Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of problem solving ability of high school students through learning with real engagement in active problem solving (REAPS) model on the concept of heat transfer

This study aims to influence the enhancement of problem solving ability before and after learning using Real Engagement in Active Problem Solving (REAPS) model on the concept of heat transfer. The research method used is quantitative method with 35 high school students in Pontianak as sample. The result of problem solving ability of students is obtained through the test in the form of 3 description questions. The instrument has tested the validity by the expert judgment and field testing that obtained the validity value of 0.84. Based on data analysis, the value of N-Gain is 0.43 and the enhancement of students’ problem solving ability is in medium category. This was caused of students who are less accurate in calculating the results of answers and they also have limited time in doing the questions given.
Visual recognition of human communication

The objective of this work is visual recognition of speech and gestures. Solving this problem opens up a host of applications, such as transcribing archival silent films, or resolving multi- talker simultaneous speech, but most importantly it helps to advance the state of the art in speech recognition by enabling machines to take advantage of the multi-modal nature of human communications. However, visual recognition of speech and gestures is a challenging problem, in part due to the lack of annotations and datasets, but also due to the inter- and intra-personal variations, and in the case of visual speech, ambiguities arising from homophones. Training a deep learning algorithm requires a lot of training data. We propose a method to automatically collect, process and generate a large-scale audio-visual corpus from television videos temporally aligned with the transcript. To build such dataset, it is essential to know 'who' is speaking 'when'. We develop a ConvNet model that learns joint embedding of the sound and the mouth images from unlabelled data, and apply this network to the tasks of audio-to-video synchronisation and active speaker detection. Not only does this play a crucial role in building the dataset that forms the basis of much of the research done in this thesis, the method learns powerful representations of the visual and auditory inputs which can be used for related tasks such as lip reading. We also show that the methods developed here can be extended to the problem of generating talking faces from audio and still images. We then propose a number of deep learning models that are able to recognise visual speech at word and sentence level. In both scenarios, we also demonstrate recognition performance that exceeds the state of the art on public datasets; and in the case of the latter, the lip reading performance beats a professional lip reader on videos from BBC television. We also demonstrate that if audio is available, then visual information helps to improve speech recognition performance. Next, we present a method to recognise and localise short temporal signals in image time series, where strong supervision is not available for training. We propose image encodings and ConvNet-based architectures to first recognise the signal, and then to localise the signal using back-propagation. The method is demonstrated for localising spoken words in audio, and for localising signed gestures in British Sign Language (BSL) videos. Finally, we explore the problem of speaker recognition. Whereas previous works for speaker identification have been limited to constrained conditions, here we build a new large-scale speaker recognition dataset collected from 'in the wild' videos using an automated pipeline. We propose a number of ConvNet architectures that outperforms traditional baselines on this dataset.
Recent development in electronic nose data processing for beef quality assessment

Beef is kind of perishable food that easily to decay. Hence, a rapid system for beef quality assessment is needed to guarantee the quality of beef. In the last few years, electronic nose (e-nose) is developed for beef spoilage detection. In this paper, we discuss the challenges of e-nose application to beef quality assessment, especially in e-nose data processing. We also provide a summary of our previous studies that explains several methods to deal with gas sensor noise, sensor array optimization problem, beef quality classification, and prediction of the microbial population in beef sample. This paper might be useful for researchers and practitioners to understand the challenges and methods of e-nose data processing for beef quality assessment.
The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6

Abstract. The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.
Unbiased Decoder Learning for Fast Image Style Transfer

Image style transfer is one of the computer vision applications related to deep machine learning. Since the proposal of the first online learning approach of single layered neural network called neural style, image style transferring method has been continuously improved in processing speed and style capacity. However, controlling the style strength of image has not been investigated deeply. As an early stage of research for style strength control, we propose a method of style manifold learning in image decoder which can generate unbiased style image for image style transfer.
The legacy of Norbert Giambiasi to the University of Corsica: from behavioral testing to DEVS fault simulation

The paper delivers a tribute to Professor Giambiasi by pointing out how the work and ideas stemming from the research performed under his umbrella in the 1980s on behavioral testing has been an inspiration for the research around DEVS formalism at the University of Corsica in the late 1990s and the beginning of the new century. The paper retraces how Giambiasi’s heritage allowed researchers at the University of Corsica to go from behavioral and hierarchical testing to DEVS fault simulation.
Anomalous behaviour detection using heterogeneous data

Anomaly detection is one of the most important methods to process and find abnormal data, as this method can distinguish between normal and abnormal behaviour. Anomaly detection has been applied in many areas such as the medical sector, fraud detection in finance, fault detection in machines, intrusion detection in networks, surveillance systems for security, as well as forensic investigations. Abnormal behaviour can give information or answer questions when an investigator is performing an investigation. Anomaly detection is one way to simplify big data by focusing on data that have been grouped or clustered by the anomaly detection method. Forensic data usually consists of heterogeneous data which have several data forms or types such as qualitative or quantitative, structured or unstructured, and primary or secondary. For example, when a crime takes place, the evidence can be in the form of various types of data. The combination of all the data types can produce rich information insights. Nowadays, data has become ‘big’ because it is generated every second of every day and processing has become time-consuming and tedious. Therefore, in this study, a new method to detect abnormal behaviour is proposed using heterogeneous data and combining the data using data fusion technique. Vast challenge data and image data are applied to demonstrate the heterogeneous data. The first contribution in this study is applying the heterogeneous data to detect an anomaly. The recently introduced anomaly detection technique which is known as Empirical Data Analytics (EDA) is applied to detect the abnormal behaviour based on the data sets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev Inequality) can be applied to any data distribution. Then, the second contribution is applying image data. The image data is processed using pre-trained deep learning network, and classification is done using a support vector machine (SVM). After that, the last contribution is combining anomaly result from heterogeneous data and image recognition using new data fusion technique. There are five types of data with three different modalities and different dimensionalities. The data cannot be simply combined and integrated. Therefore, the new data fusion technique first analyses the abnormality in each data type separately and determines the degree of suspicious between 0 and 1 and sums up all the degrees of suspicion data afterwards. This method is not intended to be a fully automatic system that resolves investigations, which would likely be unacceptable in any case. The aim is rather to simplify the role of the humans so that they can focus on a small number of cases to be looked in more detail. The proposed approach does simplify the processing of such huge amounts of data. Later, this method can assist human experts in their investigations and making final decisions.
Optimizing the Prediction of Bagging and Boosting

Background/Objectives: Since more than a decade, Ensemble methods like Bagging and Boosting have drawn great attention by the researchers aiming to improve the prediction accuracy over single classifiers Despite Some recent studies have noticed that Bagging and Boosting does not always improve the accuracy, it enhances the accuracy only if the classifier is unstable classifier. To overcome this problem, a Hybrid Ensemble Model with two phases of preprocessing is proposed in this paper and evaluated using 9 classifiers on 3 benchmark data sets of UCI Repository. Methods: In the first phase of preprocessing feature selection is performed using CFS to select the attributes highly correlated to the class and in the second phase K-means clustering algorithm is applied to remove the noisy instances. Finally, the resultant instances from the previous stages are trained with Bagging and Boosting ensembles to build the final Hybrid Ensemble classifier Model (HECM) using 10 fold cross validation. The result was evaluated using the confusion matrix and the performance measures like accuracy, kappa, mean absolute error and time to build the model. Findings: Results proved that proposed model is more efficient than the existing models and showed improved accuracy for both stable and unstable classifier ranging from 2% to 30.14% over traditional ensemble model depending upon the complexity of the algorithm.
Integrated Real-Time Big Data Stream Sentiment Analysis Service

Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.
The Control of the Reach-to-Grasp Movement

In this chapter, we showed that a close examination of the task geometry can explain how our digits move when we reach out to grasp an object. The laws that govern this behavior are similar to the ones that describe single-digit goal-directed movements. As with any movement, the selection of endpoint depends on why one is making the movement, and the trajectory is under continuous visual guidance, influenced by constraints imposed by obstacles and by the need to be precise.
Critical Reflections on System Dynamics and Simulation/Gaming

Purpose. This article critically discusses the contributions in this symposium and the progress in the fields of system dynamics and interactive learning environments. Two Perspectives. For this purpose, the article uses two different perspectives. One perspective is system dynamics or model-based policy analysis and design. The other perspective is the learning sciences, that is, the theories and principles of learning. Commentaries. With these two perspectives in mind, this article reflects on each of the eight contributions to this symposium and gives critiques and suggestions. Recommendation. The results and insights generated by these contributions suggest that the greatest need for future research on system dynamics and its contribution to simulation-gaming is demonstration of improvements in learning and performance.
Modeling, Control, and Optimization for Diesel-Driven Generator Sets

Diesel-driven generator sets constitute an important element in a broad spectrum of distributed electrical power generation applications worldwide. Due to their high reliability and low cost, diesel-driven generator sets continue to be the preferred choice whenever the power infrastructure is weak or requires backup; from failsafe applications, such as hospitals, to interim mains establishment and expansion applications. Diesel-driven generator sets are subject to increasing demands regarding efficiency and performance, yet at the same time they must be easy to commission and robust to disturbances. In order to reach these demands, the industry is currently directing its attention towards implementation of more sophisticated control techniques. In light of this, the present thesis addresses the following two important issues in relation to diesel-driven generator set control. First, current industry-standard control solutions for individual dieseldriven generator sets are based on classical proportional-integral-derivative regulation principles. One drawback of the current solutions is the built-in requirement of time-consuming manual regulator parameter tuning, which to a large extent determines the system performance. Due to this rather decisive human involvement suboptimal and/or inconsistent performance must often be expected. To address this issue, a first principles-based model is derived and a control scheme designed for one of the fundamental operating modes, that is, frequency and voltage regulation, paying particular attention to ease of commissioning and insensitivity to load changes. In addition to simplifying the parameter tuning process, the established solution facilitates a potentially fully automated parameter tuning solution. The control design approach is implemented on two diesel-driven generator sets, demonstrating clear benefits in end-user simplicity and system performance. Second, improvements in systemwide fuel efficiency hold great potential financial and environmental benefits in applications including large numbers of diesel-driven generator sets operated continuously. Current marketleading solutions utilize a repetitive Unit Commitment scheme based on predefined priority parameters; disregarding live system information, which is becoming increasingly available due to advances in communication and data
Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments

Summary Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to wellknown microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.
Design and Performance of Deep Excavations in Soft Clays

The paper discusses major design aspects related to deep excavations in soft clays including; bottom heave stability; deformations and loads on the supporting structure; methods to improve stability and limit displacements; lessons learned from failures; and finally design principles and safety aspects. The various issues are illustrated by both parametric finite element studies and experiences gained from specific case histories. The results show a strong correlation between bottom heave stability, loads and displacements, and significant arching effects when the bottom heave safety factor is low. 2 and 3D FEM analyses confirm the applicability of traditional limit equilibrium bottom heave stability analyses, provided a search for critical failure surface is made and toe penetration of the supporting wall is accounted for. A concept based on using diaphragm wall with cross walls below the base is documented to be particularly effective in improving stability and limiting displacements. Ground improvement by deep mixing or jet-grouting has also been extensively used for this purpose and provides versatile design options. Some lessons learned from failures are highlighted and measures to avoid failures discussed. It is recommended to use continuum type FEM programs for design, but their use require a good understanding of soil models to be used in the analyses. It is observed that soil parameters for use in design are often based on rather poor and rudimentary soil investigations, an issue which it is of prime importance for the geotechnical profession to face up to. When using ULS safety principles in design, the use of factored strengths may lead to unreasonable design loads. FEM based design analyses should therefore be based on using slightly conservative characteristic strength and stiffness values. The resulting characteristic loads in the support structure must then be multiplied with an appropriate load factor to arrive at the design loads.
Optimization techniques for distributed verilog simulation

Moore's Law states that computational power will roughly double every 18 months. To the semiconductor designer, this means the never-ending challenge of bringing increasingly larger and more complex ICs (Integrated Circuits) to market. It is well known that the principle bottleneck in circuit design is simulation. Uniprocessor simulators may not be able to keep up with increased demands on them for both speed and memory. This thesis has three main contributions. The first contribution is a distributed Verilog simulation environment which can be executed on a cluster of workstations using a message-passing library such as MPI (Message Passing Interface). It employs OOCTW as the synchronization backend and takes advantage of the open source code of Icarus Verilog simulator. It is designed to be flexible for future extension and optimization. To our knowledge, DVS is the first distributed Verilog simulator. The second contribution is event reconstruction, a technique which reduces the overhead caused by event saving. As the name implies, event reconstruction reconstructs input events and anti-events from the differences between adjacent states, and does not save input events in the event queue. Memory consumption and execution time of event reconstruction are compared to the results obtained by dynamic checkpointing revealing that event reconstruction yields a significant reduction in memory utilization and leads to a faster simulation. The third contribution is a multiway design-driven iterative partitioning algorithm for Verilog based on module instances. We do this in order to take advantage of the design hierarchy information contained in the modules and their instances. A Verilog instance is represented by one vertex in a circuit hypergraph. The vertex can be flattened into multiple vertices in the event that an adequate load balance is not achieved by instance based partitioning. In this case the algorithm flattens the largest instance and moves gates between the partitions in order to improve the load balance. The algorithm produces a 4.5 fold reduction in cutsize compared to the hmetis [6] partitioning algorithm. The reduction in cut size and the preservation of locality in the design hierarchy lead to a speedup of 1.91 on four machines compared to the sequential simulation.