optimization for solving global optimization problems with improved convergence performance

Abstract The grey wolf optimizer is an effective and well-known meta-heuristic algorithm, but it also has the weaknesses of insufficient population diversity, falling into local optimal solutions easily, and unsatisfactory convergence speed. Therefore, we propose a hybrid grey wolf optimizer (HGWO), based mainly on the exploitation phase of the harris hawk optimization. It also includes population initialization with Latin hypercube sampling, a nonlinear convergence factor with local perturbations, some extended exploration strategies. In HGWO, the grey wolves can have harris hawks-like flight capabilities during position updates, which greatly expands the search range and improves global searchability. By incorporating a greedy algorithm, grey wolves will relocate only if the new location is superior to the current one. This paper assesses the performance of the hybrid grey wolf optimizer (HGWO) by comparing it with other heuristic algorithms and enhanced schemes of the grey wolf optimizer. The evaluation is conducted using 23 classical benchmark test functions and CEC2020. The experimental results reveal that the HGWO algorithm performs well in terms of its global exploration ability, local exploitation ability, convergence speed, and convergence accuracy. Additionally, the enhanced algorithm demonstrates considerable advantages in solving engineering problems, thus substantiating its effectiveness and applicability.