Reviewerly/exHarmony huggingface dataset:
Organizational Justice, Employee Empowerment, and Innovative Behavior in the Post-COVID Era: Evidence from SMEs in a Majority-Muslim Country

The objective of this study is to investigate the effects of organizational justice on the innovation behavior of employees working in small and medium-sized enterprises (SMEs) in a majority-Muslim country. Through a combination of qualitative and quantitative methods, the researchers will be able to identify the factors that influence this behavior. The study collected data from 319 individuals who were working for Moroccan SMEs, and the results of the quantitative and qualitative methods were analyzed to determine the factors that influence their innovation behavior. The structural equation modeling results revealed that organizational justice had a positive impact on employees’ innovation. The study showed that the use of informational, distributive, and interactional justice had a positive effect on the innovation behavior of employees working in Moroccan SMEs. Furthermore, researchers found that organizational behavior was associated with the link between innovation and organizational justice. These findings provide valuable theoretical contributions to the field of business strategy and management in a Muslim-majority country. Hence, to effectively implement organizational justice in their organizations, Moroccan SMEs should encourage their employees to adopt innovative behaviors to improve their productivity in order to contribute to the country's economic development.
Demonstration Shaped Reward Machine for Robot Assembly Reinforcement Learning Tasks

Reinforcement Learning has been proven to be successfully applied in robotic manipulation tasks, but it has been confronted with problems about interacting with the dynamic environment and generating high-level policies. Existing reward functions include sparse and shaped reward, and other way to view the reward functions as black boxes and then discover the best strategies by interacting with the environment. In this paper, a reward machine with demonstration definition for robot assembly is presented, which is a finite state machine that supports generating the specified reward function from the demonstration data and displays the reward function structure. By dividing the task demonstration data, extracting the key points of the task and defining the state of the robot, a reward function based on state migration is generated. The robot chooses the best reward according to the current state, which is conducive to using the reward structure to improve the sample efficiency and the quality of the result strategy. In this paper, we focus on the robot assembly reinforcement learning task. The structure of the proposed method is used to carry out experiments in the robot simulator. We compare the design methods with different reward functions and it proves that the proposed method can effectively improve the training efficiency of robot reinforcement learning.
Corporate Entrepreneurship and Organizational Success: Strategic Flexibility as a Mediator in the Jordanian Pharmaceutical Industry

This research aimed to investigate the effect of corporate entrepreneurship on organizational success in the Jordanian pharmaceutical industry with the mediating role of strategic flexibility. The research consists of independent variables represented by the corporate entrepreneurship factors (Creativity, Innovation, Risk-taking, and Pro-activeness). Organizational success is the dependent variable, and Strategic flexibility is the mediating variable. In order to demonstrate the relationships between independent, dependent, and mediating variables, a quantitative approach was employed to gather primary data through a research questionnaire, which was distributed to selected pharmaceutical manufacturing firms in Jordan. The research population consists of 12 pharmaceutical firms, which are members of (JAPM). And the sample is purposive of approximately 320 employees from the top and middle management levels. The number of respondents was 211. The data were analyzed using SPSS (28) and AMOS (26) by confirmatory factor analysis, descriptive analysis, and structural equation modeling (SEM) tools. The data analysis results found a significant positive influence of corporate entrepreneurship on organizational success; also, all components of corporate entrepreneurship have a significant impact on organizational success. Strategic flexibility has a partial mediating effect on the relationship between corporate entrepreneurship and organizational success. This research adds to the practical and academic knowledge, where the research model was the first attempts to investigate the effect of corporate entrepreneurship on organizational success in the Jordanian pharmaceutical industry. It results in many implications for strategic choices development, such as focusing on corporate entrepreneurship dimensions to improve organizational success. The findings of this research give essential suggestions for decision-makers, managers, and those hoping to support and improve corporate entrepreneurship in Jordanian firms.
A Rule-Based Approach for Designing and Composing Abstract Domains

Abstract interpretation allows constructing sound static analysis tools by safely approximating program semantics. Frameworks for abstract interpretation typically provide an implementation of a specialized iteration strategy to compute an abstract fixpoint, as well as a number of abstract domains in order to approximate different program properties. However, the design and implementation of additional domains, as well as their combinations, is eventually necessary to successfully prove arbitrary program properties. We propose a rule-based methodology for rapid design and prototyping of new domains and combining existing ones, with a focus on the analysis of logic programs. We provide several examples for domains combining numerical properties and data types and apply them to proving complex program properties.
Predicate Anti-unification in (Constraint) Logic Programming

The concept of anti-unification refers to the process of determining the most specific generalization (msg) of two or more input program objects. In the domain of logic programming, anti-unification has primarily been investigated for computing msgs of tree-like program structures such as terms, atoms, and goals (the latter typically seen as ordered sequences). In this work, we study the anti-unification of whole predicate definitions. We provide a definition of a predicate generalization that allows to characterize the problem of finding the most specific generalization of two predicates as a (computationally hard) search problem. The complexity stems from the fact that a correspondence needs to be constructed between (1) some of the arguments of each of the predicates, (2) some of the clauses in each of the predicate’s definitions, and (3) some of the body atoms in each pair of associated clauses. We propose a working algorithm that simultaneously computes these correspondences in a greedy manner. While our algorithm does not necessarily compute the most specific generalization, we conjecture that it allows to compute, in general, a sufficiently good generalization in an acceptable time.
Is the Encoder Necessary in DETR-Type Models?-Analysis of Encoder Redundancy

Object detection model DETR (Detection Transformer) based on the Transformer architecture has demonstrated outstanding performance in various benchmark tests. However, these models often have a large number of parameters, which can make them slow and computationally intensive. Therefore, it is crucial to optimize the performance of these models by identifying any redundancy in the recognition model. This paper analyzes the internal mechanism of DETR detection and reveals that the backbone network and the transformer decoder already serve the intended purpose of the encoder module. The analysis results are validate through experimental design. It is find that the encoder module does not fulfill its initial intended role in the network but instead increases the parameter count and computational complexity of the entire network. For example, the performance of a single-layer encoder is equivalent to that of six layers. When using a Transformer backbone network, the encoder does not provide performance improvement to the overall architecture. Therefore, it is possible to optimize DETR-like architectures by using suitable module components without employing the encoder structure.
Towards a Certified Proof Checker for Deep Neural Network Verification

Recent developments in deep neural networks (DNNs) have led to their adoption in safety-critical systems, which in turn has heightened the need for guaranteeing their safety. These safety properties of DNNs can be proven using tools developed by the verification community. However, these tools are themselves prone to implementation bugs and numerical stability problems, which make their reliability questionable. To overcome this, some verifiers produce proofs of their results which can be checked by a trusted checker. In this work, we present a novel implementation of a proof checker for DNN verification. It improves on existing implementations by offering numerical stability and greater verifiability. To achieve this, we leverage two key capabilities of Imandra, an industrial theorem prover: its support for exact real arithmetic and its formal verification infrastructure. So far, we have implemented a proof checker in Imandra, specified its correctness properties and started to verify the checker’s compliance with them. Our ongoing work focuses on completing the formal verification of the checker and further optimising its performance.
A Novel EGs-Based Framework for Systematic Propositional-Formula Simplification

This paper presents a novel simplification calculus for propositional logic derived from Peirce’s Existential Graphs’ rules of inference and implication graphs. Our rules can be applied to arbitrary propositional logic formulae (not only in CNF), are equivalence-preserving, guarantee a monotonically decreasing number of clauses and literals, and maximise the preservation of structural problem information. Our techniques can also be seen as higher-level SAT preprocessing, and we show how one of our rules (TWSR) generalises and streamlines most of the known equivalence-preserving SAT preprocessing methods. We further show how this rule can be extended with a novel n-ary implication graph to capture all known equivalence-preserving preprocessing procedures. Finally, we discuss the complexity and implementation of our framework as a solver-agnostic algorithm to simplify Boolean satisfiability problems and arbitrary propositional formula.
Fuzzy Logic-Based Software Systems

Fuzzy logic was introduced by Lotfi A. Zadeh in 1965 as a methodology for computing with words and since then it is used extensively. It is an artificial intelligent technique that is ideal for successfully addressing, , the uncertainty, imprecision and vagueness that exist in many diverse scientific and technological areas. It is used in software systems to automatically predict, model, decide, diagnose, recommend etc.. In this chapter, several Fuzzy Logic-based software systems in the discipline of medicine, education, decision making and recommendation, natural language processing, automotive engineering and industry, heating, ventilation and air-conditioning, navigation, scheduling, network traffic and security are presented. This chapter aims to provide deep insights and valuable information about significant advances of Fuzzy Logic in software systems to researchers, scientists, educators, students, software engineers and developers.
Research on Object Detection Methods in Low-Light Conditions

Low-light images are images taken in poorly illuminated environments. Such images suffer from colour distortion, loss of detail and blurriness, which seriously affects the detection accuracy of object detection tasks. In order to improve the accuracy of object detection in low-light images, we propose a low-light image object detection algorithm based on image enhancement. The algorithm is jointly trained on the input side of the YOLOv5 network in combination with an unsupervised low-light enhancement model. The training phase optimises the overall network with the loss of object detection so that the image enhancement results are more favourable for improving the object detection accuracy. In the feature extraction phase, we design a feature enhancement model based on an attention mechanism. Our algorithm is tested on the publicly available ExDark dataset and achieves a mean average precision (mAP) of 79.15%, which is a 4.25% improvement over the baseline.
Reassembling Fragmented Entity Names: A Novel Model for Chinese Compound Noun Processing

In the process of classifying intelligent assets, we encountered challenges with a limited dataset dominated by complex compound noun phrases. Training classifiers directly on this dataset posed risks of overfitting and potential misinterpretations due to inherent ambiguities in these phrases. Recognizing the gap in the current literature for tailored methods addressing this challenge, this paper introduces a refined approach for the accurate extraction of entity names from such structures. We leveraged the Chinese pre-trained BERT model combined with an attention mechanism, ensuring precise interpretation of each token’s significance. This was followed by employing both a multi-layer perceptron (MLP) and an LSTM-based Sequence Parsing Model, tailored for sequence annotation and rule-based parsing. With the aid of a rule-driven decoder, we reconstructed comprehensive entity names. Our approach adeptly extracts structurally coherent entity names from fragmented compound noun phrases. Experiments on a manually annotated dataset of compound noun phrases demonstrate that our model consistently outperforms rival methodologies. These results compellingly validate our method’s superiority in extracting entity names from compound noun phrases.
Impact of Internal Marketing on Organizational Performance: The Moderating Role of Employee Commitment

The study aimed to investigate the impact of internal marketing on organizational performance with the moderating role of employee commitment. The data was collected from 333 employees working in Jordanian organizations. Smart pls three was used to analyze the data. The data was collected using a questionnaire. The dimensions of internal marketing showed a positive and significant impact on organizational performance, including vision, rewards and compensation, development, and Training. Furthermore, the moderating role of employee commitment was found to be positive and significant in the relationship between vision and performance. The study discussed the result in line with the existing literature and provided research implications. The future avenues of the research are also discussed.
A Pandemic Adaptation and Its Aftermath: Using AI and In-Person Facilitation for Community Health Education in Liberia and the United States

Health promotion commonly focuses on supporting youth wellness, as health behaviors acquired in childhood and adolescence tend to have a significant impact on an individual's future. Adolescent health education is associated with positive health and educational outcomes, yet young people experience barriers to fully engaging in learning about health issues that are often unique to their social location. Barriers for successful engagement in health education for African diaspora youth in North American and European contexts may include school initiatives built around engagement models that do not center Black youth; for Black youth in majority-Black societies, barriers may include access to resources or exclusionary practices based on other social characteristics. Global health promotion has used a variety of multimodal educational tools from radio to more recently online engagement, especially in African contexts, to reach young people. This essay shares experiences using AI and in-person facilitation to engage in community health education with youth in Liberia and the United States. In our practice, we found that there are far more underlying systemic and structural similarities to the inequities experienced between African and Black American youth and that utilizing AI tools alongside of in-person discussion may contribute to better outcomes for youth health education.
A deep learning approach for inpatient length of stay and mortality prediction

Accurate prediction of the Length of Stay (LoS) and mortality in the Intensive Care Unit (ICU) is crucial for effective hospital management, and it can assist clinicians for real-time demand capacity (RTDC) administration, thereby improving healthcare quality and service levels.This paper proposes a novel one-dimensional (1D) multi-scale convolutional neural network architecture, namely 1D-MSNet, to predict inpatients' LoS and mortality in ICU. First, a 1D multi-scale convolution framework is proposed to enlarge the convolutional receptive fields and enhance the richness of the convolutional features. Following the convolutional layers, an atrous causal spatial pyramid pooling (SPP) module is incorporated into the networks to extract high-level features. The optimized Focal Loss (FL) function is combined with the synthetic minority over-sampling technique (SMOTE) to mitigate the imbalanced-class issue.On the MIMIC-IV v1.0 benchmark dataset, the proposed approach achieves the optimum R-Square and RMSE values of 0.57 and 3.61 for the LoS prediction, and the highest test accuracy of 97.73% for the mortality prediction.The proposed approach presents a superior performance in comparison with other state-of-the-art, and it can effectively perform the LoS and mortality prediction tasks.
Exploiting local label correlation from sample perspective for multi-label classification via three-way decision theory

In multi-label classification, the expansion of output dimension seriously interferes learning performance, and even fails to build a joint prediction model. In order to restrain the proliferation of multi-label classifier’s hypothesis space, the current works focus on the application of global positive label correlation. However, the “black or white” mechanism ignore other possible forms of label correlation, such as negative or neutral correlation. By introducing the doctrine of the mean, three-way decision (3WD) theory provides a solution for in-depth research on local label correlation, and aims to handle the uncertainty of multi-label learning tasks. In this paper, a novel learning algorithm for multi-label joint classification, namely ML-3WD, is proposed by considering the 3WD label correlation from the perspective of samples. According to the weights of different features on any label, the comprehensive loss of each sample to three action strategies can be measured. Obviously, the 3WD rules for any label variable in multi-label output space is obtained. By aggregating the cutting thresholds between different labels, the division principles of 3WD label correlation are further established. Given any multi-label sample, the local fuzzy membership to co-occurrence or mutual state for label pair is examined based on kernelized fuzzy rough sets. The 3WD local label relevance of each sample is confirmed, that is, positive, negative or neutral. The global application strategy for multi-label classification is utilized to avoid over-fitting induced by local mining strategy. Based on the integral mean of the distribution of 3WD local label relevance in multi-label sample space, two different versions of empirical label relevance are constructed. By constraining the relative position between sub-separation hyperplanes, the 3WD label correlation distribution-based model for multi-label joint classification is designed. The experiment results on fifteen real world multi-label datasets reflect that our algorithm achieves good classification ability and versatility. The impact of core parameters on learning performance is also dissected.
CPA-YOLOv7: Contextual and pyramid attention-based improvement of YOLOv7 for drones scene target detection

Target detection in unmanned aerial vehicle application scenarios has other problems, such as dense targets. The existing unmanned aerial vehicle target detection model with high computational complexity makes it difficult to meet real-time unmanned aerial vehicle target detection, and the detection accuracy of small targets is low. To address these problems, we propose an improved YOLOv7 small target detection model based on context and pyramidal attention that can cope with dense unmanned aerial vehicle scenarios - CPA-YOLOv7. This model embeds our proposed lightweight multi-scale attentional feature spatial pyramid pooling module, which can better distinguish between small and large target features, reducing the computational effort while improving the detection accuracy of the model. Secondly, we design a contextual dynamic fusion attention module in the network to fuse global and local contextual information and dynamically assign features to multiple groups of channels; in the multi-scale fusion process, it effectively increases the characterization ability of small target features and enables the network to better focus on small target information. Finally, we improve Wise-Intersection-over-Union loss as the regression loss function, add a moderation factor to retain some of the high and low-quality sample weights to improve the regression accuracy of high-quality anchor frames, and use the dynamic non-monotonic focusing mechanism to increase the model's focus on ordinary quality anchor frames to improve the model's localization performance and robustness to low-quality samples. Numerous experimental results show that on the unmanned aerial vehicle datasets VisDrone2021-DET and AI-TOD, the mAP values of our model are 2.3% and 1.1% higher than those of the YOLOv7 model with fewer parameters introduced, and the computational speed reaches 146 frames per second (FPS), which can meet the real-time requirements of unmanned aerial vehicle detection.
Multi-branch Segmentation-guided Attention Network for crowd counting

Crowd counting has gained increasing attention recently owing to its importance in public safety. However, it remains a challenging task due to background complexities and high annotation costs. To address these challenges, we propose the Multi-branch Segmentation-guided Attention Network (MSGANet). To deal with the complex background, we incorporate segmentation-guided attention branches into both shallow and deep layers of the baseline model, allowing simultaneous consideration of spatial and semantic information. Multi-level attention maps enable the network to minimize the influence of complex backgrounds while focusing on the regions containing crowds. To tackle the issue of costly annotations, MSGANet utilizes only point annotations to generate ground truth density and segmentation maps, eliminating additional expenses. Our results demonstrate that our approach achieves highly competitive performance compared to state-of-the-art crowd counting methods.
On One-Way Ring Homomorphisms

In this paper, we propose a new candidate for a one-way ring homomorphism induced by a one-way (non-Abelian) group homomorphism. A multi-party digital signature scheme is also given as an application of the proposed one-way ring homomorphism.