Reviewerly/exHarmony huggingface dataset:
Role of Digital Technology in Transforming Organizational Competencies Influencing Green Economy: Moderating Role of Product Knowledge Hiding

Digital technology has gained momentum in the recent decade, with its relationships with digital entrepreneurship, digital economies, digital social interaction, green economies, etc. These have changed the perspective of business and hence digitalized the strategic policies through blockchains. The current study aims to identify such benefits that have changed the day-to-day life processes and procedures for carrying out different tasks due to the convenience of adopting digital technology. Those benefits have been classified as transparency, centralization, and access to new markets for the organizations considering their consequences, especially when using digital technology. When processes are taking place online, there are fair chances of hiding knowledge about certain products or procedures to gain particular benefits. Hence, this study has considered the moderating role of product knowledge hiding while interacting online. This study is a quantitative post-positivist cross-sectional study that has followed a survey technique for data collection. The population used in this study is the managerial staff of the telecom sector in the mainland in China. The sample size used in this study is 358. The software used in this study is Smart-PLS 3.3. The technique used in this study for data analysis is structural equation modeling with measurement modeling. The findings of this study show that digital technology has led to many benefits for organizations like centralization, access to the new markets, and transparency, which have been made possible remotely only because of the use of digital technology in business operations. However, the moderating role of product knowledge hiding has been found significant only for transparency. This research paper highlights the important benefits of the use of technological use in the corporate world. Also, it contributes to expanding the network of knowledge hiding, addressing the moderation of product knowledge hiding, and extending the known consequences of digital technology influencing knowledge hiding.
A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.
Peculiarities of ICT adoption in Nigeria

From the vantage point of social change and development, studies have indicated that applying information and communication technology (ICT) in people's daily activities has enabled human beings to amazingly achieve things that were considered unattainable a couple of centuries ago.These remarkable achievements are evident in healthcare system, education, connectivity, economic and religious activities, among others.Studies have further indicated that there is some discrepancy between the extent to which the residents of the advanced countries and developing countries respectively accept ICT use in their daily activities.Hence, the sole objective of this systematic review is to investigate the current position of ICT use by the residents of Nigeria and the socio-cultural, economic, and technical factors that influence the popularity and acceptability of the ICT use in the country.To attain this objective, relevant available previous, mainly indigenous, studies that were published from the year 2015 to the year 2021 were sought and sorted, while the integration of the relevant previous qualitative, quantitative, empirical and theoretical studies were carried out thematically.This study discovered ICT use in tertiary education, maintenance of relationship, complementing healthcare system, ensuring the safety the relatives, religious and economic activities among the residents of the country.The conclusion drawn from this systematic review was that the ICTs use has been widely accepted among Nigerians in their various social activities, however, with challenges posed by several socio-cultural, economic and technical factors.
Sign Language Recognition: A Comparative Analysis of Deep Learning Models

Sign language is the primary means of communication used by deaf and dumb people. Learning this language could be perplexing for humans; therefore, it is critical to develop a system that can accurately detect sign language. The fields of deep learning and computer vision with recent advances are used to make an impact in sign language recognition with a fully automated deep learning architecture. This paper presents two models built using two deep learning algorithms; VGG-16 and convolutional neural network (CNN) for recognition and classification of hand gestures. The project aims at analysing the models’ performance quantitatively by optimising accuracy obtained using limited dataset. It aims at designing a system that recognises the hand gestures of American sign language and detects the alphabets. Both the models gave excellent results, VGG-16 being the better. VGG-16 model delivered an accuracy of 99.56% followed by CNN with an accuracy of 99.38%.
The Fusion of Eye Movement and Piezoelectric Sensing Technology Assists Ceramic Art Process Optimization and Mechanical Characterization

Ceramic product shape evaluation is an important part of product development, an important method to optimize product shape design, and is of great significance to reasonably locate users’ consumption psychology and promote the development of ceramic product industry. In this paper, we propose an eye-tracking-based evaluation method for ceramic products from the user’s point of view, in view of the fact that there are few studies on ceramic product shape evaluation, and it is mainly led by designers and enterprise leaders subjectively, with low user participation and lack of objective evaluation means and objective data support. In this paper, through the implementation of eye-movement experiments, we obtain and analyze the eye-movement data related to the semantic perception evaluation of product modeling and the overall evaluation of modeling, establish the mapping relationship between user evaluation and eye-movement data, and provide objective data support for modeling evaluation. This paper provides an objective data support for the styling evaluation. This paper provides new ideas for the ceramic product modeling evaluation method, which helps to promote the development of ceramic product industry, improve the brand recognition of enterprises, and help the marketing personnel to make reasonable marketing planning plans. For the semantic perceptual evaluation of ceramic product styling based on eye-tracking, the effectiveness of product styling design concept communication is evaluated. Ceramic products are constantly changing and developing, with new shapes appearing and old shapes being eliminated. Continual innovation and development of ceramics based on inherited traditions can give them a new look and color under the existing modeling style. Compared with other categories, although ceramic modeling has relatively abstract formal characteristics, but it is the extension of the modeling, still has obvious morphological characteristics, and the impact on people’s aesthetic mood.
Phish Derby: Shoring the Human Shield Through Gamified Phishing Attacks

To better understand employees’ reporting behaviors in relation to phishing emails, we gamified the phishing security awareness training process by creating and conducting a month-long “Phish Derby” competition at a large university in the U.S. The university’s Information Security Office challenged employees to prove they could detect phishing emails as part of the simulated phishing program currently in place. Employees volunteered to compete for prizes during this special event and were instructed to report suspicious emails as potential phishing attacks. Prior to the beginning of the competition, we collected demographics and data related to the concepts central to two theoretical foundations: the Big Five personality traits and goal orientation theory. We found several notable relationships between demographic variables and Phish Derby performance, which was operationalized from the number of phishing attacks reported and employee report speed. Several key findings emerged, including past performance on simulated phishing campaigns positively predicted Phish Derby performance; older participants performed better than their younger colleagues, but more educated participants performed poorer; and individuals who used a mix of PCs and Macs at work performed worse than those using a single platform. We also found that two of the Big Five personality dimensions, extraversion and agreeableness, were both associated with poorer performance in phishing detection and reporting. Likewise, individuals who were driven to perform well in the Phish Derby because they desired to learn from the experience (i.e., learning goal orientation) performed at a lower level than those driven by other goals. Interestingly, self-reported levels of computer skill and the perceived ability to detect phishing messages failed to exhibit a significant relationship with Phish Derby performance. We discuss these findings and describe how focusing on motivating the good in employee cyber behaviors is a necessary yet too often overlooked component in organizations whose training cyber cultures are rooted in employee click rates alone.
Parameter Fuzzy Self-Adaptive Dynamic Window Approach for Local Path Planning of Wheeled Robot

As a classic algorithm for realizing robot local path planning, the dynamic window approach (DWA) uses an objective function to choose the optimal velocity commands. However, the path generated by the DWA in the complex environment is not smooth. Therefore, this paper proposes an improved DWA algorithm to make the path of the robot more smoother when avoiding obstacles. At the same time, in view of the condition that the weight coefficients of the evaluation function of the original DWA remain unchanged, this paper will add a fuzzy controller to realize the weight coefficients adaptation, so as to adapt to a more complex environment, and the generated path can be smoother.
Insider Threat Detection Based on NLP Word Embedding and Machine Learning

The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure.Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations.Efficient insider threat detection requires stateof-the-art Artificial Intelligence models and utility.Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which are major concerns needing further investigation.In this paper, an attempt to fulfill these gaps by detecting insider threats with the novelties of the present investigation first developed two deep learning hybrid LSTM models integrated with Google's Word2vec LSTM (Long Short-Term Memory) GLoVe (Global Vectors for Word Representation) LSTM.Secondly, the performance of two hybrid DL models was compared with the state-of-the-art ML models such as XGBoost, Ada-Boost, RF (Random Forest), KNN (K-Nearest Neighbor) and LR (Logistics Regression).Thirdly, the present investigation bridges the gaps of using a real dataset, high accuracy, and significantly lower false alarm rate.It was found that ML-based models outperformed the DL-based ones.The results were evaluated based on earlier studies and deemed efficient at detecting insider threats using the real dataset.
Ships Detection in SAR Images Based on Anchor-Free Model With Mask Guidance Features

Ship targets in synthetic aperture radar (SAR) images have various scales. The detection model based on anchor boxes requires manual design of candidate boxes, which are fixed and cannot completely match all kinds of targets. Instead, large of anchor boxes with different sizes also result in large amounts of computing resources being consumed. Another potential issue comes from complex background information of near-coast scenes, which leads to ship targets being unrecognized because the background contains similar appearing objects. Therefore, this article proposes an anchor-free detection model based on mask guidance features, which achieves detection mainly through three modifications. First, feature maps of multiple scales are fused to obtain high-resolution feature maps containing rich semantic information. Second, a transformer encoder module is introduced to focus on the context relationship between the target object and the global image and to enhance the dependence between ship targets. Third, the mask guide feature is used to highlight the positions of the target in the feature map, and a loss function in the mask guide mechanism is designed to optimize the mask feature map to reduce false detections and missed detections. Testing the model on the public dataset SAR ship detection dataset, the model's detection accuracy reached 96.17%, with its accuracy on small-size ships reaching 96.11% and 97.84% on large ships.
Quantum Mathematics in Artificial Intelligence

In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.
Improved Arabic Alphabet Characters Classification Using Convolutional Neural Networks (CNN)

Handwritten characters recognition is a challenging research topic. A lot of works have been present to recognize letters of different languages. The availability of Arabic handwritten characters databases is limited. Motivated by this topic of research, we propose a convolution neural network for the classification of Arabic handwritten letters. Also, seven optimization algorithms are performed, and the best algorithm is reported. Faced with few available Arabic handwritten datasets, various data augmentation techniques are implemented to improve the robustness needed for the convolution neural network model. The proposed model is improved by using the dropout regularization method to avoid data overfitting problems. Moreover, suitable change is presented in the choice of optimization algorithms and data augmentation approaches to achieve a good performance. The model has been trained on two Arabic handwritten characters datasets AHCD and Hijja. The proposed algorithm achieved high recognition accuracy of 98.48% and 91.24% on AHCD and Hijja, respectively, outperforming other state-of-the-art models.
Phrase Retrieval Learns Passage Retrieval, Too

Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems.Among them, dense phrase retrieval-the most fine-grained retrieval unit-is appealing because phrases can be directly used as the output for question answering and slot filling tasks. 1 In this work, we follow the intuition that retrieving phrases naturally entails retrieving larger text blocks and study whether phrase retrieval can serve as the basis for coarse-level retrieval including passages and documents.We first observe that a dense phrase-retrieval system, without any retraining, already achieves better passage retrieval accuracy (+3-5% in top-5 accuracy) compared to passage retrievers, which also helps achieve superior end-to-end QA performance with fewer passages.Then, we provide an interpretation for why phrase-level supervision helps learn better fine-grained entailment compared to passage-level supervision, and also show that phrase retrieval can be improved to achieve competitive performance in document-retrieval tasks such as entity linking and knowledge-grounded dialogue.Finally, we demonstrate how phrase filtering and vector quantization can reduce the size of our index by 4-10x, making dense phrase retrieval a practical and versatile solution in multi-granularity retrieval. 2
Smart Contracts and Supply Chain Management Using Blockchain

With a Fourth Industrial Revolution making inroads, encompassing all sectors of the industry with numerous concepts of disruptive technology such as Artificial Intelligence, Blockchain, Virtual and Augmented Reality, Internet of Things (IoT), Robotics and 3D Printing we have barely scratched the surface of its Scope, Implication, and Applications in various branches of the Construction Industry. This research aims to investigate the potential of Blockchain technology in the context of Smart Contracts and Supply chain management (SCM) applications in the construction sector. Most of the time, cost overruns have been attributed to the sluggish pace of contractual impediments or inefficacy of material and machinery procurement processes. The executive of material and data stream is a crucial need for development organizations. Effective execution in these regions can give significant benefits and permit more main incentives for customers. The industry is regularly censured for being delayed to grasp the change it needs and at the exact moment has been referred to as ready for the interruption because of the intricacy of work and a sheer number of guidelines and gauges, trust and check issues in regards to consistency to work principles still loom to a great extent. The momentum built by BIM infusion in the industry could be leveraged to bring Blockchain technology to the fore to mitigate Information Asymmetry by -distributing information to generate decentralized consensus building among various stakeholders involved.
Exploring Romanian Engineering Students’ Perceptions of Covid-19 Emergency e-Learning Situation. A Mixed-Method Case Study

The Covid-19 pandemic is the most disturbing event in the lifetime of most of our planet’s citizens. The lockdown measures directly impacted many areas of our lives, including the educational sector, because locking down countries meant implicitly locking down the educational system. Moreover, what was first considered a temporary solution for an extraordinary situation began to look more and more like a medium to long-term general rule. Nevertheless, the questions are: are we all ready to move the entire educational process online and fully understand the challenges and implications for all stakeholders involved? This two-part research aims to provide some answers to these questions by identifying and analyzing the perceptions of Romanian engineering students enrolled at “Gheorghe Asachi” Technical University of Iasi (TUIASI) regarding the changes registered in the past year once the emergency e-learning situation started. The first part of the research was conducted between April and May 2020 through an online survey among 134 engineering students. It aimed at identifying the students’ perception of the online learning systems provided by their university, considering the significant speed with which changes were imposed. In addition, this research phase focused on students’ access to resources and knowledge to use and integrate online learning into their study routine. The second part of the research was carried out after almost a year of e-learning between March and April 2021 and consisted of six online focus groups with 36 students and aimed at identifying the main advantages and challenges students experience throughout the online educational process. The research revealed that although students are digital natives, they still have difficulties harnessing e-learning’s advantages and integrating them into their study routine. Another significant aspect refers to the changing role of the professor perceived not only as an instructor but as a mentor during a time of crisis. The study results can offer higher education institutions insight and valuable information that can be used in designing and implementing online and hybrid activities and classes that better fit the students’ needs and expectations in terms of e-learning.
Sustainable Finance for Promoting Inclusive Growth

Various essential things will be revealed in this research on how sustainable financial performance promotes inclusive growth when the Indonesian state is experiencing a sizeable economic impact due to the covid-19 pandemic. Independent variables used to measure inclusive growth include divisia of money, fiscal policy, and green banking, while the dependent variable is inclusive growth. Multiple regression analysis was used for statistical data on developments for the last ten years per semester. The data collection by downloading statistical information and annual financial report data and analyzed using SPSS Version 20. This study indicates that divisia of money, fiscal policy, and green banking positively affect inclusive growth. The collaboration of various elements will benefit inclusive and equitable economic growth, providing opportunities for all levels of society to take part in decision-making and the use of resources. Green banking is expected to improve its reputation in the eyes of customers for its sustainability.