Volume 6, Issue 5

Presently, individuals generate tremendous volumes of information on the internet. As a result, sentiment analysis is a critical tool for automating a deep understanding of user-generated information. Of late, deep learning algorithms have shown endless promises for a variety of sentiment analysis. The purpose of sentiment analysis is to categorize different descriptions as good, bad, or impartial based on context data. Numerous studies have been concentrated on sentiment analysis in addition to the ability to examine thoughts, views, and reactions. In this paper, we review classical and deep learning approaches that have been applied to various sentiment analysis tasks and their evolution over last years and provide performance analysis of different sentiment analysis models on particular datasets. In the end, we will highlight current challenges and suggested solutions that can be considered in future work to achieve better performance.

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The study aims to assess the machine learning techniques in predicting students’ associated factors that affect their academic performance. The study sample consisted of 5084 middle and high school students between the ages of 10 and 17, attending public and UNRWA schools in the West Bank. The ‘Health Behaviors School Children’ questionnaire for the 2013-2014 academic year was used for data collection, and was then analyzed through machine learning techniques in order to evaluate their relationship with student academic outcomes. Six machine learning techniques (Random Forest, Neural Network, Support Vector Machine, Decision Tree, Naïve Bayes, and Logistic Regression) were used for prediction. The results indicated that the logistic regression and Naïve Bayes models had the highest accuracy levels (94.3%, 94%) respectively, followed by a decision tree, Neural Network, Random Forest, and Support Vector Machine (93.3%,91.9%,91.7%, and 80.2%) respectively. Thus, the Logistic Regression and Naïve Bayes had the best performance in classifying and predicting student academic performance with the associated factors. Furthermore, Decision Tree, Random Forest, and Neural Network had better predictive performance than Support Vector Machine. The results indicated that perception, Smoking, Depression, PTSD, Healthy Food Consumption, Age, gender, Grade Level, and Family income are the most important and significant factors that influence student academic performance. Overall, machine learning techniques prove efficient tools for identifying and predicting the features that influence student academic performance. The deployment of machine learning techniques within schools’ information systems will facilitate the development of health prevention and intervention programs that will enhance students’ academic performance.

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In this research we developed an BIM-based system to monitor and visualize the real-time building users information. Concentrating on building in-use stages, advantages in tracking facial recognition should be revealed through the availability of real-time information. In this way could explore the possibility of how BIM and IoT could improve data-oriented facility management. The integration system called SyncBIM. The five system elements of the construction platform are also proposed to allow more efficient management and data transmission of the building O&M system. This study integrates the three pieces of technology respectively known as BIM platform, Internet of Things, and computer vision to explore the architecture and technology required by the building O&M system, and establishes a “Decision-Making BIM-Based Cloud Platform” to allow the opportunities of information integration and collaborative operations for the application of BIM and the introduction of computer vision technology.

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With increases in technological usage, cyber incidents are also on the rise and have become a major concern in schools across the globe. What is of significant concern is that cyber incidents in South African schools are also on the rise. Existing evidence suggests that, in South Africa there are no clear procedures that are consistently followed by schools on how to report cyber incidents. The aim of this research is therefore to propose cyber incident handling procedures to enhance the effectiveness in handling cyber incidents as well as ensuring that each role player has an important contribution in the intervention process that is designed to reduce cyber incidents in South African schools. The study also assessed the perception of learners on cyber incidents in South Africa. Using the literature review approach and thematic analysis of the data collected from learners the study highlighted the procedures and roles of role players that can assist in cyber incident handling in South African schools. The study also came up with a detailed analysis of the views of learners on cyber incidents in South Africa. The results presented can help to provide a framework that will act as a guide on reporting cyber incidents and directing school management, and all within the school, towards appropriate reporting procedures and intervention processes. The study also found out that the rise in cyber incidents in South African schools, if left unaddressed, can have a devastating effect on learners. Therefore, the government of South Africa, through the Department of Basic Education, must prioritize the handling of cyber incidents in schools as cyber incidents are now a threat to the efficient and effective execution of the mandate of the department.

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Chatbot operates task-oriented customer services in special and open domains at different mobile devices. Its related products such as knowledge base Question-Answer System also benefit daily activities. Chatbot functions generally include automatic speech recognition (ASR), natural language understanding (NLU), dialogue management (DM), natural language generation (NLG) and speech synthesis (SS). In this paper, we proposed a Transfer-based English Language learning chatbot with three learning system levels for real-world application, which integrate recognition service from Google and GPT-2 Open AI with dialogue tasks in NLU and NLG at a WeChat mini-program. From operational perspective, three levels for learning languages systematically were devised: phonetics, semantic and “free-style conversation” simulation in English. First level is to correct pronunciation in voice recognition and learning sentence syntactic. Second is a converse special-domain and the highest third level is a language chatbot communication as free-style conversation agent. From implementation perspective, the Language Learning agent integrates into a WeChat mini-program to devise three user interface levels and to fine-tune transfer learning GPT-2 as back-end language model to generate responses for users. With the combination of the two parts about operation and implementation, based on the Neural Network model of transfer learning technology, different users test the system with open-domain topic acquiring good communication experience and proved it ready to be the industrial application to be used. All of our source codes had uploaded to GitHub: https://github.com/p930203110/EnglishLanguageRobot

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In this work, the impact of implementing a large amount of decentralized renewable energy sources (RES) of different scales on an extra-large power grid is investigated. Three scenarios are created, substituting 10%, 20%, and 30% of the conventional energy production by RES. For this purpose, the initial dynamic model of Continental Europe in combination with the industrial power system application DIgSILENT PowerFactory was used. In order to compare the behavior of different applied scenarios, a performance index was developed to evaluate and rank the effects of network disturbances by means of time-domain simulations. The performance index was designed based on three different criteria that analyze the oscillatory content and thus, the severity of a given event. The initial power flow of the dynamic model was identified as a limiting factor for the integration of RES, therefore two additional power flows were developed following an innovative procedure. Through the methodologies mentioned above, it was found that Turkey is the most sensitive to such changes, which are amplified by increasing implementation of RES and often lead to inter-area oscillation.

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The daily treats model reduction finite frequency (FFMR) design for Takagi Sugeno (T S) systems. This work is to FFMR design in such a way whether augmented model is steady get a reduced H? index in FF areas with noise is established as a prerequisite. To highlight the importance of suggested process, a practical application has been made.

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Background: Today, Enterprise Resource Planning (ERP) software is a major tool for strengthening competitiveness. They are an asset that is changing work practices through the rapid circulation of information, the coordination of action and the development of new ways of doing things, rapid access to a wide range of knowledge and the opening up of new services.
The purpose of our study is to validate a reliable and reasonable questionnaire to measure the impact of ERP on the Performance of Moroccan companies.
Material and methods: This is a cross-sectional study that took place between January and April 2020. The study was based on a questionnaire. The statistical validation of the questionnaire is based on the calculation of the Cronbach’s Coefficient Index and a factor analysis.
Results: The study involved 41 Moroccan companies, 27 of which have an ERP. The Cronbach’s alpha value of all the items obtained is 0.96.
Conclusion: Results showed that questionnaire is a well-structured, objective, valid and reliable in the Moroccan context, the questionnaire could be an instrument to evaluate the impact of ERP on the performance of Moroccan companies

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Ladysmith is a major economic hub in the uThukela District Municipality. However, it has been experiencing floods almost every year which has resulted in the loss of lives and disruption of business activity within the Ladysmith Central Business District. The main objective of this study was to quantify the land use/cover changes before and after floods of 1994, 2006, and 2015 using Geographic Information System and remote sensing. Landsat images for the years 1990, 2000, 2010, and 2020 were used to prepare study area maps. The study revealed that the catchment has undergone drastic modifications in land use/cover in the past four decades. The results showed that agriculture, barren land, and built-up increased by 0.09 %, 63.95 %, and 34.19 %, while vegetation and water bodies drastically declined by 45.88 % and 60 % respectively. In conclusion, the Klip river catchment is at high risk of continuous flooding because of the rapid decrease in natural vegetation and water bodies. Therefore, the study recommends that government should give a greater focus on protecting, preserving, and regenerating natural vegetation as well as water bodies. This information will be useful to planners and policymakers in the planning and development of land use management strategies needed to reduce flooding in the study area.

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Novelty detection affords to identify data patterns that stray strikingly from the normal behavior. it allows a good identification and classification of objects which were not known during the learning phase of the model. In this article, we will introduce an organized and comprehensive review of the study on novelty detection. We have grouped existing methods into three classes. Statistical Based techniques, Machine Learning Based techniques and Deep Learning Based techniques. In addition, we provide a discussion on application domains of novelty detection, and for each category, we have defined the novelty, cited the most used dataset, as well as a description and perspectives of the latest work carried out in this domain. Our article is developed with the aim of facilitating to researchers a better understanding of the interest of using novelty detection in the various fields mentioned in the article, as well as to clarify the different existing novelty detection methods.

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Radiofrequency catheter ablation is routinely used for the therapy of cardiac arrhythmias. Compared with the traditional unipolar ablation, bipolar ablation may improve the controllability of treatment, and prevent side effects and complications caused by catheter ablation. In addition, the variations of myocardial fat’s thickness and myocardial impedance may have significant influence on the performance of bipolar ablation. In this study, computer simulation was performed to study the effects of myocardium fat’s thickness and myocardial impedance on unipolar and bipolar ablation. The simulation demonstrates similar results with experimental ones using a swine heart. We observed that when the myocardial fat’s thickness increases, bipolar ablation’s heating effect and controllability may decrease. However, the final heating effect of bipolar ablation is invariably better than that of unipolar ablation. The ablation effects of unipolar and bipolar ablation are both reduced when myocardial impedance increases, while the heating effects of bipolar ablation are more sensitive to the variation of myocardial impedance and fat layers’ thickness compared with unipolar ablation. The unipolar ablation is more stable in terms of fat, impedance and ablation time.

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Affective states, a dimension of attitude, have a critical role in the learning process. In the educational setting, affective states are commonly captured by self-report tools or based on sentiment analysis on asynchronous textual chats, discussions, or students’ journals. Drawbacks of such tools include: distracting the learning process, demanding time and commitment from students to provide answers, and lack of emotional self-awareness which reduces the reliability. Research suggests speech is one of the most reliable modalities to capture emotion and affective states in real-time since it captures sentiments directly. This research, which is an extension of the work originally presented in FIE conference’20 [1], analyses students’ emotions during teamwork and explores the correlation of emotional states with students’ overall performance. The novelty of this research is using speech as the source of emotion mining in a learning context. We record students’ conversations as they work in low-stake teams in an introductory programming course (CS1) taught in active learning format and apply natural language processing algorithms on the speech transcription to extract different emotions from conversations. The result of our data analysis shows a strong positive correlation between students’ positive emotions as they work in teams and their overall performance in the course. We conduct aspect-based sentiment analysis to explore the themes of the positive emotions and conclude that the student’s positive feelings were mostly centered around course-related topics. The result of this analysis contributes to future development of predictive models to identify low-performing students based on the emotions they express in teams at earlier stages of the semester in order to provide timely feedback or pedagogical interventions to improve their learning experience.

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The ocean surface temperatures or sea surface temperatures have a significant influence on local and global weather. The change in sea surface temperatures will lead to the change in rainfall patterns. In this paper, the long-term rainfall forecasting is developed for planning and decision making in water resource management. The similarity of sea surface temperature images pattern that was applied to analyze and develop the monthly rainfall forecasting model will be proposed. In this work, the convolutional neural network and autoencoder techniques are applied to retrieve the similar sea surface temperature images in database store. The accuracy values of the monthly rainfall forecasting model which is the long-term forecasting were evaluated as well. The average value of the model accuracies was around 82.514%.

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Real-world applications modelled by time-dependent dynamical systems with specific properties such as long-range dependence or self-similarity are usually described by fractional Brownian motion. The investigation of the qualitative behaviour of its realisations is an important topic. For this purpose, efficient mappings from realisations of the dynamical system, i.e., time series, to a set of scalar-valued representations that capture certain properties are considered. Permutation entropy is a well-known measure to quantify the complexity of univariate time series in a scalar-valued representation, for example, to derive estimates for self-similarity or as features or representations in learning tasks. However, since many real-world problems involve multivariate time series, permutation entropy needs to be extended to the multivariate case. This work summarises the behaviour of pooled permutation entropy (PPE), multivariate multi-scale permutation entropy (MMSPE), and multivariate weighted permutation entropy (MWPE) on multivariate fractional Brownian motion, and this work fills the gaps in existing research. In addition, we provide a new study of multivariate ordinal pattern permutation entropy (MOPPE) on multivariate fractional Brownian motion. We conclude with a detailed experimental evaluation and comparison between all multivariate extensions, for example, demonstrating identical behaviour of PPE and MMSPE or uncovering different aspects such as amplitude and cross-correlations by using MWPE and MOPPE, respectively.

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The Conv-TasNet and Demucs algorithms, can differentiate between two mixed signals, such as music and speech, the mixing operation proceed without any support information. The network of convolutional time-domain audio separations is used in Conv-TasNet algorithm, while there is a new waveform-to-waveform model in Demucs algorithm. The Demucs algorithm utilizes a procedure like the audio generation model and sizable decoder capacity. The algorithms are not pretrained; so, the process of separation is blindly without any function of three Natural Languages (NL) detection. This research study evaluated the quality and execution time of the separation output signals. It focused on studying the effectiveness of NL in Both algorithms based on four sound signal experiments: (music & male), (music &female), (music & conversation), and finally (music & child). In addition, this research studies three NL, which are English, Arabic and Chinese. The results are evaluated based on R square and mir_eval libraries, mean absolute Error (MAE) scores and root mean square error (RMSE). Conv-TasNet has the highest Signal-to-distortion-Ratio (SDR) score is 9.21 of music at (music & female) experiment, and the highest SDR value of child signal is 8.14. The SDR score of music at (music & female) experiment is 7.8 during the Demucs algorithm, whereas child output signal has the highest SDR score 8.15. However, the average execution time of English experiment of Conv-TasNet is seven times faster than Demucs. For accuracy measure, RMSE indicates absolute values, and MAE handles the errors between observations and prediction signals. Both algorithms show high accuracy and excellent results in the separation process.

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In the automation of the fish processing industry, the measurement surface-area and volume of the fish requires a method that focuses on processing automation. The creation of a stereo-vision based on real-time measurement method is one of the most essential aspects of this work. To do this task, we completed two steps. The first, the acquisition of the image of the fish using a stereo camera and calibrating the image for size using sample of the image acquisition. Second, by applying image processing techniques and vision system, the fish surface area and fish volume is obtained in real-time. The experimental results of the proposed method have good results for fish surface area and fish volume. The measuring process using stereo-vision only takes a short time, making it suitable for the real-time method.

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The remarkable presence of side lobes levels in ultrasound B-mode imaging significantly decreases the image quality. Therefore, the use of an apodization function is of great importance. Linear windowing functions are among the most efficient techniques used to optimize antenna directivity by suppression of the side lobes. However, the apodization causes the degradation of lateral resolution by elevating the main lobe width. A new apodization approach that couples a linear windowing technique, and a non-linear windowing technique has been suggested. In this paper, a new dynamic apodization technique called “Dynamic Triangular Apodization” (DTA) is proposed. It enables non-linear windowing for each imaging direction. In addition, the effectiveness of a hybrid method that combines the Hanning window with the DTA algorithm is evaluated. In order to validate the proposed method, two types of simulation are carried out; namely point spread function and cyst phantom simulation. In the point spread function simulation, the main lobe width of the dual-apodization algorithm is very similar to that of the rectangular window and at the same time, the side lobes levels are significantly reduced. In the cyst target simulation, the Contrast to Noise Ratio (CNR) values of the dual apodization are significantly improved. The performance of the non-linear apodization is numerically investigated. In comparison with the rectangular window, the non-linear apodization method called DTA-Hann maintains low side lobes levels without altering the main lobe width. Consequently, it is a promising technique that aims to ameliorate the image quality.

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This paper reports the algorithm, the input data and the result of the numeric simulation on the flows of the waves emitted from a rotating object that forms the artificial anti-gravity. First, an object with a heavy mass is placed in the 4-dimensional time and space, which is described by a fundamental tensor. Then the first-order derivative of the tensor describes the gravity, and the second-order derivative describes the waves. If the gravity created by the heavy mass is strong enough, time and space become dependent on each other. The input data for the simulation are discrete numbers that surrogate the infinity of the 4-dimensional time-space. The object is assumed to rotate and the tensor equations are solved. Then the coefficients are calculated, which present physical properties of the waves. The result of the simulation shows that the rotating object emits the waves with positive and negative energy intensities, and they have the spin angular momentum that changes its spinning direction upon the selection of the rotation-speed of the object.

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The data distribution used in model training is assumed to be similar with that when the model is applied. However, in some applications, data distributions may change over time. This situation is called the concept drift, which might decrease the model performance because the model is trained and evaluated in different distributions. To solve this problem for scene audio classification, this study proposes the kernel density drift detection (KD3) algorithm to detect the concept drift and the combine–merge Gaussian mixture model (CMGMM) algorithm to adapt to the concept drift. The strength of the CMGMM algorithm is its ability to perform adaptation and continuously learn from stream data with a local replacement strategy that enables it to preserve previously learned knowledge and avoid catastrophic forgetting. KD3 plays an essential role in detecting the concept drift and supplying adaptation data to the CMGMM. Their performance is evaluated for four types of concept drift with three systematically generated scenarios. The CMGMM is evaluated with and without the concept drift detector. In summary, the combination of the CMGMM and KD3 outperforms two of four other combination methods and shows its best performance at a recurring concept drift.

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Gate driver has been applied in many ways, exemplified by that, by using the DC-isolated and AC-pass characteristics of gate driver’s primary and secondary sides, the problem of floating endpoint in semiconductor power switch can be solved. However, the conventional design of isolated gate driver provides circuit voltage blocking by optically coupled components. Due to the need for optoelectronic conversion, it requires III-VI semiconductor process and non-standard CMOS process, and the cost is always high. Therefore, in order to better solve the above mentioned problem, an electronic isolated gate driver is proposed. It employs an on-chip transformer to provide voltage isolation between the primary and secondary sides of the circuit, and converts the control signal in the circuit into a high-frequency modulated signal, which in the secondary side is then demodulated by the rectifier through the on-chip transformer to produce the original control signal. The miniaturized isolated gate driver proposed herein adopts TSMC T25HVG2 process and uses an on-chip transformer design in lieu of an optically coupled components. As the amplitude shift keying, on-chip transformer, full-wave quadruple rectifier and data buffer amplifier involved in this design are all integrated on the same chip, the integration can be improved. The size can be smaller than the generally separating electronic isolated gate driver, with the interference from external noise being reduced. In addition, the proposed gate driver generates large signals, in terms of chip layout, therefore, the circuit is put inside the on-chip transformer, which can further save area.

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The Internet of Things (IoT) is at a face paced growth in the advanced Industrial Revolution (IR) 4.0 in the modern digital world. Considering the current network security challenges and sophistication of attacks in the heavily computerized and interconnected systems, such as an IoT ecosystem, the need for an innovative, robust, intelligent and adaptive malware attacks and threats security solution is becoming predominant in the current cyberspace. An integrated and scalable IoT malware detection framework called iDRP framework with deep learning method was proposed as a solution to current IoT malware attacks that are largely obfuscated. The novel framework utilized systematic pre-processing and post-processing techniques and methods on the BoTNetIoT malware datasets that contains both benign and malicious IoT traffic data infected by modern day IoT attacks such as Mirai and Gafgyt etc. IoT malware variants in an IoT ecosystem. The raw IoT malware binaries were converted to image files (Gray-scaled) and computed statistically with synthesised sparsed and differential evolutionary hidden feature structures techniques, which were cyclically trained, tested, and cross-validated to establish empirical anomalies with precision in the detection, recognizing, and prediction of malware anomalies in a modern IoT ecosystem. Preliminary experiments were conducted with standardized image binary files such as the MNIST (2-D), and NORB (3-D) datasets as sound scientific exploratory experiments with profound results. The comparative results of the performance of our integrated techniques and methods on the BoTNetIoT IoT malware datasets achieved a 99.98% accuracy, 99.99% ROC/AUC, 99.95% precision, and 99.93 recall rate etc. utilizing the integrated iDRP framework mechanisms for effectively detecting IoT malware in an IoT ecosystem.

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Internet addiction (IA) has adverse effects on psychophysiological responses, interpersonal relationships, and academic and occupational performance. IA detection has received increasing attention. Although questionnaires enable long-term assessment (over 6 months) and physiological measurements to aid the short-term evaluation (over 2 min) of IA, the lack of algorithms results in an inability to detect IA in real time. A computer-aided system can address this problem. This study used the extended classifier system with continuous real-coded variables (XCSR) for rule-based machine learning to classify IA risk. Chen Internet Addiction Scale (CIAS) items were verified and instantaneous respiratory features of IA were extracted with “don’t care” attribute values. The result demonstrated that the XCSR model achieved more than 95% classification accuracy. Using the “don’t care” attribute values, the CIAS items were reduced from 26 to 19, and the instantaneous frequency (IF) of respiratory muscle contractions, respiratory wall movements, and body movements were extracted as IA-related features. These findings suggested that the XCSR model is a potentially useful system for detecting IA. The modified 19-item CIAS and IF of respiration can be adopted to assist in the real-time detection of IA and explore the psychophysiological developments of IA users. In future studies, more samples must be collected to validate these findings and instantaneous physiological responses investigated with different window sizes while participants with IA engage in active online gameplay.

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A transition towards electrification of the public transport sector is ongoing in many cities around the world, as a response to global warming and pollution. However, the question is whether the current state of technology is already sufficient to replace the conventional buses with electric ones and if the existing charging facilities are appropriate to ensure the smooth operation of the buses. Therefore, this work aims to verify the technical feasibility of the electrification of an existing urban line. The purpose is achieved by evaluating a case study on a public transport bus line in the city of Savona, Italy. The average energy consumption of an electric bus operating in the considered line path is estimated in order to investigate the possible locations and sizes of the charging systems to install. The results show that the correct service operation of the electric buses can be achieved by installing one opportunity charger of at least 300 kW in one of the terminals or by installing three 43 kW charging ports in the depot.

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Office buildings commonly use fully glazed façades to reflect a luxurious appearance and to maximize natural light of high solar exposure and high-energy consumption due to cooling and heating. There is a great abundance in constructing shading screens as they are part of the modern movement in field of Energy Conservation, renewable energies, and architectural design. This paper studies the impact of various perforated Glass Reinforced Concrete (GRC) shading screens for different orientation and the Life Cycle Cost Assessment (LCCA) in a prototypical office space in Cairo. We have simulated a wide range of perforated shading screens using Design Builder to identify optimal shading screens with the highest energy savings for different façade orientation. In this paper, we suggest a methodology to achieve better energy saving in office buildings, knowing the façade’s orientation and perforation percentage of shading screen. Simulation results show that shading screens with 10% perforation percentage commonly achieve the highest energy savings for all façade orientations and reaches up to 53% energy savings for the southwest façade. The LCCA of this shading screen in that southwest façade saves 52% in LCCA compared to the base case.

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Cyberbullying is a serious problem and caused an immense impact to the victim. To prevent the cyberbullying, the solution is to develop an automatic detection system. In this research, we propose a combined model for cyberbullying detection and emotion detection by using stacking method. The experiment is to create a better model for cyberbullying detection using SVM (support-vector machine), KNN (K-Nearest Neighbors), and Naive Bayes method, then combine the best model with the emotion model. The result conducted that using SVM classifier give the best accuracy for both emotion and cyberbullying detection. Emotion detection yields an accuracy of 96.7%. Cyberbullying detection using SVM classifier yields an accuracy of 72.73%. Then, both model are combined using the stacking ensemble method and yield an average accuracy of 77.8%. It concluded that including the emotion model would improve the accuracy of detection.

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Energy Efficiency (EE) plays a key role in decreasing energy consumption at a European level, while it is considered as one of the most cost-efficient means to achieve carbon reduction and reinforce energy sufficiency and security. EE financing is imperative to implement measures that will lead to achieving the desired carbon neutrality and, thus, avert climate change. The majority of EE investments ideas are abandoned during the first stages of investment generation as there is not enough interest by the involved actors to support the maturing of the idea. The present paper aims to boost EE investments by developing a web-based Tool that evaluates project ideas, connecting them with real financing proposals. All the above are being realised through standardised procedures, establishing a concrete typology of five (5) EE sectors, a well-structured risk assessment methodology of five (5) risk categories and (9) risk factors, and a benchmarking procedure that takes into account four (4) broadly used economic criteria and eleven (11) verified sustainability indicators. All the parameters are calculated using the candidate project data and EU official statistics, formulated into four (4) main criteria that are fed into a MultiCriteria Decision Analysis that performs the project’s benchmarking. The presented methodology is being practically tested through the development of three (3) innovative Tools (Assess, Agree, Assign) and a stakeholder consultation process with around 200 participants. The Tools filter and benchmark candidate project ideas, based on the standardised benchmarking and the EU Taxonomy sustainability principles, while connecting the most promising project ideas with state-of-the-art financing methods, such as the Green Loans, the Green Bonds and the Energy Efficiency Auctions. By this token, the developed Tools provenly provide added value to the respective stakeholders, offering standardisation in EE project benchmarking and financing, building trust between investors and projects developers.

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The article identifies an alternative style of thinking as one of the important components of human intellectual development. It is shown that it can be effectively implemented in mathematics lessons at school. The purpose of this study is to develop and substantiate a strategy for the formation of an alternative style of thinking among students in mathematics lessons. The innovation in this research lies in the development of a new approach to the formation of an alternative style of thinking of students, involving the progression of schoolchildren up the “ladder of levels”, purposefully correlating task structures with their “alternative analogs”. There are essential research findings. The levels of formation of the alternative style of thinking of schoolchildren are defined and their multiple characteristics are given. It is shown that as the main means of actualizing an alternative style of thinking, it is advisable to set tasks that provide alternative options for analyzing the elements of their content area. The stage-by-stage work of students as they move up the “ladder of levels” is presented. Methodological recommendations for teachers of mathematics have been created and partially tested. These were presented at several seminars / training sessions and were successfully applied in practice throughout the year. Statistical processing using Pearson’s criterion ?2 was applied at the end of the year to the results of the performance of special tasks by 52 students of grades 3-4 of one of the schools in Penza. Some students applied the traditional method (27 students), and the remainder applied the methodology proposed by the authors (25 students). Analysis of the results revealed a higher level of mastery of the alternative style of thinking among students in the experimental group.

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Scientists succeeded in implementing conventional public switch telephone network (PSTN) into internet protocol by launching H.323 IP telephony. The irrelevant and unknown captions in H.323, computer scientists have replaced H.323 by Session Initiation Protocol (SIP) for Voice-over-IP (VoIP). However, the security of voice communication over IP is still a major concern. Besides, security and performance contradict features. VoIP exhibits a quality-of-service requirements that are sensitive to time. Example of such QoS requirements are delay, jitter, and packet loss. Integrating Internet Protocol Security (IPsec) with Generic Routing Encapsulation (GRE) encrypts and authenticate packets from the sender to receiver, but that raises the question of performance as VoIP is time sensitive.
Consequently, three codecs were evaluated to determine the efficiency of each on GRE and IPsec implementation on Internet Protocol version 4 and Internet Protocol version 6 (IPv4 and IPv6), respectively. The topology design and device configuration in this study adopted Graphic Network Simulator 3 (GNS3) and Distributed Internet Traffic Generator (D-ITG) to generate VoIP traffic. The evaluation revealed that the G.723.1 codec achieved better results on IPv4 and IPv6 over GRE with IPsec than other codecs used in the experiment. Furthermore, the codec of choice is a major factor in IPsec VoIP deployment, as also revealed in this study.

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In this work we discuss a special technique for powering of sensor systems, based on a low-power primary cell consisting of two electrodes, made from different metals, with water (lake, rain or tap water) used as an electrolyte. Once placed into an aqueous solution, the primary cell generates a small electric current, which may be utilized for powering of sensor systems. The generated electrical energy is fed into an energy storage (capacitor). After transformation of the voltage by a step-up converter, it is used for supplying the electrical sensor circuit. The expected output power of the developed circuit is 10-15 mA by 2 V output voltage during 0.2-0.5 second. The improved voltage converter topology with implemented maximal power point techniques allows significant reduction of the energy storage’s size in the second revision of the circuit and thus reduction of the resulting size of the board. The implemented sensor board with discussed powering technique, assembled in Paderborn University was already tested in different practical scenarios.

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Discover DaVinci is a novel augmented reality system that incorporates blockchain technology with experiential learning to engage participants in an interactive discovery of Leonardo da Vinci’s oeuvre. The software was created by Marko Suvajdzic, first author of this paper, and it was produced at the University of Florida Digital Worlds Institute. In the true spirit of this “Renaissance man”, Discover DaVinci explores new ideas and technologies “ahead of their time”, opening up questions about usage of blockchain system in the domain of art and technology. This paper discusses some of these questions, such as relation of art and technology, usefulness of blockchain system for digital art, and new materiality of art in digital and informational age. Proposed work of this manuscript is to present the field of digital learning through a general review and more specifically through a prism of Discover DaVinci project created by Digital worlds Insititute at the University of Florida.

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Central authorities have registered economic transactions based on trust since the dawn of time. With the advent of paper, methods for documenting transactions and records became more difficult and detailed. With the widespread use of computers in recent years, digital recording has made record keeping easier and since then every technical advancement has made data recording more convenient and faster. Transactions have accelerated as technology has advanced, but the maintenance of records has remained under the jurisdiction of the authorities. The use of distributed ledger systems allows data to be released from the control of central authorities. A distributed ledger is a peer-to-peer network in which non-centralized data is shared by participants. The Bitcoin payment system and cryptocurrency, which arose after the 2008 financial crisis, was the first distributed ledger application. Central authorities’ poor economic decisions aided in the rise of Bitcoin. The aim of Bitcoin is to make the current financial systems liberal from the influence of central authorities. With the rise in popularity of Bitcoin, the blockchain technology that underpins it has begun to garner interest. While blockchain technology was initially associated with the financial sector due to Bitcoin, research into its use in other sectors such as supply chain, records management, electoral systems, notary services, file systems, energy and artificial intelligence has begun. Several Blockchain infrastructures have been built to allow the use of blockchain technology in a variety of industries. However, it has been revealed in practical approaches that blockchain technology has limitations in terms of speed and scalability. As a result, new distributed ledger technologies with increased speed and scalability have been established. Hashgraph, Tangle, Tempo, Holochain are examples of newly developed distributed ledger technologies. Different influential features distinguish new generation distributed ledger technologies from the conventional Blockchain methods, which can yield into several and practical modern applications. In this study, Blockchain and new generation Distributed Ledger Technologies are compared and possible future applications are outlined.

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The deep learning-based URL classification approach using massive observations has been verified especially in the field of phishing attack detection. Various improvements have been achieved through the modeling of character and word sequence of URL based on convolutional and recurrent neural networks, and it has been proven that an ensemble approach of each model has the best performance. However, existing ensemble methods have limitations in effectively fusing the nonlinear correlation between heterogeneous features extracted from characters and the sequence of sub-domains. In this paper, we propose a convolutional network-based ensemble learning approach to systematically fuse syntactic and semantic features for phishing URL detection. By learning the weights that integrating the heterogeneous features extracted from the URL, an ensemble rule that guarantees the best performance was obtained. A total of 45,000 benign URLs and 15,000 phishing URLs were collected and 10-fold cross-validation was conducted for quantitative validation. The obtained classification accuracy of 0.9804 indicates that the proposed method outperforms the existing machine learning algorithms and provides plausible solution for phishing URL detection. We demonstrated the superiority of the proposed method by receiver-operating characteristic (ROC) curve analysis and the case analysis and confirmed that the accuracy improved by 1.93% compared to the latest deep model.

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The persistent difficulties in reading and spelling acquisition are a risk factor for learning motivation. Play-like intervention tools have been developed to face these difficulties. “I read” is a software that seeks to develop and introduce systematic reading and spelling skills training in a playful and complementary way. This software is intended for children at the beginning of their school journey, as well as for those who reveal reading and/or spelling difficulties. This article intends to present the goals and the structure of this software, as well as the preliminary results of its implementation and game enjoyment with 244 children between 5 and 7 years old. Results indicate that 58% of the participants completed the activities dedicated to alphabetical decoding, and 42% were able to reach the last stage of the game, dedicated to orthographic decoding. Regarding the enjoyment with the software, 96% of the participants classify the games as fun games. In conclusion, training with this software revealed to be beneficial for reading and spelling skills promotion, as well as to increase the overall enjoyment and motivation for learning.

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The paper examines the company’s proprietary means for determining the quality of a software product and measuring its technical debt. The paper’s authors explain how a software product’s quality is directly correlated with the amount of varying technical debts that the end-users receive. All debts can be paid, and technical debt is no exception: one can use various parameters, techniques, and dimensions to effectively measure and optimize the quality of a software product. The authors share information about the company’s proprietary method to technical debt management, which is done via the Technical dEbT Reduction plAtform, otherwise known as TETRA™. They give details about the assessment’s major dimensions, tools, and measurement parameters.

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Data stream gained obvious attention by research for years. Mining this type of data generates special challenges because of their unusual nature. Data streams flows are continuous, infinite and with unbounded size. Because of its accuracy, decision tree is one of the most common methods in classifying data streams. The aim of classification is to find a set of models that can be used to differentiate and label different classes of objects. The discovered models are used to predict the class membership of objects in a data set. Although many efforts were done to classify the stream data using decision trees, it still needs a special attention to enhance its performance, especially regarding time which is an important factor for data streams. This fast type of data requires the shortest possible processing time. This paper presents VFDT-S1.0 as an extension of VFDT (Very Fast Decision Trees). Bagging and sampling techniques are used for enhancing the algorithm time and maintaining accuracy. The experimental result proves that the proposed modification reduces time of the classification by more than 20% in more than one dataset. Effect on accuracy was less than 1% in some datasets. Time results proved the suitability of the algorithm for handling fast stream mining.

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As mobile robots or Automated Guided Vehicles age, and their navigation components wear down, downtime may increase, and therefore robots may lose their path planning capabilities. In such blind-navigation scenarios, we assume robot(s) is programmed to undergo a default random-walk locomotion in a two-dimensional confined workspace with spatially populated obstacles. The robot(s) has only one capability that is to identify its destination. Extensive simulations are conducted to evaluate the cost of sending such robots, measured in number of steps, to a safe “home” position. Specifically, the contribution of this paper is to provide guidelines on how such blind-navigation mission is accomplished under two conditions: (1) Robot(s) locomoting within static obstacles configured in desired patterns, and (2) Robot(s) locomoting within randomly and freely ranging objects (robot or human). The research compares the efficiencies of randomly walking objects to workspace size, the number of obstacles and their dynamics, which provide engineers an understanding of how obstacle design may reduce the risk of collision.

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Navigation of a moving object (drone, vehicle, robot, and so on) and related localization in unknown scenes is nowadays a challenging subject to be addressed. Typically, different source devices, such as image sensor, Inertial Measurement Unit (IMU), Time of Flight (TOF), or a combination of them can be used to reach this goal. Recently, due to increasing accuracy and decreasing cost, the usage of 2D laser range scanners has growth in this subject. Inside a complete navigation scheme, using a 2D laser range scanner, the proposed paper considers alternative ways to estimate the core localization step with the usage of deep learning. We propose a simple but accurate neural network, using less than one hundred thousand overall parameters and reaching good precision performance in terms of Mean Absolute Error (MAE): one centimeter in translation and one degree in rotation. Moreover, the inference time of the neural network is quite fast, processing eight thousand scan pairs per second on Titan X (Pascal) GPU produced by Nvidia. For these reasons, the system is suitable for real-time processing and it is an interesting complement and/or integration for traditional localization methods.

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As artificial intelligence (AI) develops, it is expected that humans and AI will become more closely related than now. At the same time, however, the more closely humans and AI are related to each other, the more clearly they will face a moral dilemma, i.e., artificial intelligence will face a moral dilemma. To solve the moral dilemma problem, AI should understand and take into account human values and ethics. From this point of view. we designed a consciousness model based on Jungian post-psychological notes. As a result, we found that in order to implement the model of consciousness on a computer, it is necessary to design it with a structure similar to that of human beings, referring to human structures in various fields.

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It is widely known that cities now house more than half of the world’s population. Within this framework, this study presents the possibilities for real-world application of renewable energy sources (RES) in urban areas, as well as their contribution to the urban deployment of the new energy paradigm. A comparison is made between hybrid power systems, operating on Bulgarian territory and conventional (traditional) power systems. Proposed hybrid system is designed and actually implemented to power a single-family house and consists of wind turbine (WG) photovoltaics (PV), lithium-ion batteries for energy storage and suitable converter. The HOMER Pro software was used to model and explore the long-term continuous implementation of a hybrid power system and greenhouse (GHG) gas emissions investigation. The article discusses the amount of carbon dioxide (CO2) and nitrogen oxides (NOX), that can be reduced by using a hybrid power system (solar and wind) in conjunction with a battery storage system (BSS – lithium-ion batteries) in single-family houses. Renewable energy sources combined with energy storage, according to this report, result in a 50% reduction in dangerous carbon dioxide and nitrogen oxide emissions. The proposed system is optimized based on the lower cost of energy (COE) and proper dispatch strategies (load following and cycle charging), resulting in greenhouse gas emissions distributed by regional Bulgarian cities.

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The problem of optimal distribution of the current load of a combined heat and power plant with a complex composition of equipment is considered. A multi-criteria approach is proposed for parts of graphs with a constant load in time, considering the factors of economy, reliability and ecology. For sections of the graph with a time-variable load, we consider the formulation of a dynamic optimization problem. In order to increase the intelligence of solving the tasks set, we propose a variant of building an intelligent system that, in addition to a number of intelligent functions, aims to: switch to the principle of advanced (predictive) control when performing the dispatching schedule of loads; select the number of criteria under consideration; evaluate the values of unmeasured or poorly measured parameters; identify the current technical condition of equipment, as well as recommend either a solution available in the database, or perform new calculations.

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