Volume 6, Issue 4

We propose a novel framework exploiting domain-aware aspect similarity for solving the multi- source cross-domain sentiment classification problem under the constraint of little labeled data. Existing works mainly focus on identifying the common sentiment features from all domains with weighting based on the coarse-grained domain similarity. We argue that it might not provide an accurate similarity measure due to the negative effect of domain-specific aspects. In addition, existing models usually involve training sub-models using a small portion of the labeled data which might not be appropriate under the constraint of little labeled data. To tickle the above limitations, we propose a domain-aware topic model to exploit the fine-grained domain-aware aspect similarity. We utilize the novel domain-aware linear layer to control the exposure of various domains to latent aspect topics. The model discovers latent aspect topics and also captures the proportion of latent aspect topics of the input. Next, we utilize the proposed topic- attention network for training aspect models capturing the transferable sentiment knowledge regarding particular aspect topics. The framework finally makes predictions according to the aspect proportion of the testing data for adjusting the contribution of various aspect models. Experimental results show that our proposed framework achieves the state-of-the-art performance under the constraint of little labeled data. The framework has 71% classification accuracy when there are only 40 labeled data. The performance increases to around 82% with 200 labeled data. This proves the effectiveness of the fine-grained domain-aware aspect similarity measure.

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A reconfigurable Stepped Frequency Continuous Wave (SFCW) radar prototype for supporting the italian financial police to contrast smuggling, is proposed in this work. In particular, the proposed radar can provide information related to the container contents and the presence of false bottoms speeding up the control operations at borders and ports. Moreover, it is able to reveal the presence of people hidden behind reinforced concrete hiding places. Radar resolution is improved by using suitable post-processing method such as the MUltiple Signal Classification (MUSIC) algorithm. Numerical as well as experimental results obtained considering realistic operative scenarios demonstrated the potentialities and capabilities of this system as an effective tool for smuggling contrast. The preliminary experimental results have been obtained using a compact radar prototype equipped with high gain and directivity antennas to cover all the di bands.

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Classical clustering algorithms often require an a-priori number of expected clusters and the presence of all documents beforehand. From practical point of view, the use of these algorithms especially in more dynamic environments dealing with growing or shrinking corpora therefore is not applicable. Within the last years, graph-based representations of knowledge such as co-occurrence graphs of document corpora have gained attention from the scientific community. Accordingly, novel unsupervised and graph-based algorithms have been recently developed in order to group similar topics, represented by documents or terms, in clusters. The conducted work compares classical and novel graph-based algorithms, showing that classical clustering algorithms in general perform faster than graph-based clustering algorithms. Thus, the authors’ focus is to show that the graph-based algorithms provide similar clustering results without requiring an hyperparamter k to be determined a-priori. It can be observed that the identified clusters exhibit an associative relationship reflecting the topical and sub-topical orientation. In addition, it is shown in a more in-depth investigation that the Seqclu (sequential clustering algorithm) can be optimized performance-wise without loss of clustering quality.

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This paper makes a statistical analysis of some indicators that characterize the environment of students admitted to public and private Institutions of Higher Education i.e. university level institutions, presenting an overview to the distribution of these students in the key areas of study that these institutions offer such as: Arts, Agriculture, Natural Sciences, Social Sciences, Medical Sciences and Sports.
The distribution is studied based on gender, residential area (city or village) and high school average. The study is undertaken to know and better understand the trends related to these indicators in the main areas of study mentioned above. A careful description of the figures from the stidy reveals typical features and explains better the situation. The purpose of the study is to see the impact of these factors on the study programs where these students have been declared winners. In this paper it will be introduced the two-step method (two steps cluster method) and it will be illustrated with an application on a database obtained from the State Matura (Center for Educational Services, Ministry of Education, Sports and Youth).
The two-step cluster analysis identifies the clusters by first executing pre-clustering and then applying the hierarchical method until the final p clustering. Because of the ability to use a fast-clustering algorithm in advance, it can handle large data sets. To evaluate the quality of the groups we used the value of silhouette measure of cohesion and separation.
The software used to perform the analysis is SPSS, V 25. The criterion used to determine the groups is Schwarz’s Bayesian Criterion (BIC).

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The new generation of mobile communications and the recent advances in data management are going to enable a fast transformation in the health sector of many countries. 5G networks, with superior technical characteristics, would allow the development of a new set of application and services gathered under the concept of eHealth. In this article we propose a remote monitoring system based on 5G networks that would allow to provide a varied set of medical services from long distance. However, for achieving an optimal performance, the network must guarantee high bandwidths and low latencies, at the time that a massive number of devices and its corresponding generated data are handle efficiently. Consequently, an appropriated system architecture and data model structure are proposed, taking into consideration the high security requirements that any health-related application or service inherently implies.

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This paper is an extended version of work published in IPFA 2020. In the previous paper, advanced physical failure analysis (PFA) techniques for rescuing damaged samples with cracks, scratches, or unevenness in delayering are introduced. In the present work, the techniques will be further exploited and summarized for the potential applications in general devices. The three typical rescue cases will be fully discussed through comprehensive analysis on the failure mechanism and the rescuing process. Compared to the conventional PFA techniques that normally require back-up samples, the novel rescue techniques offer more alternative solutions for coping with sample damage problems in delayering without starting over with a new sample that would waste machine time and human resources. These new PFA techniques involve only basic failure analysis (FA) skills that could be easily manipulated and FA equipment that is commonly available in FA labs, and would extend the scope and capability of the tradition PFA to help the FA engineers deliver FA results with high quality and high success rate in the daily work, especially for handling “one of a kind” devices.

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Research focused on training and testing of dataset after Optimizing Software Component with the help of deep neural network mechanism. Optimized components are selected for training and testing to improve the accuracy at the time of software selection. Selected components are required to be attuned and accommodating as per requirement. Soft computing mechanism such as PSO and MVO will be used for optimization. Deep Neural-Network mechanism is performing training and testing to get the confusion metrics of true positive/negative and false positive/negative. The accuracy, precision, recall value and f-score are computed to assure accuracy of proposed work. The proposed mechanism is making use of LSTM layer for more accurate output. Proposed research is exploring inadequacy of existing research and extent of incorporation of previous mechanism to soft computing mechanism in CBSE.

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Just as industry is dynamic, constantly evolving according to the state of technology, economics, politics and so on, so must be, higher education. Studies have shown that higher education, for the past century, has constantly adapted to the dynamic skill and knowledge requirements of industry. This adaptation, however, is not always timeous and precise resulting in a widening gap between industry skill requirements and the skills that graduates receive during tertiary learning. This gap can be narrowed if higher education develops futuristic models that prepare students for not only the present day, but the future as well. Higher education in the fields of science, technology and engineering in particular, are in critical need of this future-prediction approach given the high levels of constant, and in some cases, even accelerating change or dynamics. This study develops a concept for industrial engineers of the future and demonstrates that is it possible to better prepare graduates for the uncertain future, by predicting some key skill requirements of industry ahead of time from information of yesterday and today.

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This paper analyzes the challenges a middle-sized city faces, particularly the Metropolitan Zone of Pachuca (MZP), which has not had the expected development given its current geographic, competitiveness, and crisis circumstances. The research proposes a systemic methodology for the analysis of the aforementioned Metropolitan Area. It emphasizes its internal and external behavior, the competitive environment of neighboring cities, conditions of middle-sized cities in Latin America, and the trends of model cities worldwide. The study identified the factors limiting their development and the competencies that can be used based on the opportunities, limitations, and perspectives. The result was a situational temporality matrix of the initiatives according to the impacts, which will specify the necessary characteristics to establishing strategies and the compatibility of the perspective of the Metropolitan Area of Pachuca for the transition towards the model of growth called New Urbanism.

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The Hamming neural network is an effective tool for solving problems of recognition and classification of objects, the components of which are encoded using a binary bipolar alphabet, and as a measure of the objects’ proximity the difference between the number of identical bipolar components which compared include objects and the Hamming distance between them are used. However, the Hamming neural network cannot be used to solve these problems if the input network object (image or vector) is at the same minimum distance from two or more reference objects, which are stored in the weights of the connections of the Hamming network neurons, and if the components of the compared vectors are encoded using a binary alphabet. It also cannot be used to assess the affinity (proximity) binary vectors using the functions of Jaccard, Sokal and Michener, Kulchitsky, etc. These source network Hamming disadvantages are overcome by improving the architecture and its operation algorithms. One of the disadvantages of discrete neural networks is that binary neural networks perceive the income data only when it’s coded in binary or bipolar way. Thereby there is a specific apartness between computer systems based on the neural networks with different information coding. Therefore, developed neural network that is equally effective for any function of two kinds of coding information. This allows to eliminate the indicated disadvantage of the Hamming neural network and expand the scope of discrete neural networks application for solving problems of recognition and classification using proximity functions for discrete objects with binary coding of their components.

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Lab sessions in Engineering Education are designed to reinforce theoretical concepts. However, there is usually not enough time to reinforce all of them. Remote and virtual labs give students more time to reinforce those concepts. In particular, with remote labs, this can be done interacting with real lab instruments and specific configurations. This work proposes a flexible configuration for Remote Lab Sessions, based on some of 2019 most popular programming languages (Python and JavaScript). This configuration needs minimal network privileges, it is easy to scale and reconfigure. Its structure is based on a unique Reception-Server (which hosts students database, and Time Shift Manager, it is accessible from the internet, and connects students with Instruments-Servers) and some Instrument-Servers (which manage hardware connection and host experiences). students always connect to the Reception-Server, and book a time slot for an experience. During this time slot, User is internally forwarded to Instrument-Server associated with the selected experience, so User is still connected to the Reception-Serer. In this way, Reception-Server acts as a firewall, protecting Instrument-Servers, which never are open to the internet. A triple evaluation system is implemented, user session logging with auto-evaluation (objectives accomplished), a knowledge test and an interaction survey. An example experience is implemented, controlling a DC source using Standard Commands for Programmable Instruments. This is an example regarding how systems enable students to interact with hardware, giving the opportunity of understand real behaviour.

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Identification of Kamphaeng Saen beef cattle is important of the registration and traceability purposes. For a traditional identification methods, Hot Branding, Freeze Branding, Paint Branding, and RFID Systems can be replaced by genius human. This paper proposed a Kamphaeng Saen beef cattle identification approach using muzzle print images as an Animal Biometric approach. There are two algorithms used in the system: Scale Invariant Feature Transform (SIFT) for detecting the interesting points and Random Sample Consensus (RANSAC) algorithm used to remove the outlier points and then to achieve more robustness for image matching. The image matching method for Kamphaeng Saen beef cattle identification consists of two phases, enrollment phase and identification phase. Beef cattle identification is determined according to the similarity score. The maximum estimation between input image and one template is affected from two perspectives. The first perspective applied SIFT algorithm in the size of the moving image with the rotating image and applied Gabor filters to enhance the image quality before getting the interesting points. For a robust identification scheme, the second perspective applied the RANSAC algorithm with SIFT output to remove the outlier points to achieve more robustness. Finally, feature matching is accomplished by the Brute-Force Matchers to optimize the image matching results. The system was evaluated based on dataset collected from Kamphaeng Saen (KPS; 47 cattle, 391 images), Nakhon Pathom and Tubkwang (TKW; 39 cattle, 374 images), Saraburi, Thailand. The muzzle print images database was collected between 2017 and 2019, in the total of 765 muzzle print images from 86 different cattles. The experimental result is given 92.25% in terms of accuracy which better than a traditional identification approach. Therefore, muzzle print images can be used to identify a Kamphaeng Saen beef cattle for breeding and marking systems.

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The Ministry of Finance is the state ministry in charge of state financial affairs which has two functions, namely the Chief Financial Officer (CFO) as the State General Treasurer and the Chief Operating Officer (COO) as a Budget User. As COO, the Ministry of Finance is expected to be able to provide information related to budget implementation to leaders quickly and accurately. The problem that occurs is the implementation information is still done manually, so it takes time to process. In addition, there is no information regarding budget predictions for the next semester or year. This study uses Business Intelligence (BI) as a technique in the process of building budget execution information. The Business Intelligence Roadmap is a methodology used to produce budget implementation information in the form of a dashboard. To see the prediction of the realization of the budget for the next semester or year using the forecasting method with the neural network model. the Results is budget implementation information can be accessed easily and has accurate data and can provide information to the leaders as supporting material in making decisions at the Ministry of Finance.

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In research project, efficiency comparison study in prediction of normalization with data mining classification. The purpose of the research was to compare three normalization methods in term of classification accuracy that the normalized data provided: Z-Score, Decimal Scaling and Statistical Column. The six known classifications: K-Nearest Neighbor, Decision Tree, Artificial Neural Network, Support Vector Machine, Naïve Bayes, and Binary Logistic Regression were used to evaluate the normalization methods. The six studied data sets were into two groups. Those data sets were data sets of White wine quality, Pima Indians diabetes, and Vertebral column of which data were 1-5 variables of the outlier coefficient of variation and data sets of Indian liver disease, Working hours, and Avocado of which data were 6-10 variables of the outlier coefficient of variation.
The result of comparison White wine quality and Vertebral column, the best efficiency method had many methods in a non-systematic way. For the data set of Pima Indians diabetes and Indian liver disease, Statistical Column and classification by K-Nearest Neighbor was the best efficiency. For the data set of Working hours, Decimal Scaling and classification by K-Nearest Neighbor was the best efficiency. For the data set of Avocado, Statistical Column and classification by K-Nearest Neighbor, Z-Score and Decimal Scaling and classification by Binary Logistic Regression were the best efficiency. All of normalization and classification methods, Statistical Column and classification by K-Nearest Neighbor was the best efficiency by precision.

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This study aims to design a gamification affordances pedagogy. Affordances are the ways in which we perceive environments to support the needs of learners in the educational system. The main questions are how gamification elements can influence student engagement to improve their affordances. Affordance behavior is a human behavior that refers to a mindset; an attitude or opinion, especially a habitual one. Motivational activities can change a learner’s behavior. A skill-based mindset can be created through the use of affordance motivation. Affordance refers to the points, badges, and leaderboards in gamification elements. This research aims to improve the affordance mindset design of interactive systems with gamification. The affordance design will improve the pedagogy related to engagement. The research focuses on the mindset factors and the relationship between the factors that promote the desired learning outcomes. The findings may help in designing the gamification affordance design method for affordance pedagogy. The expected model could improve learners’ affordances and instructional activities.

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This study aimed to develop an airflow-vibrator motivated facility and assess exercise behaviors. The combination design involved computer-controlled airflow/ vibrators, a user interface program, and an adjustable structure presenting interaction options. The teacher and the participants can choose specific music with adjustable speed. The researcher did interviews during the initial test and field study. During the intervention, all participants succeeded in following the impinged flow with a positive emotional display. A wireless module and gas flow clue lifted the distance limitation of the vibration connection and enabled prompts in a larger area covered by radio waves. The flexible structure fit individuals ergonomic and the affordance consideration. After practicing, the students knew exactly how to pass and asked for the ball from the classmate. Wireless switch and signals give students more confidence in pitching — the participants successfully swap the body to follow the airflow.

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Transformers are habitually designed and manufactured for operation at a fundamental frequency of 50Hz and sinusoidal load current. Transformers are susceptible to non-linear loads. The inception of switching action characterises Non-linear loads and consequently nonsinusoidal load current which brings about higher transformer service losses, hotspot temperature rise, and degradation of cellulosic and liquid insulation, and consequently untimely failure of transformers during service. This phenomenon yield current with different components that are multiples of the fundamental frequency of the distributed photovoltaic power (DPVP) generation system. In order to obviate these challenges, the continuous power rating of the transformer, which is intended to facilitate non-linear loads must be minimised using procedure ascribed by the standards as de-rating. This work, an extension of previous work, proposes a novel procedure by means of Finite Element Method (FEM) for the de-rating of DPVP transformers serving non-linear loads during their service life. The proposed procedure considers parameters such as skin effect, proximity effect, and the magnetic flux leakage on the windings that were not included in the IEEE recommended de-rating procedure. The theoretical examination is substantiated on a 500kVA, three-phase, oil-filled transformer.

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The objective of this research is to propose the estimator of the population mean for incomplete data by using information of simple linear relationship model in the data set. In addition, the factorization of the likelihood function is created to derive the maximum likelihood estimator for the population mean. The simulation study was conducted for 630 situations to compare the efficiency of the proposed estimator with the two population mean estimators, namely pairwise deletion and Anderson estimators. In this study, two criteria—bias and mean square error—of the performances for estimators are examined. It is found that all percentage levels of missing data, the mean square error of the proposed estimator tends to be lower than those of pairwise deletion and Anderson estimators for the large correlation levels between two variables in the data set whatever the sample sizes will be, especially for the large percentage level of missing data. However, for the small correlation between two variables in the data set, the three estimators tend to have the same performances in terms of both two criteria for all sample sizes and all percentage levels of missing data.

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The importance of rare earth resources is increasing and a lot of investigations are conducting all over the world. As a result, it was discovered that abundant deep-sea mud contained rare-earth elements on the deep-sea floor. The suction mining method can be one of the effective seabed mining methods to recover these seafloor sediments. However, it is required to evaluate the deformation behavior of the sediments in seabed mining in terms of environmental evaluation. For this reason, this study investigates the deformation behavior of sediments with different water contents by a laboratory suction test. In the test, the sediments filled in a box whose size is 155 mm× 50 mm×180 mm are vacuumed with a suction pump. The suction pressure of the pump is adjusted to 4.0 kPa, the diameter of the suction pump is 10 mm, and the duration of suction is 8 seconds. The results show that suction volume increases with an increase of water content/liquid limit ratio. In addition, the deformation behavior can be categorized as three shapes based on water content/liquid limit ratio; sharp, cone-shaped, and gentle circular arc when the ratio of water content/liquid limit is under 1.3, from 1.3 to 1.6, and over 1.6, respectively. Furthermore, the application of sealants on the sediment surface is effective to reduce the environmental disturbance although its density has to be the same level as the density of sediments to inhibit sinking the sealants.

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Multi-Robot System (MRS) is a complex system that contains many different software and hardware components. This main problem addressed in this article is the MRS design complexity. The proposed solution provides a modular modeling and simulation technique that is based on formal system engineering method, therefore the MRS design complexity is decomposed and reduced. Modeling the MRS has been achieved via two formal Architecture Description Languages (ADLs), which are Systems Modeling Language (SysML) and Business Process Model and Notation (BPMN), to design the system blueprints. By using those abstract design ADLs, the implementation of the project becomes technology agnostic. This allows to transfer the design concept from on programming language to another. During the simulation phase, a multi-agent environment is used to simulate the MRS blueprints. The simulation has been implemented in Java Agent Development (JADE) middleware. Therefore, its results can be used to analysis and verify the proposed MRS model in form of performance evaluation matrix.

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Educators should be considered the learning style of students so that the best practice approach can be applied in learning activities. As students understand their learning style, they will be able to integrate it into their learning process. Kolb Learning Style was the learning style that was widely used based on the theory of learning experiences. Therefore, this study aimed to describe engineering and non-engineering students’ learning style. The survey research design with a quantitative approach was applied in this study. A total of 300 respondents were selected randomly from all faculties in Universiti Tun Hussein Onn Malaysia. The survey questionnaire consisted of two main sections representing Learning Goals, Learning Style, and Learning Activities. The result explains that both engineering and non-engineering students are more dominant to adopt the Accommodator learning style, followed by the Converger learning style, and then Assimilator learning style and Diverger learning style. It is concluded that the engineering and non-engineering students are more incline to be a kinesthetic learner. These learning preferences and learning styles will contribute to their engagement in the concept of learning and for educators to plan teaching strategies.

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Although the perception of Environmental Kuznets Curve (EKC) has been thoroughly investigated, but there is inconsistency in the results. The relationship between nitrous oxide (N2O) emissions, financial development, economic growth, foreign direct investment, and electric power consumption in Bahrain over the period 1980 – 2012 is examined in this paper. The autoregressive distributed lags (ARDL) technique is employed to test for the cointegration in the long run. The results reveal a reversed U-shape long run relationship between N2O emissions and economic growth for Bahrain. Moreover, electric power consumption affects N2O emissions positively in the short and long run. Whereas foreign direct investments and financial development affects the emissions of N2O negatively. Therefore, Bahrain should assist households in installing solar cells to generate clean energy and enhance its financial sector.

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In control systems, several optimization problems have been overcome using Multi-Agent Sys- tems (MAS). Interactions of agents and the complexity of the system can be understood by using MAS. As a result, functional models are generated, which are closer to reality. Nevertheless, the use of models with permanent availability of information between agents is assumed in these systems. In this sense, some strategies have been developed to deal with scenarios of information limitations. Game theory emerges as a convenient framework that employs concepts of strategy to understand interactions between agents and maximize their outcomes. This paper proposes a learning method of distributed control that uses concepts from game theory and reinforcement learning (RL) to regulate the behavior of agents in MAS. Specifically, Q-learning is used in the dynamics found to incorporate the exploration concept in the classic equation of Replicator Dynamics (RD). Afterward, through the use of the Boltzmann distribution and concepts of biological evolution from Evolutionary Game Theory (EGT), the Boltzmann-Based Distributed Replicator Dynamics are introduced as an instrument to control the behavior of agents. Numerous engineering applications can use this approach, especially those with limita- tions in communications between agents. The performance of the method developed is validated in cases of optimization problems, classic games, and with a smart grid application. Despite the information limitations in the system, results obtained evidence that tuning some parameters of the distributed method allows obtaining an analogous behavior to that of the conventional centralized schemes.

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This paper addresses experiments with Scratch-developed games in the robotics introduction course at the Federal University of Technology – Paraná. It aims at assisting learning of classical and initial robotics concepts. This proposal, similar to the classic 80s war tanks game on Atari 2600, was developed using an autonomous vehicle. In the first experiment, applied to the class 2019/2, the students (players) had to battle against another autonomous tank developed (in two different ways, in Person and Distance Learning), using keyboard inputs to control their tank. In this game, the students were asked to create states machine models while were being introduced to fundamental concepts such as pose, other basic notions concerning controlled and autonomous robots, and the hierarchy of actions. At the end of the games, a questionnaire answered by the students extracted valuable findings of the examined concepts. In 2021/1 class, the second and third experiments were applied. The former was an extension of the first experiment, using autonomous parking cars. The latter was inspired by the classic Pong game, with the addition of more degrees of freedom (DOF). In this case, the player attempts to reach and catch a ball through the operation of a robotic arm with two rotating joints, using keyboard inputs. Each block or scenario will become more complex, and the student has time to perform a task. In the case of the third experiment, the concepts including 2-D workspace, multiple solutions, inverse, and direct kinematics were explored. Delivery rates for the first and second experiments were 90% and 80%, respectively. Even though three individual experiments were investigated, the single objective was achieved: the implementation of modern didactic tools to deliver critical pedagogical concepts to students in the robotics class.

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Combinatorial software testing is regarded as a crucial part when it comes to the software development life cycle. However, it would be impractical to exhaustive test highly configurable software due to limited time as well as resources. Moreover, a combinatorial testing strategy would be to employ input-output-based relations (IORs) due to its benefits versus other forms of testing as it concentrates on program output as well as interactions amongst certain input value parameters. However, there are few studies focused on IOR strategies. Although the IOR strategy has been demonstrated to minimize test suite size because of its inherent properties, size could be decreased by appropriately choosing the “don’t care value” pertaining to the test cases. To achieve a result, this paper demonstrates a unified strategy by considering the new meta-heuristic algorithm known as the adaptive firefly algorithm (AFA) in order to design an IOR strategy. In contrast to the existing work, the adaptive firefly algorithm represents a novel approach to integrate between test cases pertaining to t-way test suite generation by deploying elitism operator in classical firefly algorithm. The optimization algorithm method has been put forward to be adopted along with this strategy. Because of this, AFA is expected to deliver promising results when employing the IOR strategy. As per the experimental results backed by non-parametric statistical analysis, AFA demonstrated to offer competitive performance versus its counterparts. In particular, AFA has been found to achieve and match 68% with regards to the best sizes based on the published benchmark results including 32% new known best sizes. This finding could aid in the area of software testing by reducing the number of test cases pertaining to test execution.

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In this investigation, an integrated physics-informed deep learning and topology optimization approach for solving density-based topology designs is presented to accomplish efficiency and flexibility. In every iteration, the neural network generates feasible topology designs, and then the topology performance is evaluated using the finite element method. Unlike the data-driven methods where the loss functions are based on similarity, the physics-informed neural network weights are updated directly using gradient information from the physics model, i.e., finite element analysis. The key idea is that these gradients are calculated automatically through the finite element solver and then backpropagated to the deep learning neural network during the training or intelligence building process. This integrated optimization approach is implemented in Julia programming language and can be automatically differentiated in reverse mode for gradient calculations. Only forward calculations must be executed, and hand-coded gradient equations and parameter update rules are not required. The proposed physics-informed learning process for topology optimization has been demonstrated on several popular 2-D topology optimization test cases, which were found to be a good agreement with the ones from the state-of-the-art topology optimization approach.

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Humans have experienced motion sickness and possessed the knowledge of stereopsis since classical antiquity. Knowledge of stereopsis dates back to approximately 300 B.C., when Euclid first recognized the concept of depth perception in human vision. Further, the motion sickness is including a sensation of wooziness and nausea that has been documented since approximately 400 B.C., when it was mentioned in the Aphorisms of Hippocrates. Stereoscopic images that utilize binocular stereopsis can frequently cause viewers to experience unpleasant symptoms including visual fatigue. Despite the increased use of three-dimensional (3D) display technologies and numerous studies on 3D vision, there is insufficient accumulation of researches to clarify the effects of 3D images on the human body. Therefore, the safety of viewing virtual 3D images is an important social issue. Inconsistency between convergence and lens accommodation is suspected as a cause of which motion sickness induced by stereoscopic viewing have not yet been identified. A system to simultaneously measure the convergence and lens accommodation is constructed to characterize the 3D vision. Fixation distances were compared between the convergence and lens accommodation while a subject repeatedly viewed 3D video clips. The results indicated that the accommodative power did not correspond to the distance of convergence after 90 s of continuously viewing 3D images. Presently, the relationship between this inconsistency and the unpleasant symptoms remains unclear. Therefore, we introduce empirical research on the motion sickness that can contribute to developments in the relevant fields of science and technology.

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Estimation of optimal Air or oxygen is important for the combustion process to be efficient and produce more energy. This is to be based on each component of the fuel and the air, considering their respective pressure and density. At first, this research investigates the role of , , present in combination with , and the air on the flame temperature; using simulation with Cantera 2.4. Results have been compared and calibrated with field data from KivuWatt company. It then demonstrates the way to achieve optimum Air Fuel Ratio (AFR) for the various species of the fuel. The results estimated the flame temperature by means of the percentages of all species of the fuel and the air, as well as various conditions of pressure and temperature. Finally, it combines all to show different values of optimum AFR at various species percentages; and uses a python program to create an AFR calculator available online through the link provided.

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Survivability of systems is a very important system property and consists major concern for organizations and companies. Survivable systems should maintain their critical services functional in a timely manner. There are several approaches, proposed in the literature, on how to develop survivable telecommunication systems, but the majority is based on node outages or path failures, missing the main scope of survivability which is service failure. The contribution of this paper is that it presents a SDLC (Software Development Life Cycle) for developing survivable mobile telecommunication systems. Additionally, the main characteristic of a mobile telecommunication system is that it consists of different types of nodes (ex. MME, SGSN, etc.) that are connected to systems (ex. 5G, 4G, 3G, 2G etc.) and thus form an intersystem that provides services to end users. This interconnection and interoperability of network nodes is of high complexity constituting a threat to system survivability. Thus, another contribution of the current research work is that it provides a systematic approach for handling this complexity.

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An automatic speech recognition (ASR) is a key technology for preventing an ongoing global coronavirus epidemic. Due to the limited corpus database and the morphological diversity of the Thai language, Thai speech recognition is still difficult. In this research, the automatic speech recognition model was built differently from the traditional Thai NLP systems by using an alternative approach based on the keyword spotting (KWS) method using the Mel-frequency cepstral coefficient (MFCC) and convolutional neural network (CNN). MFCC was used in the speech feature extraction process which could convert the voice input signals into the voice feature images. Keywords on these images could then be treated as ordinary objects in the object detection domain. The YOLOv3, which is the popular CNN object detector, was proposed to localize and classify Thai keywords. The keyword spotting method was applied to categorize the Thai spontaneous spoken sentence based on the detected keywords. In order to find out the proposed technique’s performance, real-world tests were carried out with three connected airport tasks. The Tiny-YOLOv3 showed the comparative results with the standard YOLOv3, thus our method could be implemented on the low-resource platform with low latency and a small memory footprint.

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There is no doubt that energy is one of the most important requirements of life, and its importance increases with the passage of time, and this is what make countries to harness the capabilities and scientists in developing energy systems of all kinds, one of the most important energy systems these days is what is known as vertical axis wind turbines. If we compare this type of system with horizontal axis wind turbines, it is characterized by a relatively lower manufacturing cost. But on the other hand, it suffers from less efficiency in addition to the problem of starting the self-movement. The idea of this research revolves around the use of an engineering design for the vertical axis wind rotor that is very rarely used in the field of wind energy. This design takes the geometric shape of two inverted trapezoids. Within the framework of this study, the term “slant straight-blade vertical axis wind turbine” (SS-VAWT) was assigned to the wind rotor. Amendments have been made to the mathematical model of Multi stream tube to make it suitable for application and work on (SS-VAWT), where, it is known that the multi-stream tube model uses primarily and only for the original Darrieus and the H-Darrieus rotors. In order to prove the efficacy of the software used, the results obtained from it were compared with the practical results of previous studies, as it proved its effectiveness in obtaining the satisfactory results that were intended for this analysis. The analyzes and investigations that were conducted on the improved SS design included changing the geometry by changing some of its dimensional parameters represented in rotor height, rotor diameter, number of rotor blades, rotor blade section length, rotor blade section type and rotor blades inclination angle on the horizontal plane. Within the scope of the case studies that were worked on in this research, the results showed that the best efficiency of the SS rotor was achieved in the range of height to radius ratio (0.66 to 1), cord line length to radius ratio about 0.12 The angle of inclination of the blades is between 45- and 65-degrees Degree. In these ranges, the value of Max power factors has reached its turn, and the energetic range of the rotor has increased as a function of the peripheral relative velocity, in addition to a relatively large solution to the problem of starting self-movement, which appears through the highest-power factor values to move away from the limits of negative values in the range Terminal forgetfulness from 1 to 3. In addition, the effect of changing Raynaud’s number on the turbine aerodynamic performance has been investigated. The results showed that the higher the Reynolds value, the higher the power factor value, the higher the energy range and the lessening the problem of starting the self-movement.

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This study evaluated the competencies related to digital information use through technological tools aiming to acquire applicable knowledge by searching and retrieving information. Methodologically, a quasi-experimental design without a control group was applied to a sample of primary education students from Chile (n=266). First, a diagnosis of the digital-informational skills is performed, and, later, the results of a course in a blended learning context -b-learning- (treatment) are shown. The results show significant differences between the participant groups, confirming the learning in information competencies and distinguishing an initial level from a posterior intermediate level.

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This paper addresses the stock management aspect. Through this work, we provide a dynamic model of dimensioning and allocation of stocks to storage location for the automotive industry field. This model takes into consideration all constraints of the supply chain (24 constraints) from the suppliers passing by production, storage up to customer delivery and transport. At the end of this paper we will be able to specify the stock replenishment policy, particularly the definition of stock alerts (minimum, nominal, maximum) in quantity and days of stock, in space occupied, and in financial value. These stock thresholds will be integrated in material resource planning, storage allocation procedure and financial budget follow-up. The tool developed is decision-making support for logisticians. The algorithm proposed has solved a real instance and ensure a balanced stock fill rate (99%) in 1200 seconds.

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This paper presents the designs and the simulations of proposed structures of electronically frequency reconfigurable planar bow-tie antenna. In the first part, a modified wide band self-complementary bow-tie antenna is designed and implemented. In the second part, varactor and PIN diodes are integrated in top side to adjust electronically the modified structure of bow-tie antenna over multi-band frequency. By adjusting PIN diode between the two states and by tuning the varactor diode inside these two states; the proposed antenna demonstrates two different operational frequencies. In ON state, the antenna covers a narrow frequency bands and in OFF state the antenna demonstrates a wide-band operational frequency.
Furthermore, a new structure of reconfigurable antenna implemented with PIN diode and two hexagonal parasitic elements is developed to realize a multi-band operational frequency band and to cover GPS and GMS bands. Simulated results show a return loss less than -10dB with a gain varied between 0.5 and 3.5dB.

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To effectively implement the social distancing or digital contact tracing in epidemic using an RSSI-based localization approach through Bluetooth beacon is one of the most widely used technologies, but simply using RSSI measurement is not more relevant because the RF signal is affected by several factors and the environment of usage. Traditional distance or positioning algorithms have large-ranging errors when applied for moving objects because they do not account for the device orientation and use fixed path loss models. Hence, the distance between the nodes cannot be obtained accurately by RSSI measurement in a dynamic environment. In this paper, we propose a solution to compensate for the RSSI loss in real-time by filtering out the noise and then accounting for the antenna orientation using a Beacon Packet. Antenna Orientation is determined using 9DoF (9 Degrees of freedom ) IMU (Inertial Measurement Unit). The nodes simultaneously advertise their presence and scan for the presence of other similar beacons in their range. These nodes also deploy Low Power techniques during periods of inactivity to conserve battery power. Advertising is performed on three Bluetooth channels and no connection or response packet is required between the devices during advertising and scanning activities (ADV_NONCONN_IND). The addition of the Motion Sensor could also be used to optimize the battery life of the device.

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The energy states, transitions chances, oscillator intensities, and collision intensities were computed with FAC (fully relativistic flexible atomic code) program. The calculated results were utilized for identification of the reduced population to sixty-nine thin structural states in C-like Zn (XXV) and indicates the gain coefficients with several electron densities (from 10+20 to 10+22 cm3) and at a wide range of electron plasma temperatures (700,800,900,1000, &1100,1200,1300,1400,1500) eV. By using coupled rate equation to calculate the reduced population at different temperature and plotting that against electron densities; gives that at lower electron densities the reduced population proportional with reduced population till radiative decay happening; while at higher electron densities than 10+20 the radiative decay may be neglected in comparing with collisional depopulation so population states becomes independent and approximately the same. The gain coefficient was calculated by using the Doppler broadening equation of several transitions in Zn(XXV); these data plotted against electron density, and it was found that the gain was increased with temperature and producing the short wavelength laser , between 22 and 50 nm for the Zn30+ion. The data was compared with the experimental calculations values collected by NIST and with the theoretical calculations of Bhatia, Seely&Feldman; where the calculated data differs from energy levels of Zn (XXV) comparing to experimental values in NIST at (2p1/2 2p3/2)1 and (2p1/2 2p3/2)2 by 0.05 and 0.04 successively; and it differs than the theoretical work of Bhatia at (2p1/2 2p3/2)1 and (2p1/2 2p3/2)2 by 0.05 Ryd and 0.04 Ryd successively also; which proved that our calculations are in well agreement with other works.

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Wireless sensor networks are an emerging technology that is used to monitor points or objects of interest in an area. Despite its many applications, this kind of network is often limited by the fact that it is difficult to provide energy to the nodes continuously, forcing the use of batteries, which restricts its operations. Network density may also lead to other problems. Sparse networks require stronger transmissions and have little redundancy while dense networks increase the chances of overhearing and interference. To address these problems, many novel medium access control (MAC) protocols have been developed through the years. The objective of this study is to assess the effectiveness of the T-MAC, B-MAC, and RI-MAC protocols in a variable density network used to collect data inside freight trucks carrying fruits that perish quickly. This article is part of the PrunusPós project, which aims to increase the efficiency of peach and cherry farming in Portugal. The comparison was done using the OMNET++ simulation framework. Our analysis covers the behavior and energetic properties of these protocols as the density of the network increases and shows that RI-MAC is more adaptable and consumes less energy than the alternatives.

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In this article, a fuzzy-based solution of coordination between behaviors of trajectory planning and obstacle avoiding in a RRP-typed SCARA robot control is presented. The first idea of the proposed solution is to divide a robot’s complex behavior into simpler parallel behaviors. The second key idea is a fuzzy-based coordination between these behaviors to make smooth robot motions without collision. The modelling and simulation on Matlab are executed to test the performance of the proposed solutions under basic circumstances.

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Stepping-stone intrusion has been widely used by professional hackers to launch their attacks. Unfortunately, this important and typical offensive skill has not been taught in most colleges and universities. In this paper, after surveying the most popular detection techniques in stepping-stone intrusion, we develop 10 hands-on labs to enhance student-learning experience in cybersecurity education. The goal is not only to teach students offensive skills and the techniques to detect and prevent stepping-stone intrusion, but also to train them to be successfully adaptive to the fast-changing dynamic cybersecurity world.

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Serious games (SGs) are interactive and entertaining digital games with a special educational purpose. Studies have shown that SGs are effective in enhancing educational skills. Cognitive skills training through serious games have been used in improving students learning outcomes. In this article, we introduce the ‘plants kingdom’ serious game for improving adolescents’ cognitive skills, mainly attention (Focus, selection, and sustained attention) and understanding skills. The game used the grade 8 Science book in designing the game content. The plant kingdom lesson was used for developing the game story and objects, its methods and tools were designed for the purpose of attention and understanding skills improvement. The game was evaluated on 43 students from public schools between the ages of 13-15 years, the study selected data from the students who had completed 5 playing sessions. The attention and understanding skills were assessed using the automatic recording and analysis of the game player’s data. The variables utilized from the players’ data included player ID, session number, gender, number of trials, level, drag and drop time, distance, reason for failure, position, speed, status, time, and playing tool. Results showed that the game improved the attention and understanding skills of students by 27% and 25 % respectively. The study showed the significant effect of serious games in enhancing students’ cognition; thus, integrating serious games into the education system can potentially improve learning objectives and outcomes.

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The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper machine learning techniques for automated agriculture commodity price prediction still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges. In this research, we address these problems by conducting a machine learning strategy study and propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia. The results validate the efficiency of the proposed Long Short-Term Memory Model (LSTM) to serve as the prediction engine for the proposed system. Particularly in the long-term experiment testing, the average performance of LSTM with MSE has improved 45.5% while ARIMA has dropped 74.1% and the average MSE of LSTM is 0.304 which outperformed all other four algorithms.

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In devices using Windows operating system based on x86 system, the real-time performance is not guaranteed by Windows. It is because Windows is not a real-time operating system. Users who develop applications in such a Windows environment generally use commercial solutions such as the RTX or the INtime to provide real-time performance to the system. However, when using functions and API for simple real-time processing, an issue of high development cost occurs in terms of cost-effectiveness.
In this paper, the RTiK+ was implemented in the type of a device driver by controlling the MSR_FSB_FREQ register to generate a timer interrupt independent of Windows in the Windows 8 and providing a real-time functionality to the user mode by re-setting the local APIC count register. And the operating frequency of the CPU is changed to minimize power consumption for battery life in a mobile device such as a Tablet PC.
In particular, the weapon system uses highly reliable MIL-STD-1553B communication and performs BC and RT functions of MIL-STD-1553B to transmit and/or receive data in communication between component and component. It is significantly importance to guarantee integrity of data without loss data during communication. For this purpose, it is proposed to implant the Scheduling algorithm with the RTiK+ for MIL-STD-1553B communication for Windows 8 to support a real-time processing in the Windows operating system on the embedded system, and to use the periods of 2ms (max), 5ms and 10ms provided by the RTiK+ for real-time processing when performing the BC and RT functions of MIL-STD-1553B communication. In this paper, the method of the scheduling algorithm with RTiK+ for MIL-STD-1553B to provide real-time processing is proposed for the Windows based on x86 system.

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Technical safety and health are especially important for mining-extracting industry. Even though the respective lows and good engineering practices exist, technologies develop and could address even better security for humans and equipment. The research question is to survey microclimate sensors in underground mining and to find whether they are ready for automation. The article is inspired from the research in “Computer System for Microclimate Management in Closed Areas of the Post-Mining Galleries and Greenhouses”. The author offers a short review of state and trends in development of sensors and devices for monitoring and reporting of environmental parameters in underground mining. All environmental parameters: air temperature; temperature of surrounding building constructions, heated surfaces of technological machines and equipment; heat flow, heat irradiation; relative humidity; velocity of air flow; noise; illumination; gas concentration; dust level; ionizing radiations; radon concentration in air are represented with relevant measurement devices and measurement units. The next step is representing of fast-developing sensors using scientific references. Author performs quality assessment of their suitability for automated data transfer and management. The assessment criteria are: Analogue measurement devices, Digital measurement devices, Availability of microcontroller. Findings are proposed for discussion: recent used devices in underground mining are not suitable for automatization, because they miss a controller. The availability of controller also presumes availability of management services. On the base of articles about management of sensors and controllers future work is proposed: 1. Integration of existing elements sensor and controller and defining of its management; 2. Moving of new elements and synthesis of algorithms for its management. This will lead to more precise assessment of industrial risk and improvement of safety activities.

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Quantum logic gates represent certain quantum operations to perform quantum computations. Of those quantum gates, there is a category of classical behavior gates called quantum permutation gates. As a quantum algorithm, quantum permutation pad or QPP consists of multiple quantum permutation gates to be implemented both in a quantum computing system as a quantum circuit operating on n-qubits’ states for transformations and in a classical computing system represented by a pad of n-bit permutation matrices. Since first time proposed in 2020, QPP has been recently applied to create a quantum safe lightweight block cipher by replacing SubBytes and AddRoundKey with QPP in AES called AES-QPP. In AES-QPP, QPP consists of 16 selected 8-bit permutation matrices based on the shared classical key materials. For quantum safe, the key length can be any size from 256 bits to 4 KB. That means, this QPP holds up to 4 KB of Shannon information entropy. Its code size is less than 2 KB with 4 KB of RAM memory. In this paper, we propose to apply QPP for a streaming cipher and carry out its encryption performance and the randomness analysis of this streaming cipher. The proposed QPP streaming cipher demonstrates not only good randomness in its ciphertexts but also huge performance improvement: 13x faster than AES-256, with an overall runtime space (6.8 KB).

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The development of Machine Learning methods and approaches offers enormous growth opportunities in the Healthcare field. One of the most exciting challenges in this field is the automation of clinical treatment selection for patient state optimization. Using necessary medical data and the application of Machine Learning methods (like the Genetic Algorithm and the Analytic Hierarchy Process) provides a solution to such a challenge. Research presented in this paper gives the general approach to solve the clinical treatment selection task, which can be used for any type of disease. The distinguishing feature of this approach is that clinical treatment is tailored to the patient’s initial state, thus making treatment personalized. The article also presents a comparison of the different classification methods used to model patient indicators after treatment. Additionally, special attention was paid to the possibilities and potential of using the developed approach in real Healthcare challenges and tasks.

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With the advent of Big Data, we are witnessing a rapid and varied production of huge amounts of sequential data that can have multiple dimensions, we speak of data streams. The characteristics of these data streams make their processing and storage very difficult and at the same time reduce the possibilities of querying them a posteriori. Thus, it has become necessary to set up so-called summary structures, equivalent to views on the data streams which take into account these constraints and allow querying the data already pruned from the system. In order to take into account the aspect of volume, speed and variety of data streams, new methods have appeared in the field of Big Data and NoSQL. These solutions combined make it possible now to set up summaries that make it possible to store and process different types of data streams with more efficiency and representativeness and which best meet the constraints of memory and CPU resources necessary for processing data streams but also with some limits.

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In the past decade, multiple anthropomorphic prosthetic hands have been developed to replace the role of human hands. Prostheses should not only replace the functions of human hands in functionality but also replicate human hands in appearance and sense of body-belonging intuitively. Human fingers have very delicate and complex structures, and it is these complex structures that make our hands dexterity. This study proposes a design based on the anatomical characteristics of the human hand. The proposed design replicates human fingers from bones, ligaments, extensor hoods, and extensor mechanism of tendon, intended to develop a prosthesis that has the same flexibility and appearance as human hand. To evaluate the performance of the proposed prosthetic in daily life, we conducted grasping experiments on common objects. It is successfully proved that the proposed design helps to improve the grasping performance of the artificial hand and has a natural appearance. In this paper, our design succeeds to improve the grasping performance of the artificial hand and gain natural appearance.

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