Reviewerly/exHarmony huggingface dataset:
Factors Influencing University Students' Coastal Ecology and Environmental-friendly Entrepreneurship in Coastal Universities

Wang, J. and Peng, C., 2020. Factors influencing university students' coastal ecology and environmental-friendly entrepreneurship in coastal universities. In: Wan, Z. (ed.), Coordinated Sustainable Development in Coastal Areas: Environment and Economy. Journal of Coastal Research, Special Issue No. 109, pp. 57–64. Coconut Creek (Florida), ISSN 0749-0208.The relationship between personal previous experience and willingness to start a environmental-friendly business in coastal areas is not significant (r = 0.069, p>0.05). There are significant positive correlations between regulatory system and coastal environmental-friendly protection entrepreneurial intention (r = 0.315, P < 0.01), normative system and coastal environmental-friendly entrepreneurial intention (r = 0.282, P<0.01), and cognitive system and coastal environmental-friendly entrepreneurial intention (r = 0.273, P < 0.01). Botstrap method is used to test the mediating effect in this study. The mediating effect of coastal environmental-friendly entrepreneurial self-efficacy is not significant. Measures such as policy support and awareness education should be effective to promote student's green behavior and entrepreneurship.
Trajectory-based Cognitive Recognition of Dynamic Hand Gestures from Webcam Videos

An online description of the hand and detection of the centroid of a hand movement are critical requirements to trace the dynamic hand gestures.The hand acquires very small area in the image frame, and due to its nonrigid nature, and random behavior in movement, the quality of images in the video are affected considerably, if the movement is recorded from lowresolution camera for e.g.webcam.The image quality further reduces by the real-time factors associated with the background and thus, hand detection and its localization become a challenge in webcam videos.These challenges compel researchers either to work with static hand postures or to use advance sensor-based cameras.In this paper we have proposed a novel method to recognize and plot the trajectory of dynamic hand gestures directly in true color videos acquired through webcam.The cognitive learning of Scale-Invariant Feature Transform (SIFT) features of active hand template in each consecutive frames of the video help in tracing the path of hand movement without background subtraction or involving segmentation process.The determination of active hand template using Chen-Vese model makes our method invariant to the hand posture used by the user to perform his hand gesture.To test the efficiency of the methodology we have generated three different real-time common scenarios for a user to perform his hand movement.The empirical results obtained in different experiments demonstrate that the approach can withstand the challenges associated with the detection and tracking dynamic hand gestures when recorded from a simple webcam.
Dynamic Access Control In Iot: Monitoring User Behavior Using Smart Contracts

Dynamic Access Control in IoT (Internet of Things) through monitoring user behavior is a procedure that defines access control theory. Usually, an institution provides services to a user who owns a specific role that was issued by another institution. Such access control methods are possible in man-to-man communications only, but not in computer networks, because it is hard to establish security and limit spiteful actions through the users with high-risk values. High-risk values are caused by the users who do the tasks which they are not intended to do. In this paper, we bring forward a technique to limit malicious activities by monitoring user behavior using smart contracts. This proposed technique uses blockchain based technology and smart contracts for trust and the confirmation of user's possession. We described the substructure, containing two main parts: smart contract and an authentication arrangement protocol.
Robust Beam Search for Encoder-Decoder Attention Based Speech Recognition Without Length Bias

As one popular modeling approach for end-to-end speech recognition, attention-based encoder-decoder models are known to suffer the length bias and corresponding beam problem.Different approaches have been applied in simple beam search to ease the problem, most of which are heuristic-based and require considerable tuning.We show that heuristics are not proper modeling refinement, which results in severe performance degradation with largely increased beam sizes.We propose a novel beam search derived from reinterpreting the sequence posterior with an explicit length modeling.By applying the reinterpreted probability together with beam pruning, the obtained final probability leads to a robust model modification, which allows reliable comparison among output sequences of different lengths.Experimental verification on the LibriSpeech corpus shows that the proposed approach solves the length bias problem without heuristics or additional tuning effort.It provides robust decision making and consistently good performance under both small and very large beam sizes.Compared with the best results of the heuristic baseline, the proposed approach achieves the same WER on the 'clean' sets and 4% relative improvement on the 'other' sets.We also show that it is more efficient with the additional derived early stopping criterion.
Fuzzy Keywords-Driven Public Sports Resource Allocation Strategies Retrieval With Privacy-Preservation

Currently, the government proposes various public sports resource allocation strategies to promote the construction of public sports in the local. Thus, searching for the document database becomes an economic and efficient way for government officials to understand the contents of various public sports resource allocation strategies. As the government officials lack background knowledge of public sports resource allocation, the government officials are hard to retrieve appropriate documents. What's more, the document database is generally unwilling to share all text document information with the government official as some documents contain sensitive data information. Considering the above drawbacks, we put forward a fuzzy keywords-driven public sports resource allocation strategies retrieval approach based on the Simhash (named PSRAS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+S</sub> ) in the paper. The retrieval approach can accurately recommend appropriate documents to the government officials, by using the text description mining of documents and the fuzzy keywords search technology. Furthermore, the retrieval process is not revealing the sensitive data information of the irrelevant public sports resource allocation strategy document to the government officials, so the retrieval process can achieve local information protection mechanism. Finally, a case study explains the feasibility and effectiveness of our retrieval approach step by step.
Spectral Clustering and Cost-Sensitive Deep Neural Network-Based Undersampling Approach for P2P Lending Data

Peer-to-peer lending, also known as P2P lending, is the new generation loan disbursement process, where lenders and borrowers communicate through online services. Loans through P2P lending platforms are generally unsecured, due to the presence of borrowers with low credit scores. Lendingclub dataset, consisting of quantitative and qualitative information of borrowers from 2007 to 2011, is taken for the research. Machine learning models trained with such imbalanced dataset consists of biasing towards major class samples. The model performs significantly well on major class (safe borrowers) in terms of high precision but does not perform significantly well on minor class (defaulted) borrowers and provides low recall on minor class samples. To deal with the issue, a novel undersampling algorithm based on the combination of spectral clustering and cost sensitive deep neural network (SCCSDNN) is proposed. Experimental results showcased the outstanding performance of the proposed technique, and it outperforms state of the art undersampling, oversampling and ensemble resampling techniques.
The Effect of the Use and Knowledge of AI on the Advanced Entrepreneurship in Saudis Small Business and Startups

The study aims to study the actual use of the AI (Artificial intelligence) among small businesses in Saudi Arabia, and the effect of this use and the knowledge on the advanced entrepreneurship in these businesses. The actual use and knowledge were measured, along with the effect of these variables on the advanced entrepreneurship. The sample chosen was 204 small businesses and startups from three main cities in KSA. The sample was mostly of technology related businesses 45%, and others as of service related and manufacturing simple products. The results show a lack of both the actual use of AI technology and knowledge of this technology. The results also show this lack is affecting the entrepreneurship of theses business and their abilities to grow with a sense of creativity in the local market. The research demonstrates the need for AI technology to strength the entrepreneurship among small business in Saudi Arabia. The research results close a gap in the available literature of small business use of technology and demonstrating advanced entrepreneurship in Kingdom of Saudi Arabia.&#x0D; &#x0D; Further research is needed to understand the reasons of the lack of the use of AI among these businesses in Saudi Arabia.
Deeply understanding graph-based Sybil detection techniques via empirical analysis on graph processing

Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graphbased Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.
Developing a POS Tagged Corpus of Urdu Tweets

Processing of social media text like tweets is challenging for traditional Natural Language Processing (NLP) tools developed for well-edited text due to the noisy nature of such text. However, demand for tools and resources to correctly process such noisy text has increased in recent years due to the usefulness of such text in various applications. Literature reports various efforts made to develop tools and resources to process such noisy text for various languages, notably, part-of-speech (POS) tagging, an NLP task having a direct effect on the performance of other successive text processing activities. Still, no such attempt has been made to develop a POS tagger for Urdu social media content. Thus, the focus of this paper is on POS tagging of Urdu tweets. We introduce a new tagset for POS-tagging of Urdu tweets along with the POS-tagged Urdu tweets corpus. We also investigated bootstrapping as a potential solution for overcoming the shortage of manually annotated data and present a supervised POS tagger with an accuracy of 93.8% precision, 92.9% recall and 93.3% F-measure.
Developing smart edutainment for preschoolers: a multidisciplinary approach

Education has suffered multiple changes due to technological progress. Even though the current generation of preschoolers (aged 3 to 6 years in our country) is called digital native, there is a lack of focus on introducing technology enabled learning tools for them. This paper presents our approach to designing smart learning experiences for a fringe users group, the preschoolers. We present our method proposal for designing edutainment applications for preschoolers, based on a User Centered Design process and how we may integrate Artificial Intelligence to complete and support the capabilities of our little users.
Sentiment Analysis on KAI Twitter Post Using Multiclass Support Vector Machine (SVM)

Information in form of unstructured texts is increasing and becoming commonplace for its existence on the internet. This information is easily found and utilized by business people or companies through social media. One of them is Twitter. Twitter is ranked 6th as a social media that is widely accessed today. The use of Twitter has the disadvantage of unstructured and large data. Consequently, it is difficult for business people or companies to know opinion towards service with limited resources. To Make it easier for businesses know the public's sentiment for better service in the future, public sentiment on Twitter needs to be classified as positive, neutral, and negative. The Multiclass Support Vector Machine (SVM) method is a supervised learning classification method that handles three classes classification. This paper uses One Against All (OAA) approach as a method to determine the class. This paper contains the results of classifying OAA Multiclass SVM methods with five different weighting features unigram, bigram, trigram, unigram+ bigram, and word cloud for analyzing tweet data, finding the best accuracy and important feature when processed with large data. The highest accuracy is the unigram TF-IDF model combined with the OAA Multiclass SVM with gamma 0.7 is 80.59.
Security Measurement in Industrial IoT with Cloud Computing Perspective: Taxonomy, Issues, and Future Directions

In recent years, cloud computing has gained massive popularity in information technology and the industrial Internet of things. It provides facilities to the users over the wireless channel. Many surveys have been carried out in cloud security and privacy. The existing survey papers do not specify the classifications on the basis of cloud computing components. Therefore, they fail to provide the techniques with their specialities as well as the previously available literature review is outdated. This paper presents the security for cloud computing models with a new aspect. Unlike the previously existing surveys, the literature review of this paper includes the latest research papers in the field of cloud security. Also, different classifications are made for cloud computing security on the basis of different cloud components that are used to secure the cloud models. Furthermore, a total of eleven (11) classifications are considered, which includes cloud components to secure the cloud systems. These classifications help the researchers to find out the desired technique used in a specific component to secure the cloud model. Moreover, the shortcoming of each component enables the researchers to design an optimal algorithm. Finally, future directions are given to highlight future research challenges that give paths to researchers.
Modelling The Learner Model Based Ontology In Adaptive Learning Environment

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research. Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits.Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Model
Optimization of 3D Triangular Mesh Watermarking Using ACO-Weber’s Law

The development of new multimedia techniques such as 3D printing is increasingly attracting the public's attention towards 3D objects.An optimized robust and imperceptible watermarking method based on Ant Colony Optimization (ACO) and Weber Law is proposed for 3D polygonal models.The proposed approach partitions the host model into smaller sub meshes and generates a secret watermark from the sub meshes using Weber Law.ACO based optimized strength factor is identified for embedding the watermark.The secret watermark is embedded and extracted on the wavelet domain.The proposed scheme is robust against geometric and photometric attacks that overcomes the synchronization problem and authenticates the secret watermark from the distorted models.The primary characteristic of the proposed system is the flexibility achieved in data embedding capacity due to the optimized strength factor.Extensive simulation results shows enhanced performance of the recommended framework and robustness towards the most common attacks like geometric transformations, noise, cropping, mesh smoothening, and the combination of such attacks.
Contribution of Conceptual Modeling to Enhancing Historians’ Intuition - Application to Prosopography

Historians, and in particular researchers in prosopography, focus a lot of effort on extracting and coding information from historical sources to build databases. To deal with this situation, they rely in some cases on their intuition. One important issue is to provide these researchers with the information extracted from the sources in a sufficiently structured form to allow the databases to be queried and to verify, and possibly, to validate hypotheses. The research in this paper attempts to take up the challenge of helping historians capturing and assessing information throughout automatic processes. The issue emerges when too many sources of uncertain information are available. Based on the high-level information fusion approach, we propose a process that automatically supports historians’ intuition in the domain of prosopography. The contribution is threefold: a conceptual data model, a process model, and a set of rules combining the reliability of sources and the credibility of information.
Neural Discriminant Analysis for Deep Speaker Embedding

Probabilistic Linear Discriminant Analysis (PLDA) is a popular tool in open-set classification/verification tasks. However, the Gaussian assumption underlying PLDA prevents it from being applied to situations where the data is clearly non-Gaussian. In this paper, we present a novel nonlinear version of PLDA named as Neural Discriminant Analysis (NDA). This model employs an invertible deep neural network to transform a complex distribution to a simple Gaussian, so that the linear Gaussian model can be readily established in the transformed space. We tested this NDA model on a speaker recognition task where the deep speaker vectors (x-vectors) are presumably non-Gaussian. Experimental results on two datasets demonstrate that NDA consistently outperforms PLDA, by handling the non-Gaussian distributions of the x-vectors.
Exploring the Documentation and Preservation of African Indigenous Knowledge in a Digital Lexical Database

Transcending the boundaries of printed lexicographic resources is becoming easier in the digital age, with e-resources facilitating restrictions on the size and type of information that can be included. In this article we explore innovative ways of documenting and preserving African indigenous knowledge, often underrepresented in traditional dictionaries, in an existing digital lexical database. Our approach is based on the extension of the African Wordnet, a lexical database under construction for nine African languages, in this case applied to isiZulu. This article addresses the challenge of consolidating dispersed indigenous knowledge collected from a variety of sources such as conventional dictionaries, interdisciplinary publications and a flat-structured online database, in a digitised hierarchical wordnet structure. A representative sample of traditional domestic utensils in Zulu culture is used to demonstrate the conversion into a set of typical semantic relations in a wordnet structure. By focusing on filling lexical gaps between isiZulu and English as found in the Princeton WordNet, with culturally relevant synsets, the African Wordnet also becomes a useful resource for natural language processing. Finally, it is shown how the hierarchical classification of selected domestic utensils is visually presented in wordnet graphs in the WordnetLoom interface.
Forming readiness for communication in innovative engineering and scientific research activity among technical university students by means of a foreign language

The relevance of the study is caused by the fact that readiness for foreign language communication in innovative engineering and scientific research activity is one of the major components of specialist’s readiness to create innovative products, processes, services and their commercialization. Forming the content of the discipline «Foreign language» (English) for professionally oriented foreign language teaching for students of different types of training at a technical university is becoming more relevant. Applied research in the field of linguistics was conducted and the terminology for innovative and scientific research activity was defined (the level of education – baccalaureate). The algorithm of selecting lexical minimum was used on the basis of checking the frequency of using lexical units. There were 21 people in the control group and 21 people in the experimental group in the research. Pearson criterion χ2 was used to evaluate statistical significance of differences between the groups. It was found that using the universal module «Innovation and types of research» led to significant growth of students' level of foreign language proficiency in the experimental group comparing with the one in the control group (χ2 = 6,17; p = 0,05). The universal module developed can be used for different types of training at a university. The research represents practical value for specialists who deal with developing content, technologies, services, effective management for professionally oriented teaching of the discipline «Foreign language» (English) (the level of education – baccalaureate) for both full-time and distance education and foreign language teachers.
Premises Reduction of Rule-Based Expert Systems Using Association Rules Technique

The heart of expert system is the knowledge base that determines the power of expert system and search engine space. The one important form of this knowledge is the production rule. The premises of these rules are the heart of production rules, therefore, the reduction of these premises is very useful to reduce the time and space in search engine. In this paper an approach will be presented to reduce the premises of production rules using association rules algorithm especially Apriori algorithm. Applying this approach gives a logical premise reduction which plays a good role in the search engine.