Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting ‘curriculum crisis’ dialogue: in search of an antidote

ABSTRACT This article builds upon the international dialogue around ‘curriculum crisis’ initiated by Michael Young in Journal of Curriculum Studies (JCS) in 2013 and followed up in JCS in 2015. It seeks to expand the dialogue in three avenues. First, when considered from a sociological perspective, Young is correct to declare ‘curriculum crisis’; however, his position is limited only to the conflict theory. Second, from educational perspectives, the curriculum crisis as such is self-inflicted and it has been more of a battle among different curriculum ideologies in the Anglo-Saxon world and resistance to Bildung-centred Didaktik tradition in the Western world more broadly. Third, it points to the fourth industrial revolution as an inevitable phase. It concludes that ‘curriculum crisis’ is only partially about bringing knowledge back in, and the article suggests ‘the prepared mind’ as a metaphor to bridge knowledge and learning outcome perspectives. Specialized knowledge in curricula will become even more relevant in the innovation age, and a prepared mind adaptable to the changing world and open to continued learning can assist individuals navigate life and career creatively, meaningfully and constructively.
Online detection of Botnets on Network Flows using Stream Mining

The threat posed by botnets of infecting a large number of devices and using them together to perform several malicious actions has become a growing issue to the Internet security. One way to deal with it is to have methods able to correctly identify those botnets and then run necessary countermeasures. Many approaches using machine learning (ML) have been proposed over the years to cope with botnet detection. Nonetheless, the algorithms commonly employed cannot adapt to new data without significant effort. In this sense, a ML research topic referred to as stream mining may be a solution. Stream mining algorithms are specially tailored to learn incrementally with new instances, without consuming significant memory or time. This work proposes an approach using the Very Fast Decision Tree, a classification algorithm used on stream mining that can learn incrementally when needed, to identify botnets by observing network flows. When evaluating the approach on multiple scenarios with different botnets, we were able to achieve high performance metrics on the majority of scenarios, while using a significantly low number of labelled instances.
A Comparative Study of VoxelNet and PointNet for 3D Object Detection in Car by Using KITTI Benchmark

In today's world, 2D object recognition is a normal course of study in research. 3D objection recognition is more in demand and important in the present scenario. 3D object recognition has gained importance in areas such as navigation of vehicles, robotic vision, HoME, virtual reality, etc. This work reveals the two important methods, Voxelnet and PointNet, useful in 3D object recognition. In case of NetPoint, the recognition is good when used with segmentation of point clouds which are in small-scale. Whereas, in case of Voxelnet, scans are used directly on raw points of clouds which are directly operated on patterns. The above conclusion is arrived on KITTI car detection. The KITTI uses detection by using bird's eye view. In this method of KITTI we compare two different methods called LiDAR and RGB-D. We arrive at a conclusion that pointNet is useful and has high performance when we are using small scenarios and Voxelnet is useful and has high performance when we are using large scenarios.
Green site practices and environmental performance: how project complexity moderates the relationship

The characteristics of a project that makes it hard to understand, predict, and manage its general behavior despite the availability of required information relating to the project dynamics is referred to as project complexity. A good knowledge of project complexity at the construction phase of a project, as well as a well thought out plan to manage complexity will determine how proficiently construction projects are planned, managed, and executed in an environmentally friendly manner. The level of complexity of construction projects to a large extent determines the performance or otherwise of the projects with regards to achieving specific environmental standards. At construction sites, the effects of adopting green site practices on environmental performance is largely dependent on the level of complexity inherent in the project’s construction processes. This study investigates the moderating effects of project complexity on the relationship between various green construction site practices and environmental performance of construction projects. A survey was conducted on class A contractors in Nigeria and 168 usable responses were received. The data were analyzed using the partial least squares structural equation modelling technique. The results show that project complexity moderates the relationship between waste management and environmental performance, and the relationship between materials management and environmental performance. But project complexity does not moderate the relationship between energy management and environmental performance. The study provides important theoretical and practical information for construction managers in understanding the dynamics involved in managing of projects with different degrees of complexity, while adopting certain green site practices with the aim of delivering projects with high degree of environmental performance.
Children’s English in Singapore

In Inventing Tomorrow, Sarah Cole provides a definitive account of Wells’s work and ideas. She contends that Wells casts new light on modernism and its values: on topics from warfare to science to time, his work resonates both thematically and aesthetically with some of the most ambitious modernists. At the same time, unlike many modernists, Wells believed that literature had a pressing place in public life, and his works reached a wide range of readers.
Circular microchannels inside bulk polymethylmethacrylate generated by femtosecond laser using slit beam shaping

The authors report on the laser generation of three-dimensional microchannel architectures with fundamentally unlimited channel length by focused 514 nm femtosecond laser pulses inside polymethylmethacrylate bulk material. The three-dimensional channel layout is implemented by moving the specimen using three-dimensional motorized stages, allowing freely chosen complex shaped channel architectures. Based on nonlinear absorption of high intensities around the focal volume, a material modification, including a refractive-index shift is triggered. After laser exposure, the channel is created by a gaseous degradation of exposed areas during an annealing process. Cross section and aspect ratio of thus generated microchannels are strongly influenced by the numerical aperture of the applied objective. To obtain a circular cross-sectional shape, high focusing objectives are necessary. In this report, the authors improve the existing setup by using an elliptical input beam, transforming the intensity distribution of the focal area in the propagation direction to a circular shape even for low and medium numerical aperture focusing objectives and therefore avoiding a small working distance as a limiting factor for processing depth. The elliptical input beam is obtained by insertion of a slit mask in front of the focusing objective. The ellipticity of the incident beam defines the aspect ratio of the generated microchannels. Furthermore, the size of internal microchannels is adjustable by varying laser and annealing process parameters.The authors report on the laser generation of three-dimensional microchannel architectures with fundamentally unlimited channel length by focused 514 nm femtosecond laser pulses inside polymethylmethacrylate bulk material. The three-dimensional channel layout is implemented by moving the specimen using three-dimensional motorized stages, allowing freely chosen complex shaped channel architectures. Based on nonlinear absorption of high intensities around the focal volume, a material modification, including a refractive-index shift is triggered. After laser exposure, the channel is created by a gaseous degradation of exposed areas during an annealing process. Cross section and aspect ratio of thus generated microchannels are strongly influenced by the numerical aperture of the applied objective. To obtain a circular cross-sectional shape, high focusing objectives are necessary. In this report, the authors improve the existing setup by using an elliptical input beam, transforming the intensity distribution o.
Study on the Specified Target Traceability in Multi-camera

In view of the tracing of the pedestrian history trajectory, a counter-tracking method based on multi-camera is proposed. Firstly, a basic database including many attributes such as video frame rate and perspective relationship is established. Secondly, all of the cameras in the environment are calibrated and the relationships among them are constructed. Various features obtained by ResNet neural network are used to improve the accuracy of recalibration for the processed camera data. Finally, using the mean-shift algorithm realizes target tracking and traceability in reverse-time. Compared with ASMS, ColorKCF and Staple algorithm, experiments in the paper show that the proposed method has some advantages in accuracy and loss rate of target tracking, which is effective in practical application.
Deep learning based RGB-D vision tasks

Depth is an important source of information in computer vision. However, depth is usually discarded in most vision tasks. In this thesis, we study the tasks of estimating depth from single monocular images, and incorporating depth for object detection and semantic segmentation. Recently, a significant number of breakthroughs have been introduced to the vision community by deep convolutional neural networks (CNNs). All of our algorithms in this thesis are built upon deep CNNs. The first part of this thesis addresses the task of incorporating depth for object detection and semantic segmentation. The aim is to improve the performance of vision tasks that are only based on RGB data. Two approaches for object detection and two approaches for semantic segmentation are presented. These approaches are based on existing depth estimation, object detection and semantic segmentation algorithms. The second part of this thesis addresses the task of depth estimation. Depth estimation is often formulated as a regression task due to the continuous property of depths. Deep CNNs for depth estimation are trained by iteratively minimizing regression errors between predicted and ground-truth depths. A drawback of regression is that it predicts depths without confidence. In this thesis, we propose to formulate depth estimation as a classification task which naturally predicts depths with confidence. The confidence can be used during training and post-processing. We also propose to exploit ordinal depth relationships from stereo videos to improve the performance of metric depth estimation. By doing so we propose a Relative Depth in Stereo (RDIS) dataset that is densely annotated with relative depths.
Research on Mobile Robot Target Recognition and Obstacle Avoidance Based on Vision

This work investigates the method of object recognition and autonomous obstacle avoidance for mobile robot based on vision, and solves to the problem that mobile robot can move and identify objects in complex environment. Build the Mecanum wheel mobile platform and equip the Kinect sensor and laser rangefinder sensor, transmitted data to the host computer to information decision analysis, and the execution system generation mobile robot motion control commands, then upload the commands to the cloud data center and establish the SQL Server database table to store the information. Robot scan 100ms of every table, to perform the latest control information. 0bject detection uses Gauss model background difference method to detect objects. Target feature extraction adopts the SURF (Speed Up Robust Features) algorithm and uses RANSAC (Random Sample Consensus) algorithm to optimization, removing the mismatching points improves the computation speed and the detection accuracy. Autonomous obstacle avoidance module through the laser range finder to collect distance information, set the safety distance with the obstacles, can achieve the effect of self-obstacle avoidance. The experiment proves that the robot can complete the task of target recognition and autonomous obstacle avoidance, and verify the effectiveness of the system.
Glucose signal estimation using high performance ADC and lightprobes

A need is felt in recent times to depart from invasive me thods of glucose estimation and to adopt noninvasive approaches which are basically harmless in nature . These techniques are more attractive as they surpass the primary fear of the patient i.e. pricking of the body for blood extraction, etc. One of the methods of Glucose estimation is by using a light probe which utilizes the NIR region of electromagne tic radiation. Glucose presents unique absorbance spectra in the NIR region. The authors propose to design an e lectronic system using LED sources and photodiode detectors as sensors. Afte r the light has passed through the sample , the signal falling on the detector will be used to calculate the absorbance. A trans impedance amplifier will amplify the weak current signal and pass it to a ADC. The system will be equipped with a high precision 32-bit ADC viz.ADS1262. This sigma de lta ADC works at 38kSPS and has 10 input channels. These input channels can be used in either in diffe rential mode or singleended mode. This ADC is inte rfaced using SPI interface and includes additional features such as 50/60 Hz Rejection, GPIO, Oscillator, Programmable gain control and Temperature Sensor. The digitized signal will be processed using multivariate techniques such as PLSR and the model for prediction will be built using the calibration data. Once the model is ready the glucose concentration can be predicted from unknown sample using the prediction model. The accuracy of the glucose estimation depends heavily upon the resolution of the ADC. Therefore the choice of an ADC is of outmost importance in the design and development of instrumentation for glucose estimation.
Development of grating interferometer-based stroboscopic X-ray tomography

X-ray phase/dark-field tomography by using an X-ray grating interferometer and white synchrotron radiation has been demonstrated to observe dynamics in materials consisting of light elements, because it is available with X-rays of a broad energy bandwidth. In this work, we combined X-ray phase/dark-field tomography with a stroboscopic technique, which synchronizes image acquisitions with repetitive tension applied to a sample. Snapshot images with a 200 μs temporal resolution are measured to reconstruct phase/dark-field tomograms. A result of stroboscopic X-ray dark-field tomography is described, which was obtained for a rubber sample under a 24 Hz repetitive compression-stretch motion of a 10 mm amplitude.
EFFECT OF SEXUAL BEHAVIOR AND PHEROMONES OF OVARIECTOMIZED FEMALE ON TESTOSTERONE PLASMA LEVELS IN MALE WISTAR RAT

Abstract. Background: Pheromones are responsible for sexual and social behavior among members of the same species. In this research, the goal was to examine the changes of testosterone plasma levels in male Wistar rats that have been faced with sexual behavior and pheromones of ovariectomized female . Materials and Methods: In this experimental-interventional study 48 rats were divided into three groups (control , experimental 1and 2), each group contained 8 male and 8 female. After 24 hour, control and experimental 1 groups remained in their cages; but male rats in experimental 2 group, were separated from their pregnant pair and as strange male rat were placed in the neighborhood with another female pregnant of the same group in the special cages that just moved volatile pheromones of female to male. (Experimental 1, 2 groups female rats, 17th day postpartum were ovariectomized). In the first day from control group mail rats and10th day of pregnancy, 14th and 24th days of postpartum, blood samples were collected from all male rats. Data based on editing 19 SPSS statistical program and one-way ANOVA statistical methods were analyzed. The significance level For results (P≥0.05) significant was considered. Results:. The testosterone plasma levels had significant decrease among experimental 1, 2 groups male rats in 24th day of postpartum. Conclusion: These findings suggest that female rats non-volatile pheromone's (when the ovaries are active) can stimulate sexual behavior in male rats. Keywords: ovariectomy, rat, sexual pheromones, sexual behaviors, testosterone.
Automatic Detection of Nasal Leak in Cleft Palate Speech Based on an Improved Group Delay Method

Cleft palate is a kind of congenital defect, and nasal leak is one of common symptoms in cleft palate speech. The automatic detection of nasal leak in cleft palate speech can provide a certain auxiliary basis for clinical diagnosis. Aiming at the characteristics of nasal leak in cleft palate speech, an improved method based on group delay is proposed. Since group delay parameter can greatly characterize nonlinear signals. Gammatone filter combined with the characteristics of human auditory is used to demodulate speech signal, and group delay parameters are calculated for each frequency band. It is used as features to identify nasal leak in cleft palate speech. The proposed algorithm is tested on 351 speech samples provided by West China Hospital of Stomatology Sichuan University. Compared with the classical group delay method, the accuracy of this improved method is 91.74% for the detection of nasal leak in cleft palate speech.
Do Upfront Investments Increase Cooperation? A Laboratory Experiment

We investigate whether upfront investments increase cooperation in settings with no enforcement mechanism, where cooperation is not easily sustained voluntarily. Such investments are a cost that individuals incur before deciding whether to cooperate and increase cooperation payoff. We find that cooperation rarely emerges in treatments without investments, while both endogenous and exogenous investments boost overall cooperation levels. For low endogenous investments, cooperation is lower than when the same investments are exogenous. For high investments, cooperation is not significantly different between endogenous and exogenous conditions. This supports low investments being interpreted as a signal of unwillingness to cooperate, triggering non-cooperative choices.
Development of Theater Education Textbook with Character Education and Local Wisdom Through Active Learning to Improve the Students’ Script Writing Ability

The purposes of this study are to describe: (1) the students’ and lecturers’ needs on the Theater Education Textbook, (2) the development of the textbook model, (3) the effectiveness testing on the designed textbook, and (4) the result of the textbook dissemination. This research was Research and Development using Borg and Gall’s framework that consisted of 4 stages: (1) exploration, (2) model development, (3) model testing, and (4) dissemination. The research approach used in the exploration stage was a qualitative descriptive approach. The data were collected through documentation study, observation, in-depth interview, and questionnaire. The data were then analyzed by using interactive analysis model. Model testing was done by experimental research. The results of this research were: (1) the exploration stage showed that the Theater Education Textbook used in Undergraduate Elementary Teacher Education (ETE) Program of Sebelas Maret University, Muhammadiyah University, and Slamet Riyadi University Surakarta did not meet the students’ and lecturers’ needs, (2) the theater education textbook was developed through preliminary field testing; and (3) testing phase of textbook effectiveness was done through main field testing. The value of t-obtained is 0.17 and t-table (0.05; 173) = 1.64. Thus, the critical area {t <-1.64 or t> 1.64}. Since t-obtained (0.17) < t-table (1.64) H0: μ1 ≠ μ2 was accepted. This means that the theater script writing textbook with character education and local wisdom was able to improve the students’ scriptwriting ability.
Impact of Entrepreneurship Course on Entrepreneurial Intention among Undergraduate Students of Al-Hikmah University, Kwara State, Nigeria

Unemployment has become one of the most newsworthy concerns in Nigeria. It has reached a level in which graduates of higher institutions have to normally wait for a long time before getting a job. Hence, the importance of entrepreneurship in terms of job creation and economic growth cannot be overemphasized. This research therefore examined the impact of entrepreneurship course on entrepreneurial intention among undergraduate students of Al-Hikmah University, Kwara State, Nigeria. The population of the study consists of 2800 while quota, simple random and convenience sampling techniques were used to 338 students from seven faculties. Instrument titled “Entrepreneurship Course and Entrepreneurial Intention Questionnaire (ECEIQ)” was used to elicit data from the respondents while Statistical Package for Social Sciences (SPSS) was used to analyse the data. Findings revealed that the teaching of entrepreneurship course significantly impact on student's knowledge and their entrepreneurial intention to start-up business. Findings also indicate perceived challenge which include poor state of infrastructure (electricity, road etc.) and lack of fund dampen students’ spirit of becoming an entrepreneur. Also, availability of infrastructure and fund are students’ perceived solutions to the challenges of becoming an entrepreneur. In view of the findings, this study therefore recommends that university should device a strategy to assist the students that indicate intention to start enterprise while in school and after graduation. Lastly, stakeholders in education should assist the university in providing equipment and funds for the advancement of entrepreneurship.
Hydraulic anti-lock braking control strategy of a vehicle based on a modified optimal sliding mode control method

The objective of this paper is to propose a modified optimal sliding mode control method for the hydraulic anti-lock braking system of a vehicle to achieve both robustness and optimal control performance. The longitudinal dynamic model of a vehicle, tyre model and hydraulic anti-lock braking system model are established, and the weakness of the common optimal sliding mode control method in designing the anti-lock braking system controller is analysed synthetically. The analyses form the basis for tracking an ideal slip ratio. A new modified optimal sliding mode controller is proposed to regulate the hydraulic anti-lock braking system for a better braking performance and robustness: the optimal sliding mode manifold function includes several virtual damping elements and infinitely small-sized items to meet the working conditions of the current optimal sliding mode control method. The control results of the proposed controller are compared with those of the common sliding mode controller. Simulation results under various road conditions demonstrate that the modified optimal sliding mode controller not only has strong robustness against uncertainties in the road adhesion coefficient but also achieves better control performance of the slip ratio.