bluuebunny/crossref_metadata_2025 huggingface dataset:
Istidraj dalam al-Quran Perspektif Imam al-Qurthubi
Authors: Febriani Dina Fitri , Zubir Muhammad
Published: July 2020

Pokok permasalahan dalam penelitian ini adalah penafsiran ayat-ayat tentang istidraj dalam kitab-kitab tafsir masih secara umum, sehingga pemahaman orang tentang konsep istidraj masih belum komprehensif dan diperlukan adanya penafsiran dengan metode tematik perspektif tokoh mufassir, supaya pemahaman tentang istidraj bisa dipahami secara jelas. Tujuan penelitian ini adalah untuk mengkaji bagaimana pemahaman istidraj dalam al-Quran perspektif penafsiran Imam al-Qurthubi dalam kitab tafsir al-Jami’ li Ahkam al-Qur’an. Metode analisis yang digunakan adalah metode maudhu’i. Hasil penelitian ini adalah bahwa istidraj dalam al-Quran semakna dengan beberapa kata lain seperti al-makr, al-khid’ah dan al-imla’. Dan istidraj menurut Imam al-Qurthubi yaitu setiap kali seseorang hamba melakukan suatu kemaksiatan yang baru, seketika itu pula Allah Swt. menambahkan kepada mereka nikmat, sehingga mereka larut di dalamnya dan tidak menyadari bahwa sebenarnya nikmat tersebut bukan karena kasih sayang Allah, melainkan hanya sebagai alat untuk menghukum mereka, kemudian pada akhirnya mereka diazab dengan azab yang pedih.
PERGESERAN SISTEM PERNIKAHAN ENDOGAMI MASYARAKAT ETNIS BUGIS
Authors: Fitriana A. Dian , Nisa' Khaerun
Published: July 2020

Endogamy or marriage in family clumps or known as in-marriage is one way to maintain kinship relations in Bugis society. The purpose of this research is to know the development of the endogamous marriage system adopted by Bugis ethnic society to the present. This research uses qualitative method with fenomenology approach. The subject of this research consisted of six pairs of husband and wife who had mixed ethnic marriages. The results showed that the contemporary Bugis extended the boundaries of endogamy, from within close and extended families (cousins) to Bugis from the same village, and eventually to ethnic Bugis generally. But over the times, technology and time have been able to erode the endogamous marriage system that was originally adopted by the Bugis ethnic society. There are several reasons that affect Bugis endogamous marriage system, such as the background of parents who choose to marry different cultures, gender (males who have more influence to determine the spouse), characters and prospective spouse's work.
TOWARDS A THEORY OF DUNG CULTURE: AN INNER ASIAN CASE STUDY
Authors: Bao Haiyan
Published: July 2020

Research on milk, meat, fur, bone and animal power predominates in theories of livestock resources. However, research on livestock dung, an important livestock resource that is produced regardless of the age and sex of the animal, and is a continuous and stable resource in terms of quantity, has been neglected. From the perspective of cultural anthropology, based on archival and ethnographic research, this paper examines livestock dung culture in terms of dung gathering, utilisation, and naming systems. It is argued that a theory of dung culture will provide an insight into the formation of pastoralism and the origin of domestication.
Screening and identification of key biomarkers in alimentary tract cancers: A bioinformatic analysis
Authors: Cai Zeling , Wei Yi , Chen Shuai , Gong Yu , Fu Yue , Dai Xianghua , Zhou Yan , Yang Haojun , Tang Liming , Liu Hanyang
Published: July 2020

BACKGROUND: Alimentary tract cancers (ATCs) are the most malignant cancers in the world. Numerous studies have revealed the tumorigenesis, diagnosis and treatment of ATCs, but many mechanisms remain to be explored. METHODS: To identify the key genes of ATCs, microarray datasets of oesophageal cancer, gastric cancer and colorectal cancer were obtained from the Gene Expression Omnibus (GEO) database. In total, 207 differentially expressed genes (DEGs) were screened. KEGG and GO function enrichment analyses were conducted, and a protein-protein interaction (PPI) network was generated and gene modules analysis was performed using STRING and Cytoscape. RESULTS: Five hub genes were screened, and the associated biological processes indicated that these genes were mainly enriched in cellular processes, protein binding and metabolic processes. Clinical survival analysis showed that COL10A1 and KIF14 may be significantly associated with the tumorigenesis or pathology grade of ATCs. In addition, relative human ATC cell lines along with blood samples and tumour tissues of ATC patients were obtained. The data proved that high expression of COL10A1 and KIF14 was associated with tumorigenesis and could be detected in blood. CONCLUSION: In conclusion, the identification of hub genes in the present study helped us to elucidate the molecular mechanisms of tumorigenesis and identify potential diagnostic indicators and targeted treatment for ATCs.
Hybrid plagiarism detection method for French language
Authors: Elamine Maryam , Mechti Seifeddine , Belguith Lamia Hadrich
Published: July 2020

With the growth of the content found throughout the Web, every information can be plagiarized. Plagiarism is the process of using the ideas of another without naming the source. Consequently, plagiarism detection is necessary but complicated as it is often facing significant challenges given the large amount of material on the World-wide-web and the limited access to a substantial part of them. In this paper, we present a novel plagiarism detection method for French documents. The proposed method combines the intrinsic and extrinsic aspects for plagiarism detection. We achieved good results with both approaches. For the extrinsic method, we achieved an accuracy of 62% for the first tests of the method. As for the intrinsic, we achieved an F-score of 0.328.
Semantic process mining: A conceptual application of main tools, framework and model analysis
Authors: Okoye Kingsley
Published: July 2020

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.
Automatic lung segmentation in CT images using mask R-CNN for mapping the feature extraction in supervised methods of machine learning using transfer learning
Authors: Souza Luís Fabrício , Holanda Gabriel , Silva Francisco Hércules , Alves Shara Shami , Filho Pedro Pedrosa
Published: July 2020

According to the World Health Organization, severe lung pathologies bring about 250,000 deaths each year, and by 2030 it will be the third leading cause of death in the world. The usage of (CT) Computed Tomography is a crucial tool to aid medical diagnosis. Several studies, based on the computer vision area, in association with the medical field, provide computational models through machine learning and deep learning. In this study, we created a new feature extractor that works as the Mask R-CNN kernel for lung image segmentation through transfer learning. Our approaches minimize the number of images used by CNN’s training step, thereby also decreasing the number of interactions performed by the network. The model obtained results surpassing the standard results generated by Mask R-CNN, obtaining more than 99% about the metrics of real lung position on CT with our best model Mask + SVM, surpassing methods in the literature reaching 11 seconds for pulmonary segmentation. To present the effectiveness of our approach also in the generalization of models (methods capable of generalizing machine knowledge to other different databases), we carried out experiments also with various databases. The method was able, with only one training based on a single database, to segment CT lung images belonging to another lung database, generating excellent results getting 99% accuracy.
Deep support vector neural networks
Authors: Díaz-Vico David , Prada Jesús , Omari Adil , Dorronsoro José
Published: July 2020

Kernel based Support Vector Machines, SVM, one of the most popular machine learning models, usually achieve top performances in two-class classification and regression problems. However, their training cost is at least quadratic on sample size, making them thus unsuitable for large sample problems. However, Deep Neural Networks (DNNs), with a cost linear on sample size, are able to solve big data problems relatively easily. In this work we propose to combine the advanced representations that DNNs can achieve in their last hidden layers with the hinge and ϵ insensitive losses that are used in two-class SVM classification and regression. We can thus have much better scalability while achieving performances comparable to those of SVMs. Moreover, we will also show that the resulting Deep SVM models are competitive with standard DNNs in two-class classification problems but have an edge in regression ones.
Improving multi-class Boosting-based object detection
Authors: Buenaposada José Miguel , Baumela Luis
Published: July 2020

In recent years we have witnessed significant progress in the performance of object detection in images. This advance stems from the use of rich discriminative features produced by deep models and the adoption of new training techniques. Although these techniques have been extensively used in the mainstream deep learning-based models, it is still an open issue to analyze their impact in alternative, and computationally more efficient, ensemble-based approaches. In this paper we evaluate the impact of the adoption of data augmentation, bounding box refinement and multi-scale processing in the context of multi-class Boosting-based object detection. In our experiments we show that use of these training advancements significantly improves the object detection performance.
Human-robot interaction in Industry 4.0 based on an Internet of Things real-time gesture control system
Authors: Roda-Sanchez Luis , Olivares Teresa , Garrido-Hidalgo Celia , de la Vara José Luis , Fernández-Caballero Antonio
Published: July 2020

In the context of fast-growing digitization of industrial environments, Industry 4.0 aims to improve key elements to achieve more efficient processes, flexibility in customizing products and reduction in energy consumption, among other objectives. This paper presents a system that exploits the Internet of Things (IoT), massive data computation, and human-robot collaboration to reach these goals. The described system combines technological and human-centered aspects to enhance human-robot interaction. In fact, the human factor cannot be left aside when technological advances affecting society are foreseen. The proposal has been tested on a gesture control system that enables a natural interaction with a robotic arm through the use of IoT-oriented inertial measurement unit devices. These devices capture the movements of both human’s arms. Experiments of a technical nature have been run to measure accuracy and latency. In addition, human-centered tests have been conducted with real users to determine the level of intuitiveness and acceptance of the proposed gesture control. The results obtained demonstrate that the proposal meets the demands in terms of real-time, success rate, flexibility and scalability, which are fundamental requirements in Industry 4.0. The usability results have enabled drawing useful conclusions on the use of such human-robot interaction systems.
Shallow buried improvised explosive device detection via convolutional neural networks
Authors: Colreavy-Donnelly Simon , Caraffini Fabio , Kuhn Stefan , Gongora Mario , Florez-Lozano Johana , Parra Carlos
Published: July 2020

The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method.
Effects of Palmitoylethanolamide Combined with Luteoline on Frontal Lobe Functions, High Frequency Oscillations, and GABAergic Transmission in Patients with Frontotemporal Dementia
Authors: Assogna Martina , Casula Elias Paolo , Borghi Ilaria , Bonnì Sonia , Samà Domenico , Motta Caterina , Di Lorenzo Francesco , D’Acunto Alessia , Porrazzini Francesco , Minei Marilena , Caltagirone Carlo , Martorana Alessandro , Koch Giacomo
Published: July 2020

Background: Frontotemporal dementia (FTD) is a presenile neurodegenerative disease for which there is no effective pharmacological treatment. Recently, a link has been proposed between neuroinflammation and FTD. Objective: Here, we aim to investigate the effects of palmitoylethanolamide (PEA) combined with luteoline (PEA-LUT), an endocannabinoid with anti-inflammatory and neuroprotective effects, on behavior, cognition, and cortical activity in a sample of FTD patients. Methods: Seventeen patients with a diagnosis of probable FTD were enrolled. Cognitive and neurophysiological evaluations were performed at baseline and after 4 weeks of PEA-LUT 700 mg×2/day. Cognitive effects were assessed by Neuropsychiatric Inventory (NPI), Mini-Mental State Examination, Frontal Assessment Battery (FAB), Screening for Aphasia in Neurodegeneration, Activities of Daily Living-Instrumental Activities of Daily Living, and Frontotemporal Lobar Degeneration-modified Clinical Dementia Rating scale. To investigate in vivo neurophysiological effects of PEA-LUT, we used repetitive and paired-pulse transcranial magnetic stimulation (TMS) protocols assessing LTP-like cortical plasticity, short-interval intracortical inhibition, long-interval intracortical inhibition (LICI), and short-latency afferent inhibition. Moreover, we used TMS combined with EEG to evaluate the effects on frontal lobe cortical oscillatory activity. Results: Treatment with PEA-LUT was associated with an improvement in NPI and FAB scores. Neurophysiological evaluation showed a restoration of LICI, in particular at ISI 100 ms, suggesting a modulation of GABA(B) activity. TMS-EEG showed a remarkable increase of TMS-evoked frontal lobe activity and of high-frequency oscillations in the beta/gamma range. Conclusion: PEA-LUT could reduce behavioral disturbances and improve frontal lobe functions in FTD patients through the modulation of cortical oscillatory activity and GABA(B)ergic transmission.
Probiotic Bifidobacterium breve in Improving Cognitive Functions of Older Adults with Suspected Mild Cognitive Impairment: A Randomized, Double-Blind, Placebo-Controlled Trial
Authors: Xiao Jinzhong , Katsumata Noriko , Bernier Francois , Ohno Kazuya , Yamauchi Yuki , Odamaki Toshitaka , Yoshikawa Kenji , Ito Kumie , Kaneko Toshiyuki
Published: July 2020

Background: Probiotics use has been associated with modulation of inflammation and considered as a possible intervention for CNS diseases such as mild cognitive impairment (MCI) and dementia. Objective: We aimed to test the effect of the probiotic strain, Bifidobacterium breve A1 (MCC1274), to restore cognition in a physically healthy, suspected MCI population. Methods: In this randomized, double-blind, placebo-controlled trial, 80 healthy older adults suffering from MCI were divided into two even groups to receive once daily either probiotic (B. breve A1, 2×1010 CFU) or placebo for 16 weeks using a computer-generated algorithm. Cognitive functions were assessed by the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Japanese version of the MCI Screen (JMCIS) tests before and after the study as primary and secondary endpoints, respectively. Results: 79 participants completed the study, and no adverse events were observed. RBANS total score was significantly improved in probiotic group compared with placebo (mean between-group difference 11.3 [95% CI 6.7 to 15.8]; p < 0.0001) after 16 weeks of consumption, in particular with significant improvement in domain scores of immediate memory, visuospatial/constructional, and delayed memory (p < 0.0001), in both intention-to-treat (ITT) analysis and per-protocol (PP) analysis. JMCIS score was also improved versus placebo in ITT analysis (p = 0.052) and PP analysis (p = 0.036). Conclusion: Study results indicate B. breve A1 is a safe and effective approach for improving memory functions of suspected MCI subjects.
Purple Queen® fruits of Punica granatum L.: Nutraceutical properties and unconventional growing substrates
Authors: Balli Diletta , Tozzi Francesca , Khatib Mohamad , Adessi Alessandra , Melgarejo Pablo , Masciandaro Grazia , Giordani Edgardo , Innocenti Marzia , Mulinacci Nadia
Published: July 2020

BACKGROUND: Purple Queen® is an early ripening pomegranate cultivar growing well in soilless cultivation. Substrates have direct effects on plant development and, due to progressive peatland exhaustion, the request for new materials for plant growth is high. OBJECTIVE: The objective of this work was to verify the effects of a new potential substrate obtained from the remediation of marine port sediments on the nutraceutical profile of Purple Queen® fruits, using 50% and 100% of reclaimed sediment. METHODS: The study was carried out determining ellagitannins and polysaccharides obtained from peel after decoction, and anthocyanins in aril juice on fruits from two agronomic seasons. RESULTS: The presence of 100% of the sediment had a partial detrimental effect on fruits size and weight. Compared to a peat-based commercial substrate (control), remediated sediment preserved ellagitannin content and increased the anthocyanin amount (up to 40% higher) and polysaccharide content up to 34% on dry fruit. High molecular weight polysaccharides (>2000 kDa) were identified in all the fruits with glucose and galacturonic acid as the major sugars. CONCLUSIONS: Fruits from plants cultivated only on sediment or in a mixture with 50% of peat showed significant increases of bioactive compounds as stress response in two consecutive seasons.
Comparison between different licensing schemes in a Stackelberg model when the follower is the innovator
Authors: Bode Oana R. , Ferreira Flávio , Ferreira Fernanda A.
Published: July 2020

In the present paper we consider a differentiated-good Stackelberg model, when the follower firm engages in an R&D process that gives an endogenous cost-reducing innovation. We assume that there can exist a technology transfer between innovator and non-innovator firm. The aim of this paper is three-fold: to do a comparison of the fixed-fee and royalty licensing cases, of the two-part tariff and royalty licensing cases, respectively of the two-part tariff and fixed-fee licensing cases, in order to state in which case is indicated for the innovator firm to license its technology. This comparison allows us to identify which licensing scheme is more profitable for the innovator (follower firm).
A group authentication and privacy-preserving level for vehicular networks based on fuzzy system
Authors: Xu Cheng , Liu Hongzhe , Pan Zhenhua , Li Wenfa , Ye Zhao
Published: July 2020

Vehicular ad hoc networks play an important role in current intelligent transportation networks, which have attracted much attention from academia and industry. Vehicular networks can be implemented by Long-Term Evolution Advanced (LTE-A) networks, which have been formally defined in a series of standards by third-generation partnership projects (3GPP). Abundant challenges exist in the authentication processes in LTE-A-based vehicular networks. This paper aimed to improve the security functionality of these vehicular networks by proposing a secure and efficient group authentication and privacy-preserving scheme for vehicular networks based on fuzzy system: the group authentication and privacy-preserving level (GAPL). Compared with existing schemes, the proposed scheme can greatly reduce the number of control message transmissions from mass vehicular equipment (VEs) to the network and substantially avoid overhead in LTE-A-based vehicular networks. Privacy-preserving levels are established to protect VE privacy in authentication. Furthermore, the scheme contains security functions, including privacy preservation, non-frameability and non-repudiation verification.
Permanent magnet synchronous motor algorithms based on nonlinear identification generalized predictive and Intelligent fuzzy control system
Authors: Yonglei Cao , Xiaodong Zhang
Published: July 2020

A control strategy of permanent magnet-oriented field synchronous motor based on intelligent fuzzy control system and generalized predictive control with non-linear identification is proposed to develop the effectiveness of the controlling method of constant magnet-oriented field synchronous motor, the accessor can be split into stabilization control part and intelligent control part. The input of traditional feedback control is used as the stabilization control part, while the feed-forward is incorporated into the intelligent part to compensate for the uncertainties of repetitive load torque and model parameters. The proposed feed forward compensation term uses simple learning rules without any load torque disturbance observer. The additional learning feed forward term does not require information about motor parameters and load torque values, it is insensitive to load torque uncertainty and model parameters, and does not need to identify the system model. With that, the solidness and intermingling confirmation of the proposed control framework reaction is given. The exploratory outcomes demonstrate that the proposed technique has littler speed overshoot list, and the heap torque against aggravation capacity list is improved by over 30%.
Performance appraisal of business administration based on artificial intelligence and convolutional neural network
Authors: Gui Xin
Published: July 2020

Performance appraisal in business administration has a great impact on social and economic development, so a sound performance appraisal system should be established. Moreover, in the information age, scientific methods are needed to improve business management performance. Based on this, this study links artificial intelligence with convolutional neural networks, and builds a corresponding performance research model based on actual conditions. When building the model, this paper selects the data width of 8Bit and 32 data per line, and shifts storage 2 rows, and sets the read/write enable signal to be half of the clock signal. In addition, the image matrix of the input image subjected to nonlinear processing by the excitation function ReLU will exhibit sparsity. Finally, combined with the model and data constructed in this study, the model is validated and the relevant strategies for performance evaluation are obtained.
Construction of science and technology achievement transfer and transformation platform based on deep learning and data mining technology
Authors: Guan Xiaoxu , Fan Yixuan , Qin Qirong , Deng Ke , Yang Gen
Published: July 2020

The transfer of scientific and technological achievements is an inevitable stage in the application of science and technology to the process of productivity. This process is accompanied by various influencing factors. How to eliminate the influence of adverse influence factors on the transformation of technology into productivity is crucial to the development of social productive forces. Based on this, from the perspective of deep learning, this study builds a technology transfer transformation platform through deep learning combined with data mining technology and analyzes the method in detail. On this basis, this paper takes a city as an example to analyze the platform of scientific and technological achievements transfer. In addition, by collecting existing data as system input and data mining analysis, this paper summarizes the advantages, disadvantages, opportunities and threats of the city’s enterprises in the transformation of results and proposes corresponding countermeasures. The example verification shows that the method proposed in this study has certain practical effects and can provide theoretical reference for subsequent related research.
Analysis of English teaching based on convolutional neural network and improved random forest algorithm
Authors: Cao Huifang
Published: July 2020

At present, English teaching does not play the role of a smart classroom, and it is difficult to grasp the student status and characteristics in real time in actual teaching. Based on this, starting from the video image and static image and the actual situation of English classroom teaching, this study, based on the convolutional neural network and random forest algorithm, performs static image human behavior recognition under different image representation conditions, and studies the influence of background information of image and spatial distribution information of image features on recognition accuracy. Then, based on the similarity between different behavior classes, a static image human body behavior recognition method based on improved random forest is proposed. In addition, through theoretical research, an algorithm model that can identify the characteristics of English classrooms is constructed, and the static and dynamic images of English teaching are taken as an example to conduct experimental analysis. The research shows that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
Application of image content feature retrieval based on deep learning in sports public industry
Authors: Xu Nianli , Liu Fengying
Published: July 2020

The image content retrieval can effectively promote the development of the entire industry. At present, sports competition is becoming more and more fierce, and the requirements for image content retrieval are getting higher and higher. In this paper, research has been carried out on image descriptor generation, image feature quantization and coding, accurate nearest neighbor cluster center fast search, multi-dimensional inverted index construction and fast retrieval. Moreover, based on deep learning, this paper constructed an effective detection algorithm for the characteristics of sports images, and compared the image shape and color as examples. It can be seen from the comparative study that the research method of this paper can effectively reduce the size of the candidate set of query results without affecting the accuracy of the query, which is of great significance for improving the speed of image query and has certain significance for promoting the development of sports public industry.
Development of computer aided classroom teaching system based on machine learning prediction and artificial intelligence KNN algorithm
Authors: Quan Yu
Published: July 2020

With the continuous development of science and technology, computer-aided teaching has become a common mode of school teaching. From the current situation, it can be seen that the current computer-aided teaching mostly replaces the traditional teaching mode with multimedia, and does not play the role of functional teaching, and teachers cannot effectively grasp the students’ psychological thoughts in teaching. Based on this, this study combines machine learning prediction and artificial intelligence KNN algorithm to actual teaching. Moreover, this study collects video and instructional images for student feature behavior recognition, and distinguishes individual features from group feature recognition, and can detect student expression recognition in detail. In addition, this study designed a case study to analyze the performance of the algorithm. From the experimental results, it can be seen that the proposed algorithm has certain effects and can be used as an algorithm to assist the teaching process and can provide theoretical reference for subsequent related research.
Analysis of the characteristics of English part of speech based on unsupervised machine learning and image recognition model
Authors: Li Pengpeng , Jiang Shuai
Published: July 2020

If there are more external interference factors in the process of intelligent recognition in English, the recognition accuracy will be greatly reduced. It is of great academic value and application significance to deeply study feature recognition of English part-of-speech and realize automatic image processing of English recognition. Based on unsupervised machine learning and image recognition technology, this study combines the actual factors of English recognition to set the corresponding influencing factors and proposes a reliable method to identify multi-body rotating characters. This method utilizes the principle of the periodic characteristics of the trajectory rotation on the feature space. Moreover, this study conducts a comparative analysis of recognition accuracy by comparative experiments. In addition, this paper analyzes the recognition principles of 4 fonts in detail. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
Analysis of task degree of English learning based on deep learning framework and image target recognition
Authors: Yu Jing
Published: July 2020

Task degree has become one of the important indicators to measure students’ English learning intensity and learning quality, and the difference in task degree has different effects on students’ English learning. In order to realize the task recognition of English classroom teaching, combined with the characteristics of deep learning, this study combines the actual situation of English classroom teaching to analyze, and distinguishes characters through student positioning and feature recognition. Moreover, this paper combines the characteristics of English learning scoring to judge students’ learning situation, and designs a shallow convolutional neural network based on TensorFlow architecture for identifying images and uses GPU training acceleration to solve the problem of training time-consuming in the face of large data volume. In addition, the task results feedback is evaluated by scoring method, and the performance of the algorithm is analyzed by experiments. By setting the category of sensitive targets, this paper can perceive the results according to the target location and mark the sensitive targets in the input scene image. The research results show that the method proposed in this paper has certain effects.
Application of MapReduce parallel association mining on IDS in cloud computing environment
Authors: Wu Wentie , Xu Shengchao
Published: July 2020

The rise of the cloud computing model has resulted in more than terabytes of data being stored in the cloud platform every day on the Internet. Mining valuable information from these massive data has become an emerging industry direction, but the current Intrusion-detection system (IDS) has been unable to adapt to large-scale log information mining. Therefore, an association rule mining algorithm based on MapReduce parallel computing framework is proposed. Firstly, the frequent itemsets mining algorithm Apriori is analyzed, and the MapReduce model is used to parallelize and improve it to more efficiently complete the mining of frequent itemsets. Secondly, the parallel Apriori is designed to run on IDS. Finally, the simulation experiment was carried out by building an open source cloud computing framework Hadoop cluster. Finally, the simulation experiment was carried out by building an open source cloud computing framework Hadoop cluster. The results show that the proposed method has higher detection efficiency when processing massive data, and requires less processing time.
Optimization of architectural art teaching model based on Naive Bayesian classification algorithm and fuzzy model
Authors: Liu Ying
Published: July 2020

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode.
A lightweight vehicle detection and tracking technique for advanced driving assistance systems
Authors: Farag Wael
Published: July 2020

In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly a pipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the careful fusion of the employed algorithms where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. The particulars of the employed algorithms together with their implementation are described in detail. Additionally, these algorithms and their various integration combinations are tested and their performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark with 87% average precision achieved. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions.
Limit theory of isv-functions and its application for rough sets
Authors: Luo Zhiming , Wang Pei
Published: July 2020

In this paper, limit theory of set-valued functions defined on an interval (for short, isv-functions) is preliminarily established. Firstly, the concept of isv-functions is introduced. Secondly, limits of isv-functions are proposed and their properties are obtained. Thirdly, point-wise continuity of isv-functions and continuous isv-functions are discussed. Finally, an application of this theory for rough sets is given.
Intuitionistic fuzzy three-way formal concept analysis based attribute correlation degree
Authors: Xin Xian-Wei , Song Ji-Hua , Xue Zhan-Ao , Peng Wei-Ming
Published: July 2020

As an important expanded of the classical formal concept, the three-way formal concept analysis integrates more information with the three-way decision theory. However, to the best of our knowledge, few scholars have studied the intuitionistic fuzzy three-way formal concept analysis. This paper proposes an intuitionistic fuzzy three-way formal concept analysis model based on the attribute correlation degree. To achieve this, we comprehensively analyze the composition of attribute correlation degree in the intuitionistic fuzzy environment, and introduce the corresponding calculation methods for different situations, as well as prove the related properties. Furthermore, we investigate the intuitionistic fuzzy three-way concept lattice ((IF3WCL) of object-induced and attribute-induced. Then, the relationship between the IF3WCL and the positive, negative and boundary domains in the three-way decision are discussed. In addition, considering the final decision problem of boundary objects, the secondary decision strategy of boundary objects is obtained for IF3WCL. Finally, a numerical example of multinational company investment illustrates the effectiveness of the proposed model. In this paper, we systematically study the IF3WCL, and give a quantitative analysis method of formal concept decision along with its connection with three-way decision, which provides new ideas for the related research.
Identification of key customer requirements based on online reviews
Authors: Li Nailiang , Jin Xiao , Li Yupeng
Published: July 2020

Customer requirements are the essential driving force for successful product development. They can be grouped into several categories, including basic requirements, indifferent requirements, reverse requirements, expected requirements, and attractive requirements. Among these, the latter two are crucial for improving customer satisfaction and can be classified as key requirements. However, the literature on identifying key requirements suffers from issues related to subjective interference and the lack of a specific quantitative calculation process. Thus, this study proposes a model for identifying critical customer requirements. First, use Python to run the web crawler for extracting online customer reviews. Second, extract product engineering characteristics using the relevant text mining technology and latent Dirichlet allocation topic clustering algorithm. Third, we combine sentiment analysis and other factors that influence customer satisfaction with the product engineering characteristics to conduct the conjoint analysis and calculate utility values for the product engineering characteristics. Finally, integrate Kano model to formulate the requirements hierarchy rules, determine the final key requirements index, and identify the key customer requirements. And a case study implemented the key customer requirements identification problem for a smartphone to demonstrate the feasibility and effectiveness of the proposed methodology.
Nilpotent fuzzy lie ideals
Authors: Mohammadzadeh E. , Muhiuddin G. , Zhan J. , Borzooei R.A.
Published: July 2020

In this paper, we introduce a new definition for nilpotent fuzzy Lie ideal, which is a well-defined extension of nilpotent Lie ideal in Lie algebras, and we name it a good nilpotent fuzzy Lie ideal. Then we prove that a Lie algebra is nilpotent if and only if any fuzzy Lie ideal of it, is a good nilpotent fuzzy Lie ideal. In particular, we construct a nilpotent Lie algebra via a good nilpotent fuzzy Lie ideal. Also, we prove that with some conditions, every good nilpotent fuzzy Lie ideal is finite. Finally, we define an Engel fuzzy Lie ideal, and we show that every Engel fuzzy Lie ideal of a finite Lie algebra is a good nilpotent fuzzy Lie ideal. We think that these notions could be useful to solve some problems of Lie algebras with nilpotent fuzzy Lie ideals.
New risks in the new beta coefficient: Behavioral approach
Authors: Bogatyrev S.Yu.
Published: July 2020

The article addresses theoretical issues, applied instruments and practical implementation of the behavioral valuation methodology. It formulates the range of specific correction parameters for beta coefficient, used in calculating CAPM-based discount rate in order to find behavioral discount rate and calculate stock’s market value, with regard to behavioral factors and risks associated with them. The hypothesis that emotions influence the deviation of the beta coefficient used by irrational investors in the market from the beta coefficient used in the CAPM model was tested and confirmed. The range of deviations of the beta coefficient used by irrational investors in the market from the beta coefficient used in the CAPM model is presented. It can be used for specific practical applications in calculating the market value of shares. The results of the studies described in the article are used in valuation, in corporate finance, in the stock market. It is especially important to use the tools of behavioral valuation in conditions of instability and crisis, a change in the market paradigm, market shifts, changes in the parameters of profitability and volatility of financial instruments. The use of behavioral valuation tools complements and expands the classic tools of traditional finance, improves the quality of value management solutions in modern markets.
Phronesis Embedded Leadership and its Role in Conflict Management
Authors: Ahmed Ali , Khan Talha Zubair Ahmad
Published: July 2020

Conflict management has emerged as a major subfield of organization behavior in which researchers have presented numerous models and approaches to deal with conflicting situations. In this context, leadership has been identified as one of the promising approaches to effectively deal with conflict. Built upon the strong and constant interplay that exists between leadership and conflict management this paper presents a conceptual argument that the “phronesis centered leadership” is more likely to play an effective role in managing the conflict because phronetic leadership abilities help the leaders to make quick and righteous decisions in problematic situations. This propositional paper outlines that how a conceptual model of phronesis centered leadership can be applied to conflict management. This paper concludes with a discussion that wisdom of senior transformational leader and as well as distributed wisdom in an organization play an important role in managing conflict.
Should the Criteria for Evaluating an Organizational Theoretical Contribution be Universal, Distant from Cultural and Contextual Factors?
Authors: Hamdy Aisha Ahmed
Published: July 2020

This paper aims to discuss the criteria used for evaluating theoretical contributions and whether these are universally applicable. Questions are raised regarding the impact of cultural and contextual variables in perceiving the value of a theoretical contribution especially in the field of organizational theory.
Preferences of Students towards Conducting Qualitative Research in Social Sciences at Higher Education Level
Authors: Khalil Atif , Saeed Muhammed , Tauheed Kanwal
Published: July 2020

The study aimed to explore the intentions of higher education students towards conducting qualitative research in social sciences. The study included four PhD and six MPhil students from Punjab Univeristy (Institute of Education and Research) and Eduacation University who were selected through purposive sampling technique. A semi-structured interview protocol was constructed which was validated through experts’ opinion. The respondents were asked about their feelings, knowledge, skills, interests and barriers towards qualitative research. A thematic analysis was applied after obtaining the data through audio recording and field notes. The results revealed that most of the PhD scholars did not possess knowledge and skills required to conduct qualitative research. There was no visible difference between the responses of IER (PU) and EU students. But PhD and MPhil Students had different preferences towards conducting qualitative research.
Comunidades autónomas
Authors: Gorosquieta Javier
Published: July 2020

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El estatuto del trabajador y la actual crisis económica
Authors: Gomez Camacho Francisco
Published: July 2020

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La ciencia y la tecnología como ideología
Authors: Hortal Augusto
Published: July 2020

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La juventud rural leonesa contra la sociedad tradicional
Authors: Cordero Del Castillo Prisciliano
Published: July 2020

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Ética de la reforma sanitaria
Authors: Redacción Consejo de
Published: July 2020

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La Reforma Sanitaria: tres cuestiones clave
Authors: Evangelista Benitez Manuel
Published: July 2020

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El Artículo 27 de la Constitución y su interpretación
Authors: Herrero Rodríguez Miguel
Published: July 2020

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Ideas claras sobre la libertad de enseñanza
Authors: Lumbrearas Meabe Juan María
Published: July 2020

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Problemas financieros de la enseñanza no obligatoria
Authors: Soriano Tena Rufino
Published: July 2020

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Acerca de la escuela "pública"
Authors: Brugmans Henri
Published: July 2020

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Helsinki, Belgrado, Madrid. Estado de la cuestión
Authors: Carrillo Salcedo Juan Antonio
Published: July 2020

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La IV Consulta Post-Helsinki de la Conferencia de Iglesias Europeas (CEC)
Authors: García Matías
Published: July 2020

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Research on the reuse of alkali solution in rubber pretreatment process to minimize chemical costs
Authors: Nguyễn Liên Thị , Nguyen Dinh Quan , Tran Thi Tuong An , Le Diep Trung Tin
Published: July 2020

Agricultural and forestry wastes are the popular renewable resources due to its price and efficiency. In recent decades, there are a lot of successful researches about bioethanol from lignocellulose to decrease the fossil fuels consumption. To produce bioethanol from lignocellulose materials, the prerequisite step is pretreatment step to remove lignin, to break the tight structure of lignin-cellulose-hemicellulose complex, to facilitate the contact between hydrolysis agent and cellulose. Alkaline pretreatment has been known as the most popular method. In this study, NaOH solution after the first pretreatment will be used again, adding a minimum amount of NaOH. The addition is calculated so that the total amount is reduced while maintaining the pretreatment efficiency. Analysis results showed that an aqueous solution of 2.0 wt.% NaOH can be used to pretreat the biomass for the first time in 24h, the first pretreatment allow the cellulose enrichment of raw materials from 41.2 % to 53%, in the second reused pretreatment, the addition give the same efficiency. The total amount of alkaline solution decreases 30.3% and the pure water decrease 41.2%, compared to the traditional method. The proposed method achieves the main goal: reduce significantly chemical costs, have a positive and practical meaning in contributing to solve the economic - technical problem of the second generation bioethanol’s production technology.