Miracle-dz/UniFAR-FTUs huggingface dataset:
Perception of Mathematics Game’s Design for Primary School: Based on Teachers’ Opinions

Unmistakable methods can be used for learning, and they can be looked at in a few viewpoints, particularly those identified with learning results. In this paper, we introduce an examination with a specific end goal to think about the design adequacy and development’s requirement of a game based learning (GBL) approach that is about to be used in LINUS screening for mathematics subject in primary school. The approach includes multiple interaction forms regarding addition and subtraction operation in mathematics based on LINUS constructs. Ten teachers from three different school located in Batu Pahat have participated in the study. The investigations involving survey activity by using questionnaire as the instrument. While breaking down the results, the outcomes demonstrated that the kids observed the amusement to be all the more fulfilling if there are less levels and more colours. Since the survey were conducted to a very common type of school in Malaysia, we believe game that is about to be built based on opinion gained could be utilized as an effective instrument in primary schools to strengthen pupils’ lessons.
Custom Dual Transportation Mode Detection By Smartphone Devices Exploiting Sensor Diversity

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smartphones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.
Admittance Control of Manipulators in Unknown Environment

The manipulator may contact with obstacles when working in an unknown environment. The manipulator using position control only is unable to get away from the obstacle after contact with obstacles. Moreover, the contact force between the manipulator and the obstacle and the output torque of the joint actuator will increase with the increase of position error, which may damage the manipulator. In order to solve this problem, this paper proposes a control method of manipulator based on admittance control in unknown environment. This method applies a virtual force in the direction of vertical with contact force, and adopts admittance control in the direction of contact force and vertical with contact force respectively. The simulation results show that, compared with the manipulator only using position control, this method can reduce the output torque of joint actuator as well as when the manipulator contact with the obstacle, but also can make the manipulator get out of the obstacles to arrive at the target position. Therefore, using this method, the manipulator can complete the task safely in unknown environment.
Generalized Haar Filter-Based Object Detection for Car Sharing Services

Object detection is important in car sharing services. Accuracy, efficiency, and low memory consumption are desirable for object detection in car sharing services. This paper presents a network system that satisfies all these requirements. Our approach first divides the object detection task into multiple simpler local regression tasks. Then, we propose the generalized Haar filter-based convolutional neural network to reduce the consumption of memory and computing resource. To achieve real-time performance, we introduce a sparse window generation strategy to reduce the number of input image patches without sacrificing accuracy. We perform experiments on both vehicle and pedestrian data sets. Experimental results demonstrate that our approach can accurately detect objects under challenging conditions. Note to Practitioners—Object detection is an important part of intelligent vehicle technologies, which play an important role in car sharing services. Object detection provides metadata for collision avoidance, self-driving systems, and driver-assistance systems, which can result in better safety and consumer experiences in car sharing services. Although deep learning has achieved an excellent performance in object detection, they consume a large amount of storage and computing resource, which makes them difficult to be deployed for car sharing services. This paper suggests a novel approach which is based on the generalized Haar filter and the local regression strategy. Our approach is accurate, efficient, and light. The experimental results verify the effectiveness of the proposed approach in car sharing services.
Industrially Applicable System Regression Test Prioritization in Production Automation

When changes are performed on an automated production system (aPS), new faults can be accidentally introduced into the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and onsite in a very uncomfortable environment. Until now, there has been no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results. Note to Practitioners—Currently, prioritizing relevant system tests to be executed in case of changes to an automated production system (aPS) is very challenging and depends largely on the intuition and experience of the testing technician. As system tests involve manual operations, testing requires substantial effort. In addition, testing is mostly performed under severe time pressure and in an uncomfortable environment such as onsite at the customer’s premises. In this paper, an approach is presented that supports the testing technician in finding and prioritizing available test cases based on previous test executions and change analysis. For practitioners, this approach could streamline regression testing and increase the code quality of aPSs significantly, in particular, for fully integrated aPSs, which have not been the focus of other research so far. As the approach was developed in line with industrial requirements, relevance, and adaptability for industrial use are ensured.
Rolling Mill Cycloconverter Condition Assessment by Harmonic Current via Time–Frequency Signature

High-power industrial rolling mills rely heavily on the sustained operation of cycloconverters, a type of variable-frequency drive. This research proposes a methodology, which observes and diagnoses the operation of cycloconverters as either normal or abnormal by use of time–frequency signature analysis. Various features of the cycloconverter's input current in the time–frequency domain are identified and used to derive parameters that describe these two states in a quantitative manner. A reference model using the parameters is then developed, and comparisons in the time–frequency domain to real data are implemented. Based on these comparisons, a statistical decision boundary is delineated that is used to classify the health status of the cycloconverter.
An Environmental Potential Field Based RRT Algorithm for UAV Path Planning

Path planning is essential for UAVs to perform some specific missions. In this paper, an environmental potential field based RRT (EPF-RRT) algorithm is proposed to deal with UAVs' path planning problems. The EPF-RRT integrates environmental potential field with the original RRT algorithm, which optimizes the strategy of sampling and expansion. In the EPF-RRT, as the environmental potential field is involved, goal positions generate virtual gravitational force and obstacles are repulsive, the resultant force will guide the RRT grow away from the obstacle and near the target, therefore the efficiency of path planning is greatly improved compared with the RRT. The theoretic analysis and simulation results demonstrate that the proposed EPF-RRT algorithm is characterized by high efficiency, good convergence performance and strong planning ability, which solves the path planning well for UAVs in complicated environments.
A Survey on the Design of Gamified Systems for Energy and Water Sustainability

In a world affected by the constant growth and concentration of the population in urban areas, the problem of preserving natural resources has become a priority. A promising approach to resource conservation is demand management, i.e., the ability to positively influence the behaviour of the population towards more sustainable consumption. Information and Communication Technology (ICT) tools have shown a great potential in influencing consumers’ behaviour, which could be exploited for the common good. However, the design of so-called persuasive systems for environmental purposes is a challenging task, because it cannot be based solely on the utilitarian motivation of users, but must be able to trigger a broader range of engagement factors deeply rooted in the human psychology. In this paper, we review the main design principles and models at the base of a class of persuasive system that exploits gamification and Games with a Purpose (GWAPs) to engage users towards sustainability; we identify the most commonly used incentive mechanisms for inducing behavioural changes; and present a selection of gamified systems for energy and water conservation. From such a survey, we distill design guidelines to be applied in the design of demand management socio-technical systems.
Forecasting the Diffusion of Product and Technology Innovations: Using Google Trends as an Example

Product innovations represent the backbone of sustainable entrepreneurial success. In times of increasingly short development and life cycles of products on the one hand and an increasing degree of product complexity and individuality on the other hand, the right timing of the development and market introduction of physical and digital product innovations has become the key success factor for companies. The forecast of the diffusion of innovative product technologies and in particular the tipping point after which they significantly penetrate the market is therefore of paramount importance both for new and established companies. Research has found answers on how to identify technological change, yet the prediction of the timing of technological change remains unsolved. Traditional life cycle models usually measure the development of products and technologies based on one criterion such as sales or performance and lack in the definition of variables and their measurement. However, to predict the diffusion of innovative product technologies multiple factors and their operationalization have to be taken into account in technology-push-market-pull systems. On the technology side, patents and publications have been acknowledged as valuable indicators for progress. On the market side, appropriate indicators still need to be identified. Therefore, in this paper a quantitative scientometric analysis of past innovations has been performed using Google Trends, a service which has recently taken on greater significance. The analysis yields a significant correlation.
The Study of Words Used in Visual Metaphor in Motion Graphic

When applying visual language to the expression of semantic meaning, Motion Graphic is influenced by some factors, for example, the cognition, affection and prior experience of the target audience. In other words, the target audience plays a part in the construction of the meaning of the Motion Graphic; in particular, visual metaphor plays an indispensable role in the audience's participation. Based on the above information, the author of this article uses some materials in a Motion Graphic that is about skin allergy, and conducts some survey and investigation to explore the words that corresponds to the audience's prior experience, so that a specific carrier could be found to express abstract semantic meaning.
Reference Architecture Model in Medicine: A Case of DNA Mircoarray Diagnosis as a Primary Lesson

Reference Architecture Model in Industry 4.0 (RAMI4.0) aims to map existing standards, identify lack or overlap of standards, and clarify the relationships among standards. In the medical field, technology standards and regulations for one product or medical treatment consist of complex combinations and cause difficulties in understanding current settings and finding the opportunities to form new technology standards or regulations. The Reference Architecture Model in medicine would be a useful tool to solve this type of issue. This study evaluates parameters of RAMI4.0 using DNA microarray diagnosis as a case based on existing regulations and technology standards and examines the possibilities of a Medical RAMI.
Ageing characterization of oil impregnated paper insulation based on dispersion staining colors

The life expectancy of a transformer is closely related to the ageing state of its paper insulation. This paper presents novel markers for ageing characterization of paper insulation in oil-filled transformers based on the dispersion staining colors of cellulose fibres. Accelerated thermal ageing experiments were performed for oil-paper specimens at 120 and 130 °C to obtain aged oil-paper samples of different ageing degrees. The average degree of polymerization (DP), crystallinity index, and refractive index of insulation paper were measured; the experimental results show that an increase in refractive index of insulation paper was observed with the increase of ageing degree. Meanwhile, the dispersion staining colors of cellulose fibres in oil were observed using dispersion staining method; the experimental results show that the refractive index of cellulose fibres detached from insulation paper increases with the increment of ageing degree of insulation paper. Finally, two ageing markers—that is, the primary color ratio-based ratio of blue to red and the primary color ratio-based Mahalanobis distance were presented. Their relationship with DP and ageing time of insulation paper were obtained, and were compared with the corresponding relationship with 2-furaldehyde (2-FAL) and carbon oxides. The proposed ageing markers demonstrate an advantage over 2-FAL and carbon oxides and show potential in ageing state evaluation and residual life prediction of paper insulation in transformers.
Binary Artificial Immune Algorithm for Adaptive Visual Detection

A visual model plays an important role in developing an efficient and robust visual tracker. The visual cues employed in the state-of-the-art models are usually predefined and fixed for all the tested videos. However, the discriminative ability of features usually varies among videos. Therefore, using a fixed set is both redundant and noisy: only a subset of any fixed set will present distinct profiles for modeling. Thus, selecting a highly discriminative cue subset in visual modeling should improve the tracking accuracy. In this paper, an optimization method based on a binary artificial immune algorithm is proposed that selects an effective, discriminative feature subset that is adaptive to specific videos. Specifically, a metric is defined to measure the discriminative abilities of visual models. Then, the visual modeling problem is transformed into an optimization scheme, and a binary artificial immune algorithm is introduced and specially designed to solve the modeling problem. Moreover, to preserve the subset of visual cues that are most adaptive to the tracking condition, the selected cues are assigned adaptive weights and modeled in a Sequential Monte Carlo framework. To show its effectiveness, the proposed algorithm is tested on ten representative videos. The experimental results demonstrate the improvement in tracking performance, the improved tracker performs better or comparable with previous excellent trackers from the literature.
Co-design of event generator and synchronous controller for chaotic neural networks with quantization

This paper studies co-design of event generator and synchronous controller for chaotic neural networks with quantization. A quantized event-triggered scheme is presented to reduce the load of network transmission. Sufficient stabilization conditions for synchronization error system are derived on the basis of Lyapunov-Krasovskii functional. Based on the above stability condition, a co-designed parameters of controller and the corresponding event-triggered parameters are obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed control method.
A Conducted Immunity Test Method Based on Electromagnetic Clamp Injection

In the study of EMC performance, the susceptibility test of equipment is especially important. In this paper, the switching power supply (model FDPS-50A) is taken as an example. Conducted susceptibility test is carried out under the condition of no-load and load with the method of electromagnetic forceps injection. The disturbance signal is generated by the signal source and amplified by the power amplifier and attenuator. Injecting the processed interference signal into the cable by using the electromagnetic injection clamp, and then injecting the interference voltage (140dB μ V) into the switching power supply to test the voltage stability of the output terminal at different frequencies. Through the test, the electromagnetic susceptibility of this model of switching power supply is obtained, the validity of this test method is verified, and it is proved that it can be used in the testing of other equipment. The predictive analysis of conducted electromagnetic susceptibility of power electronic equipment has certain practical significance.
All-Digital Transmitter Architecture Based on Two-Path Parallel 1-bit High Pass Filtering DACs

This paper presents a novel transmitter architecture which is tailored for low power, all-digital, and high speed implementation. It is based on two-path parallel digital-to-analog converters (DAC) which are driven by 180° phase-shifted clocks. The architecture operates in high pass mode and extends the output carrier frequency up to half the DAC clock rate. To decrease the number of analog unit current cells in the converter, a low-pass <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>-modulator is used. Since the modulator also converts the input resolution to 1-bit, an inherently-linear digital-to-analog conversion is realized by embedding filtering in the DAC. Furthermore, the finite impulse response DAC transfer function is designed to cancel the <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>-modulator quantization noise. Simulation results at system level demonstrate the robustness of the architecture against random coefficient mismatches, and its suitability for broadband transmissions. The error vector magnitude of the quadrature output is simulated for up to 15% random coefficient mismatch and it maintains a value below −22 dB even when the input signal bandwidths vary from 20 MHz (64-subcarrier OFDM) to 160 MHz (512-subcarrier OFDM). Experimental results are presented to discuss the validity of the proposed all-digital transmitter architecture and to highlight the challenges of implementing it in advanced CMOS nodes.
Intelligent Communication in Wireless Sensor Networks

Wireless sensor networks (WSN) are designed to collect information by means of a large number of energy-limited battery sensor nodes. Therefore, it is important to minimize the energy consumed by each sensor, in order to extend the network life. The goal of this work is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent is sent to each sensor in order to process the information and to cooperate with neighboring sensors while mobile agents (MA) can be used to reduce information shared between source nodes (SN) and send them to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the multi-agent system (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the sensors of the network have been grouped into sectors. For each MA, we have established an optimal itinerary, consuming a minimum amount of energy with data aggregation efficiency in a minimum time. Successive simulations in large-scale wireless sensor networks through the SINALGO (published under a BSD license) simulator show the performance of the proposed method, in terms of energy consumption and package delivery rate.