Reviewerly/exHarmony huggingface dataset:
Biased Target-tree <sup>*</sup> Algorithm with RRT <sup>*</sup> for Reducing Parking Path Planning Time

The target-tree <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, which is a variant of the optimal rapidly-exploring random tree (RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) has been proposed to reduce the parking path planning time. This algorithm pre-generates a set of backward paths (target-tree) around a parking spot and extends an RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> from the initial pose until it is connected to a random sample of the target-tree. However, it is difficult to obtain the shortest (optimal) parking path within a short planning time because connected samples between the tree and the target-tree are randomly searched. To deal with this problem, this paper proposes a biased target-tree <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm with RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> that searches connected random samples in a biased range near the target-tree. This range has a Gaussian distribution centered on the optimal connected sample where the shortest parking path can be obtained quickly and is obtained through supervised learning. In actual parking situations, the biased target-tree <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm obtained a shorter path with less length deviation than the original target-tree <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm within a shorter planning time.
Increasing the Robustness of DES and TDEA Against the Differential Cryptoattack

The study of DES (Data Encryption Standard) algorithm reveals some vulnerabilities caused by the permutation and substitution functions used by it. These vulnerabilities decreases the robustness of DES against differential cryptoattack and TDEA (Triple Data Encryption Algorithm) inherits them. We analyze the permutation vectors and the S-boxes of DES over algebraic finite fields, using polynomials, and propose using more performant functions and vectors such as Costas arrays which can be almost perfect non-linear permutations, increasing the nonlinearity and the strength of these encryption algorithms.
XGBoost with Q-learning for complex data processing in business logistics management

The modern business landscape is characterized by complex technical information, economic globalization, and high customer expectations. These factors have led to significant changes in various industries. To ensure customer satisfaction, companies rely on supply chain management (SCM) for the timely delivery of products and gathering feedback for analysis. The collected customer data is often complex and requires advanced methods for processing and management. To effectively manage demand and supply in real-time, businesses must have the ability to handle complex data. Due to the inefficiency and ineffectiveness of traditional methods for the increased data volume and speed, much-emerging research is being conducted on how to harness complex data in SCM. This paper examines the limitations of conventional methods and introduces an Artificial Intelligence (AI) approach based on Q-Learning algorithm with Extreme Gradient Boosting (QL-XGB) model. The QL-XGB method is applied to select suppliers and predict their future demand for the production of products. It is built on the foundation of accurate data and analysis of supply chain characteristics using metrics such as MAE and RMSE. The results show that the QL-XGB model with an accuracy rate of 96.02% outperforms QL and XGB models with respective accuracy rates of 93.44% and 94.68%.
ROLE OF DIGITAL CURRENCY AND BLOCK CHAIN TECHNOLOGY ON INDIAN ECONOMY WITH SPECIAL REFERENCE TO RBI AND GOVERNMENT RECENT REFORMS IN DIGITAL RUPEE

India has many in need of help. It has approximately 1.4 billion people, making it the second most populous country in the world. This makes it the world's second-largest country with a relatively lowdensity density population. Unfortunately, it is second only to China in income disparity, where over 43 percent of the population makes below the national poverty line. The Center for Internet &amp; Society at Stanford University notes that India's child sex ratio (1,006 females per 1,000 males) is the worst in the world; and, in 2008, the nation had a literacy rate of only 74 percent, which is also the lowest in the world. It also says that half the Indians are expected to live below the poverty line by 2020.
An improved particle swarm optimization combined with double-chaos search

&lt;abstract&gt; &lt;p&gt;Particle swarm optimization (PSO) has been successfully applied to various complex optimization problems due to its simplicity and efficiency. However, the update strategy of the standard PSO algorithm is to learn from the global best particle, making it difficult to maintain diversity in the population and prone to premature convergence due to being trapped in local optima. Chaos search mechanism is an optimization technique based on chaotic dynamics, which utilizes the randomness and nonlinearity of a chaotic system for global search and can escape from local optima. To overcome the limitations of PSO, an improved particle swarm optimization combined with double-chaos search (DCS-PSO) is proposed in this paper. In DCS-PSO, we first introduce double-chaos search mechanism to narrow the search space, which enables PSO to focus on the neighborhood of the optimal solution and reduces the probability that the swarm gets trapped into a local optimum. Second, to enhance the population diversity, the logistic map is employed to perform a global search in the narrowed search space and the best solution found by both the logistic and population search guides the population to converge. Experimental results show that DCS-PSO can effectively narrow the search space and has better convergence accuracy and speed in most cases.&lt;/p&gt; &lt;/abstract&gt;
Argumentative Reasoning in ASPIC+ under Incomplete Information

Reasoning under incomplete information is an important research direction in AI argumentation. Most computational advances in this direction have so-far focused on abstract argumentation frameworks. Development of computational approaches to reasoning under incomplete information in structured formalisms remains to-date to a large extent a challenge. We address this challenge by studying the so-called stability and relevance problems---with the aim of analyzing aspects of resilience of acceptance statuses in light of new information---in the central structured formalism of ASPIC+. Focusing on the case of the grounded semantics and an ASPIC+ fragment motivated through application scenarios, we develop exact ASP-based algorithms for stability and relevance in incomplete ASPIC+ theories, and pinpoint the complexity of reasoning about stability (coNP-complete) and relevance (Sigma_2^P-complete), further justifying our ASP-based approaches. Empirically, the algorithms exhibit promising scalability, outperforming even a recent inexact approach to stability, with our ASP-based iterative approach being the first algorithm proposed for reasoning about relevance in ASPIC+.
Blockchain in Healthcare Systems

This chapter discusses blockchain healthcare applications, difficulties, and potential. It suggests using blockchain, machine learning, artificial intelligence, big data, and the internet of things to improve healthcare by addressing complex intermediary networks and transaction traceability, data fragmentation, inefficient research and services, lack of clinical trial reporting, expensive drug supply chain costs, patient data security, and bogus pharmaceuticals. It also identifies essential success criteria for blockchain implementation in healthcare systems. It prioritizes chronic fatigue syndromes using a fuzzy approach to order preference by similarity to an ideal solution like technique for order of preference by similarity to an ideal solution. Blockchain knowledge offers a decentralized, reformed environment, providing immediate security and reliability.
MC-YOLOv5: A Multi-Class Small Object Detection Algorithm

The detection of multi-class small objects poses a significant challenge in the field of computer vision. While the original YOLOv5 algorithm is more suited for detecting full-scale objects, it may not perform optimally for this specific task. To address this issue, we proposed MC-YOLOv5, an algorithm specifically designed for multi-class small object detection. Our approach incorporates three key innovations: (1) the application of an improved CB module during feature extraction to capture edge information that may be less apparent in small objects, thereby enhancing detection precision; (2) the introduction of a new shallow network optimization strategy (SNO) to expand the receptive field of convolutional layers and reduce missed detections in dense small object scenarios; and (3) the utilization of an anchor frame-based decoupled head to expedite training and improve overall efficiency. Extensive evaluations on VisDrone2019, Tinyperson, and RSOD datasets demonstrate the feasibility of MC-YOLOv5 in detecting multi-class small objects. Taking VisDrone2019 dataset as an example, our algorithm outperforms the original YOLOv5L with improvements observed across various metrics: mAP50 increased by 8.2%, mAP50-95 improved by 5.3%, F1 score increased by 7%, inference time accelerated by 1.8 ms, and computational requirements reduced by 35.3%. Similar performance gains were also achieved on other datasets. Overall, our findings validate MC-YOLOv5 as a viable solution for accurate multi-class small object detection.
Application of Certification Management Information Systems at LSP Engineering Hospitality Indonesia

One of the problems that exist in the Hospitality Engineering Indonesia Professional Certification Institute is in terms of administration management which still uses conventional data and is inefficient in time, effort and cost in filling out the APL form and also the frequent loss of participant competency certificate archives because there is no specific system for managing data certification. The purpose of conducting this research is to help the management of the Engineering Hospitality Indonesia Professional Certification Institute find solutions to existing problems by building and implementing a Professional Certification Management Information System at the Indonesian Hospitality Engineering Professional Certification Institute which has a website platform. This research was conducted using the Waterfall Software Development Life Cycle (SDLC) method. This system is designed using Context Diagrams, Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD) and also this system is built with a website platform that uses the php programming language with the Laravel framework. In testing the system is done by blackbox testing and also distributing questionnaires to respondents as measured by a Likert scale to determine user acceptance of the information system. This system gets an average value of 85.8% based on measuring user acceptance of the system and it can be concluded that the Professional Certification Institute Certification Management Information System has been designed with "Very Good".
Exploring Heterogeneous Decentralized Markets in DeFi and NFT on Ethereum Blockchain

Blockchain applications have grown tremendously recently, especially in the Decentralized Finance (DeFi) and Non-fungible Token (NFT) markets. The DeFi and NFT markets generate massive transactions and research-worthy data. However, few studies have systematically processed and analyzed them, preventing users from understanding the ecosystem. The main challenge of analyzing the DeFi & NFT markets is the heterogeneity of data that different markets have heterogeneous businesses and data. To address this problem, in this paper, we propose a framework to explore the heterogeneous decentralized markets in DeFi and NFT on the Ethereum blockchain. Based on this framework, we analyze the data of 21 exchange/lending markets in DeFi/NFT, with 184,173,656 records in total. We investigate the activity, profitability, and security of these markets. We obtain several findings to help market users through quantitative analysis. Datasets and codes are released.
The Role of Customer Satisfaction in Improving the Performance of Sail (Steel Authority of India LTD)

Purpose: The study focussed on the influence of customer satisfaction on reform policies and the performance of SAIL and attempts to investigate the influence of reform policies in improving the performance of SAIL (De, 2014).&#x0D; &#x0D; Theoretical Framework: Research examined the moderating role of customer satisfaction between reform policies and the performance of SAIL. A conceptual model with two hypotheses was developed and data were collected from 385 employees and managerial staff in the form of a questionnaire from five integrated units of SAIL (Jain et al., 2014).&#x0D; &#x0D; Design/Methodology/Approach: Paper goes through the quantitative and qualitative discussion to analyse the performance of SAIL against the backdrop of liberalization measures introduced in the 1990s and afterward. The study will analyse two different periods of SAIL: Before 1990 and after 1990.&#x0D; &#x0D; Findings: The findings of the study provide useful insights to evaluate and improve SAIL's reform policies and performance and to enhance customer satisfaction.&#x0D; &#x0D; Conclusion: Article paves the way to huge scope for future researchers. The scope of the study can be further broadened by including several PSEs across multiple industries to gain comprehensive findings about the impact of reforms on the performance of different types of PSEs.
Document-level relation extraction with hierarchical dependency tree and bridge path

The inter-sentence relation in a document is characterized by complex contextual information, large span of correlation and many kinds of relations, leading to the poor effect of sentence-level relation extraction models when addressing inter-sentence relations. Graph networks have been widely used in the research of document-level relation extraction due to their advantages in modeling local structural features and long-distance context dependencies. However, most previous studies modeled document in a coarse-grained manner, which ignores the richness and otherness of hierarchical features in a document. Consequently, contextual information modeling is not sufficient and fails to participate in deep reasoning efficiently. In this paper, we propose a document-level relation extraction model based on the Hierarchical Dependency Tree and Bridge Path (HDT-BP). The model uses sentence as a unit to independently extract the fine-grained features of each hierarchy and reconstructs the chain-structured document based on multiple dependent relationships into a hierarchical dependency tree. Moreover, the relational bridge entity is introduced during relation extraction to improve the model performance by modeling the bridge path feature. Experimental results demonstrate that our model exhibits superior performance on the DocRED dataset and achieves a significant improvement in extracting relational facts that never appeared in the training set. Extensive additional experiments further verify the effectiveness of our model.
Robust Fingerprint of Privacy-Preserving Location Trajectories

Location-based services have brought significant convenience to people in their daily lives, and the collected location data are also in high demand. However, directly releasing those data raises privacy and liability (e.g., due to unauthorized distribution of such datasets) concerns since location data contain users' sensitive information, e.g., regular moving patterns and favorite spots. To address this, we propose a novel fingerprinting scheme that simultaneously identifies unauthorized redistribution of location datasets and provides differential privacy guarantees for the shared data. Observing data utility degradation due to differentially-private mechanisms, we introduce a utility-focused post-processing scheme to regain spatiotemporal correlations between points in a location trajectory. We further integrate this post-processing scheme into our fingerprinting scheme as a sampling method. The proposed fingerprinting scheme alleviates the degradation in the utility of the shared dataset due to the noise introduced by differentially-private mechanisms (i.e., adds the fingerprint by preserving the publicly known statistics of the data). Meanwhile, it does not violate differential privacy throughout the entire process due to immunity to post-processing, a fundamental property of differential privacy. Our proposed fingerprinting scheme is robust against known and well-studied attacks against a fingerprinting scheme including random flipping attacks, correlation-based flipping attacks, and collusions among multiple parties, which makes it hard for the attackers to infer the fingerprint codes and avoid accusation. Via experiments on two real-life location datasets and two synthetic ones, we show that our scheme achieves high fingerprinting robustness and outperforms existing approaches. Besides, the proposed fingerprinting scheme increases data utility for differentially-private datasets, which is beneficial for data analyzers.
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development

The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.In this paper, we introduce PRIMEQA: a one-stop and open-source QA repository with an aim to democratize QA research and facilitate easy replication of stateof-the-art (SOTA) QA methods.PRIMEQA supports core QA functionalities like retrieval and reading comprehension as well as auxiliary capabilities such as question generation.It has been designed as an end-to-end toolkit for various use cases: building front-end applications, replicating SOTA methods on public benchmarks, and expanding pre-existing methods.
Human-in-the-loop Schema Induction

Schema induction builds a graph representation explaining how events unfold in a scenario.Existing approaches have been based on information retrieval (IR) and information extraction (IE), often with limited human curation.We demonstrate a human-in-the-loop schema induction system powered by GPT-3. 1 We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph.By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches but also reduces efforts of human curation thanks to our interactive interface.
A System for Answering Simple Questions in Multiple Languages

Our research focuses on the most prevalent type of queries-simple questions-exemplified by questions like "What is the capital of France?".These questions reference an entity such as "France", which is directly connected (one hop) to the answer entity "Paris" in the underlying knowledge graph (KG).We propose a multilingual Knowledge Graph Question Answering (KGQA) technique that orders potential responses based on the distance between the question's text embeddings and the answer's graph embeddings.A system incorporating this novel method is also described in our work.Through comprehensive experimentation using various English and multilingual datasets and two KGs -Freebase and Wikidata -we illustrate the comparative advantage of the proposed method across diverse KG embeddings and languages.This edge is apparent even against robust baseline systems, including seq2seq QA models, search-based solutions and intricate rule-based pipelines.Interestingly, our research underscores that even advanced AI systems like ChatGPT encounter difficulties when tasked with answering simple questions.This finding emphasizes the relevance and effectiveness of our approach, which consistently outperforms such systems.We are making the source code and trained models from our study publicly accessible to promote further advancements in multilingual KGQA.
Pyramid Graph Neural Network: A Graph Sampling and Filtering Approach for Multi-scale Disentangled Representations

Spectral methods for graph neural networks (GNNs) have achieved great success. Despite their success, many works have shown that existing approaches are mainly focused on low-frequency information which may not be pertinent to the task at hand. Recent efforts have been made to design new graph filters for wider frequency profiles, but it remains an open problem how to learn multi-scale disentangled node embeddings in the graph Fourier domain. In this paper, we propose a graph (signal) sampling and filtering framework, entitled Pyramid Graph Neural Network (PyGNN), which follows the Downsampling-Filtering-Upsampling-Decoding scheme. To be specific, we develop an ω-bandlimited downsampling approach to split input graph into subgraphs for the reduction of high-frequency components, then perform spectral graph filters on subgraphs to achieve node embeddings with different frequency bands, and propose a Laplacian smoothing-based upsampling approach to extrapolate the node embedding on subgraphs to the full set of vertices on the original graph. In the end, we add frequency-aware gated units to decode node embeddings of different frequencies for downstream tasks. Results on both homophilic and heterophilic graph datasets show its superiority over state-of-the-art methods.
Keyphrase Generation Beyond the Boundaries of Title and Abstract

Keyphrase generation aims at generating important phrases (keyphrases) that best describe a given document.In scholarly domains, current approaches have largely used only the title and abstract of the articles to generate keyphrases.In this paper, we comprehensively explore whether the integration of additional information from the full text of a given article or from semantically similar articles can be helpful for a neural keyphrase generation model or not.We discover that adding sentences from the full text, particularly in the form of the extractive summary of the article can significantly improve the generation of both types of keyphrases that are either present or absent from the text.Experimental results with three widely used models for keyphrase generation along with one of the latest transformer models suitable for longer documents, Longformer Encoder-Decoder (LED) validate the observation.We also present a new large-scale scholarly dataset FULLTEX-TKP for keyphrase generation.Unlike prior large-scale datasets, FULLTEXTKP includes the full text of the articles along with the title and abstract.We release the source code at https://github.com/kgarg8/FullTextKP.
InforMask: Unsupervised Informative Masking for Language Model Pretraining

Masked language modeling is widely used for pretraining large language models for natural language understanding (NLU).However, random masking is suboptimal, allocating an equal masking rate for all tokens.In this paper, we propose InforMask, a new unsupervised masking strategy for training masked language models.InforMask exploits Pointwise Mutual Information (PMI) to select the most informative tokens to mask.We further propose two optimizations for InforMask to improve its efficiency.With a one-off preprocessing step, InforMask outperforms random masking and previously proposed masking strategies on the factual recall benchmark LAMA and the question answering benchmark SQuAD v1 and v2.