Reviewerly/exHarmony huggingface dataset:
Regulatory sandboxes in the AI Act: reconciling innovation and safety?

This paper explores the regulatory sandbox regime under the EU's draft Artificial Intelligence (AI) Act. It investigates how useful the sandbox regime is for testing an AI-based skin cancer detection systems in an EU member state. The paper focuses on whether the proposed AI regulatory sandbox regime can resolve tensions between innovation and safety. Although we find considerable potential for the sandbox regime, the proposal also creates several legal issues. It blurs jurisdictional boundaries between the EU and member states, raises concerns of legality and equal treatment, creates liability risks for innovators, and fails to require informed consent from testing subjects. To address these problems, the paper suggests adopting a more targeted legal basis for the sandbox regime that takes inspiration from conventional testing mechanisms such as clinical investigations for medical devices.
Competitive strategy development through green supply chain practices

Nowadays, the topic of preserving the environment and serving local communities is a hot issue. Hence, this study aims to explore how the green supply chain affects Jordan's pharmaceutical manufacturing industry's ability to compete globally. This study's research methodology is quantitative, descriptive, and cause-effect. Data was gathered from a sample of 124 managers selected randomly from a pool of 300 managers working in 10 out of 14 pharmaceutical manufacturing organizations. The study tool underwent evaluations for normality, validity, and reliability before the data were subjected to descriptive analysis and a correlation analysis was performed between variables. Finally, hypothesis testing was conducted through the application of multiple regression analysis. The findings show that green practices affect competitive strategy, where green operations were having the highest effect on competitive strategies, then green purchasing, and green selling, respectively. The study's conclusions show that the adoption of a green supply chain improves the competitiveness of the Jordanian pharmaceutical manufacturing sector. Accordingly, the study recommends that Jordanian pharmaceutical manufacturing companies should include green supply chain practices in their daily supply practices to increase the competitiveness of the organizations.
Empirical Analysis of Supervised and Unsupervised Machine Learning Algorithms with Aspect-Based Sentiment Analysis

Abstract Machine learning based sentiment analysis is an interdisciplinary approach in opinion mining, particularly in the field of media and communication research. In spite of their different backgrounds, researchers have collaborated to test, train and again retest the machine learning approach to collect, analyse and withdraw a meaningful insight from large datasets. This research classifies the texts of micro-blog (tweets) into positive and negative responses about a particular phenomenon. The study also demonstrates the process of compilation of corpus for review of sentiments, cleaning the body of text to make it a meaningful text, find people’s emotions about it, and interpret the findings. Till date the public sentiment after abrogation of Article 370 has not been studied, which adds the novelty to this scientific study. This study includes the dataset collection from Twitter that comprises 66.7 % of positive tweets and 34.3 % of negative tweets of the people about the abrogation of Article 370. Experimental testing reveals that the proposed methodology is much more effective than the previously proposed methodology. This study focuses on comparison of unsupervised lexicon-based models (TextBlob, AFINN, Vader Sentiment) and supervised machine learning models (KNN, SVM, Random Forest and Naïve Bayes) for sentiment analysis. This is the first study with cyber public opinion over the abrogation of Article 370. Twitter data of more than 2 lakh tweets were collected by the authors. After cleaning, 29732 tweets were selected for analysis. As per the results among supervised learning, Random Forest performs the best, whereas among unsupervised learning TextBlob achieves the highest accuracy of 99 % and 88 %, respectively. Performance parameters of the proposed supervised machine learning models also surpass the result of the recent study performed in 2023 for sentiment analysis.
Impact of dynamic capabilities on competitive performance: A moderated-mediation model of entrepreneurship orientation and digital leadership

This research aims to provide a logical and experimental framework that helps organizations achieve goals in turbulent environments by examining the impact of dynamic capabilities on competitive performance with the conditional indirect effect of entrepreneurship orientation and digital leadership. A conceptual framework was derived from well-established theories in strategic management, along with empirical evidence based on a survey conducted on a sample of 102 leaders and managers in the entrepreneurial companies in Jordan. This study demonstrates the positive impact of dynamic capabilities in developing competitive performance. Moreover, the entrepreneurship orientation mediates the relationship between dynamic capabilities and competitive performance and digital leadership has a positive moderating role in this relationship. This research recommends leaders and managers in entrepreneurial organizations to define clear standards for measuring competitive performance that enable identifying and correcting deviations in a timely manner and invites them to focus on creating value in turbulent environments by exploiting advanced technological capabilities and adopting innovative strategies and business models.
A Scalable Blockchain Framework for Secure Transactions in IoT-Based Dynamic Applications

The essential characteristics of blockchain, such as immutability and auditability, are leading to its increasing incorporation into society.The Internet of Things (IoT) aims to create a decentralized framework for IoT and enhance security.The processing of transactions for different IoT applications poses challenges for the convergence of IoT and blockchain due to varying performance requirements.In addition, it is important to note that the dispersed IoT system's membership may change when an IoT device adds or departs from it.As the system becomes dynamic, it presents new difficulties for managing devices.Accordingly, a dynamic application block generation (DABG) scheme for blockchain-enabled IoT with dynamic device management and conditional traceability is proposed.To begin, it is necessary to construct a framework for an IoT system based on a consortium blockchain.This framework should consist of structures for dynamic application transactions and blocks, as well as a consensus mechanism.Miners are presented in distinct ways to adaptively process urgent and routine application transactions.A protocol based on group signature, known as DABG, is proposed for this framework.It is possible to achieve nonframeability, traceability, and anonymity by the use of the group signature.The suggested method may quickly validate transactions, manage devices in a dynamic manner, enable conditional traceability while maintaining data security, and keep users' privacy intact.This is made possible by merging time-bound keys in the group signatures and node in the blockchain.Extensive testing has shown that the strategy in question has the potential to achieve very high levels of effectiveness.
The Characterization of Mechanical and Chemical Properties of Recycled Styrofoam Waste Employing Extrusion Process

&lt;p&gt;&lt;em&gt;Styrofoam waste is a polystyrene plastic-based waste in the form of foam with low density. Scavengers or recyclers are not interested in receiving styrofoam waste because of its light density which makes it ineffective in transportation and storage. Styrofoam waste can actually be converted into solid polystyrene products through a thermal-extrusion process but data regarding the optimum operating temperature and the characteristics of the resulting solid polystyrene products are not yet available. The purpose of this study was to investigate the properties of solid polystyrene products resulting from recycled styrofoam waste and determine the optimum temperature of the thermal-extrusion treatment process to be compared with the characteristics of the original polystyrene based on the results of tensile and impact tests. In this study, the processing was carried out using the thermal-extrusion method; Styrofoam is melted and extruded into a mold to produce polystyrene solids. The research was carried out with variations in extrusion temperature at 180°C, 200°C, and 220°C. The polystyrene solids are then molded into tensile and impact test specimens by injection molding. The results showed that the optimal extrusion temperature was 200°C with tensile and impact strength values of 27.55 MPa and 1,069 j/m2, respectively. Compared to the original polystyrene, the tensile strength value is 25.3% lower and the impact strength value is 29.5% lower. The decrease in the tensile and impact strength values is due to the shortening of the molecular bonds in the recycled polystyrene during the thermal-extrusion treatment process. Even though the tensile strength and impact are still lower, the use of styrofoam waste using the thermal-extrusion method has the potential to be developed at the production and commercialization stage because the resulting product has good economic value and can also reduce the use of original polystyrene and at the same time can solve the problem of styrofoam waste.&lt;/em&gt;&lt;/p&gt;
MAPPING THE IMPACT OF THE INTELLECTUAL CAPITAL ON THE AGILITY AND PERFORMANCE OF AN ORGANIZATION: A BIBLIOMETRIC STUDY

Research Background: In a changing environment, it is very important for the organizations to understand that the intellectual capital represents an asset for the organization, as it can impact its agility and performance. While previous research has explored the relationship between intellectual capital, agility, and performance, there is a need for a comprehensive understanding of the knowledge landscape and for the research trends when it comes to this topic. Thus, a bibliometric analysis offers a systematic and quantitative approach to map the intellectual structure, identify influential studies, and uncover emerging themes and gaps in the literature. Purpose of the article: The purpose of this research is to present a bibliometric analysis of the literature focusing on the impact of the intellectual capital in the organizational agility and performance. Methods: The methodology employed in this study is centered around a bibliometric analysis conducted using the specialized software VOSviewer. This analysis visually represents nine semantic clusters that depict the co-citation relationships among various concepts related to the search terms utilized, such as "intellectual capital," "performance," and "agility." The study utilized papers published in Scopus-indexed journals as the database. Findings and value added: The research paper's outcomes consist of graphical representations of the semantic clusters associated with the aforementioned search terms, alongside tables presenting content analysis of these clusters and other relevant publication data (e.g., top 10 countries contributing to the topic and document types retrieved). The findings unequivocally establish a significant correlation between innovation, intellectual capital, knowledge management, performance, and agility. This research makes a notable contribution as the first bibliometric analysis exploring the interrelation of intellectual capital, performance, and agility, as evidenced by the papers retrieved from the Scopus database. Keywords: Intellectual capital; Performance; Agility; Bibliometri
On the Stability-Plasticity Dilemma of Class-Incremental Learning

A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from previously seen classes, and plastic enough to learn concepts from new classes. While previous works demonstrate strong performance on class-incremental benchmarks, it is not clear whether their success comes from the models being stable, plastic, or a mixture of both. This paper aims to shed light on how effectively recent class-incremental learning algorithms address the stability-plasticity trade-off. We establish analytical tools that measure the stability and plasticity of feature representations, and employ such tools to investigate models trained with various algorithms on large-scale class-incremental benchmarks. Surprisingly, we find that the majority of class-incremental learning algorithms heavily favor stability over plasticity, to the extent that the feature extractor of a model trained on the initial set of classes is no less effective than that of the final incremental model. Our observations not only inspire two simple algorithms that highlight the importance of feature representation analysis, but also suggest that class-incremental learning approaches, in general, should strive for better feature representation learning.
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation

Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a global model by multiple clients with data privacy-preserving. Although many FL algorithms have been proposed for classification tasks, few works focus on more challenging semantic segmentation tasks, especially in the class-heterogeneous FL situation. Compared with classification, the issues from heterogeneous FL for semantic segmentation are more severe: (1) Due to the non-IID distribution, different clients may contain inconsistent foreground-background classes, resulting in divergent local updates. (2) Class-heterogeneity for complex dense prediction tasks makes the local optimum of clients farther from the global optimum. In this work, we propose FedSeg, a basic federated learning approach for class-heterogeneous semantic segmentation. We first propose a simple but strong modified cross-entropy loss to correct the local optimization and address the foreground-background inconsistency problem. Based on it, we introduce pixel-level contrastive learning to enforce local pixel embeddings belonging to the global semantic space. Extensive experiments on four semantic segmentation benchmarks (Cityscapes, CamVID, PascalVOC and ADE20k) demonstrate the effectiveness of our FedSeg. We hope this work will attract more attention from the FL community to the challenging semantic segmentation federated learning.
An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images

This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.The publicly available training dataset provided for the 2021 RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge was used in this study, consisting of 1251 multi-institutional, multi-parametric MR images. Post-contrast T1, T2, and T2 FLAIR images as well as ground truth manual segmentation were used as input for the model. The data were split into a training set of 1151 cases and testing set of 100 cases, with the testing set remaining constant throughout. Deep convolutional neural network segmentation models were trained using the NiftyNet platform. To test the viability of active learning in training a segmentation model, an initial reference model was trained using all 1151 training cases followed by two additional models using only 575 cases and 100 cases. The resulting predicted segmentations of these two additional models on the remaining training cases were then addended to the training dataset for additional training.It was demonstrated that an active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas (0.906 reference Dice score vs 0.868 active learning Dice score) while only requiring manual annotation for 28.6% of the data.The active learning approach when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.Active learning concepts were applied to a deep learning-assisted segmentation of brain gliomas from MR images to assess their viability in reducing the required amount of manually annotated ground truth data in model training.• This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model. • The active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas. • Active learning when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.
Trajectory Tracking of a Quadcopter UAV using PID Controller

UAVs or Drones are aircraft with no onboard pilot to control the flight. They are introduced in a few categories such as single-rotor, multi-rotors, fixed-wing, and hybrid VTOL. As for multirotor drones, quadcopters are the most well-known either commercially or in the research field. Due to its advantages, a quadcopter has been chosen to perform various tasks across various fields such as entertainment, military, meteorological reconnaissance, civil and emergency responses. As the demand for quadcopters has diverged, the required features of quadcopters have also diverged. One of the current features required by quadcopters is the ability to track trajectories. However, due to its nature of non-linearity, under-actuated and unstable, controlling quadcopter for an accurate and stable performance is quite a challenge. Despite the various proposed methods throughout the past decades, PID controller is still used as either the main controller or the base controller in most cases of industrial control, including quadcopter, mainly due to its simplicity and robustness. However, to design a proper PID controller for quadcopter system is a challenge as it defies the control inputs of four with its six degree-of-freedom form, in which six inputs are required to be controlled to ensure a stable and accurate flight. This paper derived a mathematically model of a quadcopter with Newton-Euler’s equation. Some assumptions on the body and structure of the quadcopter are taken into account to make the modelling possible. Then, a manually tuned PID controller is designed to achieve the objective of controlling the operation and stability of the quadcopter during its flight. The designed controller is tested with five different trajectories which are circular, square, lemniscate, zigzag, and spiral. The results show the proposed controller successfully tracks the desired trajectories, which prove PID controller can be used to control a quadcopter.
Reliable Natural Language Understanding with Large Language Models and Answer Set Programming

Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions.While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they fall short in problems that require reasoning.They also cannot reliably explain the answers generated for a given question.In order to emulate humans better, we propose STAR, a framework that combines LLMs with Answer Set Programming (ASP).We show how LLMs can be used to effectively extract knowledge-represented as predicates-from language.Goal-directed ASP is then employed to reliably reason over this knowledge.We apply the STAR framework to three different NLU tasks requiring reasoning: qualitative reasoning, mathematical reasoning, and goal-directed conversation.Our experiments reveal that STAR is able to bridge the gap of reasoning in NLU tasks, leading to significant performance improvements, especially for smaller LLMs, i.e., LLMs with a smaller number of parameters.NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.
Motion In-Betweening via Deep $\Delta$-Interpolator

We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Delta$</tex-math></inline-formula> -regime is viable with respect to the reference of the last known frame (also known as the zero-velocity model). This supports the more general conclusion that operating in the reference frame local to input frames is more accurate and robust than in the global (world) reference frame advocated in previous work. Our code is publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/boreshkinai/delta-interpolator</uri> .
A Role of Artificial Intelligence in the Context of Economy: Bibliometric Analysis and Systematic Literature Review

This bibliometric analysis aims to explore and assess the multifaceted impact of technology on the economy through a comprehensive review of academic literature. By systematically examining a wide range of research articles, this study intends to provide insights into the various ways in which technology influences economic growth, productivity, innovation, and other key indicators. The analysis will identify trends, influential authors, and prominent journals in the field, shedding light on the evolving relationship between technology and the economy. The paper might highlight a gap in research on how AI is being adopted and leveraged in developing economies. The actual research gaps addressed in the paper will depend on the findings of their systematic literature review and bibliometric analysis. The significance of this paper reflects a recognition that AI has become a driving force behind innovation, productivity improvements, and competitive advantage across industries. The main objective of this research is to use bibliometric analysis to investigate the publication output about artificial intelligence, using data sourced from the Scopus database, to contribute to economic improvement. The data has been gathered using specified terms, including "artificial intelligence, technologies," and "economy," with a focus on the fields of business management, accounting, and computer sciences. The bibliometric analysis included the use of Scopus to gather data, using VOSviewer, Rstudio, and Excel as analytical tools. The data collection spanned from 2016 to 2023. The primary aims of this work are doing a comprehensive bibliometric analysis, examining the co-citation structure, analyzing keyword co-occurrence, and conducting a geographical analysis within the domain of artificial intelligence and economics. The research yields findings about the implications of publishing in the field of artificial intelligence and its impact on business, management, accounting, and computer science from an academic standpoint. This article aims to assist scholars and business experts seeking to construct contemporary company structures, serving as a valuable resource for future endeavors in this field.