Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a participatory design process in designing an educational mobile game for primary school students: a case study in Malaysia

Over the years, the technology integration has been increasingly evident in the higher education institutions. Nevertheless, traditional teaching and learning methods are still largely implemented in the primary schools of the developing countries. Despite the teachers and the students being technology savvy, passive learning is still being practiced in the primary schools of Malaysia. The digital educational game is one of the alternative assessment options capable of enhancing the learning experience of the students. The educational game design is crucial in attracting the attention of the students while fulfilling their learning needs. This research, therefore, aimed to identify an optimal participatory design (PD) process with a suitable mobile game design framework. This research is now at its third phase. All the inputs have been conceived from the previous phase into this phase. Purposive sampling and interview have been done in the first phase and the input for the following phase of the game workshop setting has been delivered. In the third phase, the participants will design an educational game based on the input from the second phase, where the data were qualitatively collected using observation, picture and field notes. Finally, to map the findings from the third phase to the final stage, it was suggested that a game design document (GDD) should be incorporated in the future research works as the final input to complement the game prototype phase.
An Improved Algorithm of UKF-SLAM Based on RBF Neural Network Adaptive Robot

For Extend Kalman Filtering (EKF) algorithm in the robot simultaneous localization and mapping (SLAM), the linearization of the nonlinear system leads to the error of the system state equation, the large amount of computation caused by the Jacob matrix calculation, and the noise model uncertainty. Filtering instability and other issues. A modified UKF-SLAM algorithm based on RBF network adaptive is proposed. In the absence of prior noise information, the algorithm identifies the process noise and observation noise through RBF network adaptive identification unit, and iteratively corrects the noise covariance and the mean filter new covariance, so that the real-time positioning accuracy of the robot is improved. The experimental results show that this method has higher positioning precision and adaptive ability compared with the EKF algorithm and UKF algorithm.
Tracking facilitates 3-D motion estimation

The recently emerging paradigm of Active Vision advocates studying visual problems in form of modules that are directly related to a visual task for observers that are active. Along these lines, we are arguing that in many cases when an object is moving in an unrestricted manner (translation and rotation) in the 3D world, we are just interested in the motion's translational components. For a monocular observer, using only the normal f lowthe spatio-temporal derivatives of the image intensity function we solve the problem of computing the direction of translation and the time to collision. We do not use optical flow since its computation is an ill-posed problem, and in the general case it is not the same as the motion field the projection of 3D motion on the image plane. The basic idea of our motion parameter estimation strategy lies in the employment of fixation and tracking. Fixation simplifies much of the computation by placing the object at the center of the visual field, and the main advantage of tracking is the accumulation of information over time. We show how tracking is accomplished using normal flow measurements and use it for two different tasks in the solution process. First it serves as a tool to compensate for the lack of existence of an optical flow field and thus to estimate the translation parallel to the image plane; and second it gathers information about the motion component perpendicular to the image plane.
Overall Web Rating into Single Standard Five-Scale Rating

: Product recommendation is the key factors for convincing the customer in a right way, to buy the products in any e-commerce website. The online customer ratings are based on each customer actual experience which can incorporate meaningful and more user-oriented information. The recommendation might be based on cost, feature, previously purchased brand and many more. In this project we aim to enhance the recommendation service by evaluating standard rating out of all the ratings available in various e-commerce website for any particular product. The result is that the number of products will be reduced up to one third of total number of products available under the category the user is willing to purchase product. Finally very few products will be recommended to the user by applying user-provided filtering features and single five-scale rating.
Tuning a Fully Convolutional Network for Velocity Model Estimation

Different parameters of a fully convolutional network (FCN) are experimented to evaluate which combination predicts sound velocity models from a single configuration of seismic modeling. The evaluation is made considering some fixed parameters of the deep learning model, such as number of epochs, batch size and loss function, but with variations of the optimizer and activation function. The considered optimizers were RMSprop, Adam and Adamax, whilst the activations functions were the Rectified Linear Unit (ReLU), Leaky ReLU, Exponential Linear Unit (ELU) and Parametric ReLU (PReLU). Five metrics were used to evaluate the model during the testing stage: R2, Pearson's r, factor of two, mean absolute error and mean squared error. To the extent of these experiments, it was found that the optimizers have much more influence than the activation functions when determining the resolution of the output model. The best combination was the one using the PReLU activation function with the Adamax optimizer.
TagSort: Accurate Relative Localization Exploring RFID Phase Spectrum Matching for Internet of Things

The radio frequency identification (RFID) technologies, which have been widely adopted in different Internet of Things (IoT) applications, are the fundamental building block for achieving smart factories, smart logistics, smart stores, etc. Besides knowing the ID of tags, the relative location information of different tags is of great importance since it contains the spatial relationship among different tags, which is essential for many object localization applications beyond the capacity of absolute localization approaches. In this article, we propose TagSort, an RFID-based sorting system that exploits the physical layer information, i.e., the phase of RFID wireless signals to achieve the relative localization of different tags. Several novel filtering and peak detection algorithms are proposed to achieve accurate and robust detection of the order of tags. Extensive evaluation shows promising results (over 95% accuracy) and make TagSort a promising system for future RFID sorting systems, thus enabling a variety of IoT applications and services.
A novel hierarchical clustering approach based on data gravitation model

The purpose of clustering is to group the data points into several clusters according to their similarity. Inspired by law of gravitation, each data point considered as an object is exposed in data space and influenced by the data gravitation of the other data points. In this paper, a novel hierarchical clustering approach (NGHC) is proposed based on local data gravitation. Firstly, the dataset is partitioned into some clusters as intermediate result by a simple gravity-based clustering approach. Then the intermediate clusters are merged by a new linkage measure which combines the data gravitations between two clusters until the satisfactory result is obtained. Two real-world datasets are used to validate the clustering performance of NGHC compared with five representative clustering algorithms. The results exhibit that the NGHC algorithm obtains the best performance on low- and high-dimensional datasets.
Object Graph Networks for Spatial Language Grounding

Consider a domestic robot being asked to pick up "the cup nearest to the plate". Natural language is an intuitive way for humans to interact with robots. However, enabling robots to comprehend natural language, and correctly interpret spatial references, is challenging for two reasons. Firstly, phrases must be semantically represented in structures that can be processed computationally; secondly correspondences must be found to map these structures to models that represent objects, relationships and actions in the environment. Recently neural networks have demonstrated a strong potential to address both challenges, most notably in the context of Visual Question Answering (VQA) where they have performed well at answering natural language questions about images. However, the state-of-the-art networks for VQA tasks are not directly applicable to robotic applications. They do not support interfaces suitable for integration with a robotic system and most have a limited capacity to interpret spatial phrases. In this paper we present a neural network architecture trained on synthetic data and evaluated on synthetic and real data. It correctly interprets referring spatial relationships in phrases such as the one above and provides a modular interface that allows a robot to localise an object in the environment from such a phrase.
KINO: an approach for rule-based chatbot development, monitoring and evaluation

Chatbots are software conversational agents invented in the 1960’s that answer in human language. There are still many challenges and opportunities to be explored, even though more than fifty years have gone by since the first chatbot was created. The aim of this research is to develop the implementation, evaluation, and monitoring for a rule-based chatbot. Having this in mind, KINO, a bot that answers questions about movies, was developed. KINO’s architecture is presented, along with challenges and proposed solutions described as a reference for the development of other chatbots. An experiment was performed on Facebook with more than 300 users generating automated metrics of assertiveness and feedback for further evaluation and monitoring. These metrics were validated using different Brazilian Portuguese text normalization methods: RiveNorm, Enelvo, and UGCNormal. As a result, the feasibility of evaluation and monitoring proposed has been demonstrated, yielding various analyses. Furthermore, the analyses of the collected metrics have shown issues that allow future maintenance and possible improvements of KINO.
Classification of Sambas Traditional Fabric “Kain Lunggi” Using Texture Feature

Traditional fabric is a cultural heritage that has to be preserved. Kain Lunggi is Sambas traditional fabric that saw a decline in its crafter. To introduce Kain Lunggi in a broader national and global society in order to preserve it, a digital image processing based system to perform Kain Lunggi pattern recognition need to be built. Feature extraction is an important part of digital image processing. The visual feature that does not represent the character of an object will affect the accuracy of a recognition system. The purposes of this research are to perform feature selection on sets of feature to determine the best feature that can increase recognition accuracy. This research conducted in several steps which are image acquisition of Kain Lunggi pattern, preprocessing to reduce image noise, feature extraction to obtain image features, and feature selection. GLCM is implemented as a feature extraction method. Feature extraction result will be used in a feature selection process using CFS (Correlation-based Feature Selection) methods. Selected features from CFS process are Angular Second Moment, Contrast, and Correlation. Selected features evaluation is conducted by calculating classification accuracy with the KNN method. Classification accuracy prior to feature extraction is 85.18% with K values K=1 ; meanwhile, the accuracy increases to 88.89% after feature selection. The highest accuracy improvement of 20.74% in KNN occurred when using K value K= 4.
Exploiting Chaos to Suppress Spurious Tones in General Double-Loop EA Modulators

Conventional CA modulators are operated exclu- sively in the nonchaotic regime in which the internal integrators are stable. Theoretical interest in chaotic CA modulators recently arose, mainly because the circuits are simple and strongly nonlin- ear systems with rich behavior, not unlike Chua's circuit (l), (2). It has since been recognized that the application of conventional CA modulators to A/D and D/A conversion may be haunted by spurious "tones" in the output, and it has been proposed to use chaotic modulators to overcome that problem. This paper employs a semi-analytical technique to solve the design problem for general chaotic double-loop modulators with constant inputs. Using the results, optimized scaling factors are easily found for given integrator pole locations, such that an approximate performance measure is maximized subject to bounds on internal state variables. The clarification of this explicit tradeoff between tone rejection through chaos, system stability, and practical per- formance may help to overcome some understandable skepticism in the community of CA designers.
Deduplicação de Contatos em Dispositivos Móveis Utilizando Similaridade Textual e Aprendizado de Máquina

This paper presents a method that identifies duplicate contacts, i.e., records representing the same person or organization, automatically collected from multiple data sources. Contacts are compared using several similarity functions, of which scores are combined by a classification model based on decision trees, which eliminates the need for an expert to manually configure similarity thresholds. The experiments show that the proposed method correctly identified up to 92% of duplicate contacts.
ON THE STATISTICAL ANALYSIS AND PROBABILISTIC MODELING OF SHIP MANEUVERING RESULTS FOR WATERWAY DESIGN

This paper presents a new approach to estimate the probabilities of ship accident (collision or grounding) on the basis of ship maneuvering results. Assuming that trajectories of ship track or swept path are considered as the response ensemble of either a stationary or non-stationary random processes, the study then concentrates on estimating the response characteristics by analyzing of their power spectrum density. Finally, the extreme statistics of a ship exceeding the channel limits are determined on the basis of this information. In that way, it subsequently becomes possible to study channel width and channel depth in an integrated manner. A real probabilistic model of ship grounding risk for the whole channel has therefore been established. Numerical examples have been given and the proposed approach has been quantitatively evaluated.
A Methodology for the Robust Estimation of the Radiation Pattern of Acoustic Sources

We propose a novel methodology for estimating the radiation pattern of acoustic sources, which is general enough as to be suitable for a wide variety of sources without the need of anechoic conditions of operation. Multiple plenacoustic cameras (which can be thought of as arrays of acoustic cameras) scan the source while keeping reflections and interferers at bay through deconvolution and windowing of the measured response. In the case of a moving source (e.g. a musical instrument while it is being played), the plenacoustic cameras are also used for tracking the position of the source. As for its orientation, we propose practical solutions for tracking that as well, whenever such information is not known in advance. Two experiments are conducted in order to validate the proposed solution. The former focuses on a commercial loudspeaker cabinet, whose radiation pattern is known in advance and can be used as groundtruth. The latter concerns violins, which exhibit an extremely rich and hard to predict acoustic behavior, due to their inherent structural and constructional complexity. Our method allows us to capture the radiation pattern of the instrument while it is being played, thus returning data corresponding to the natural timbre of the instrument, including the unavoidable acoustic shadow of the violinist's head. Experimental results confirm a relevant improvement in accuracy and robustness afforded by the adoption of dynamic plenacoustic solutions with respect to state-of-the-art techniques.
Research on Question Answering Technology Based on Bi- LSTM

Question Answering (QA) is always the key issue in the Natural Language Process (NLP). This paper mainly researches the question answering model based on Bi-directional Long Short Term Memory networks (Bi-LSTM), with the use of the WebQA dataset to train the model. Experiment results show that ACC@1 in Bi-LSTM model is 55.00%, ACC@3 is 73.24%, and ACC@10 reaches to 86.64%. The Bi-LSTM model outperforms Best Match 25 (BM25) algorithm in all of the three metrics, which proves the superiority of Bi-LSTM model.
An Abstract Syntax Tree Encoding Method for Cross-Project Defect Prediction

In the last few years, with the development of deep learning theory, researchers have tried to introduce the method of artificial intelligence into the field of software defect prediction (SDP) to improve its prediction effect. To be fed into the neural network, the sample codes are represented as an abstract syntax tree (AST), and the AST is encoded as real numbers. However, in most cross-project defect prediction (CPDP) task, the method for converting the AST into a real number cannot effectively estimate the semantic distance between the ASTs, resulting in a significant reduction in training effects. To solve that problem, we present a new encoding framework, tree-based-embedding (TBE), to convert AST into real vectors and make the semantic gap between the ASTs measurable. To estimate the effect of this encoding method, we promise a tree-based-embedding convolutional neural network with transferable hybrid feature learning (TBCNN-THFL) to perform the CPDP tasks. TBCNN-THFL is fed data encoded with TBE method for learning the transferable joint features between different projects; meanwhile, TBCNN-THFL introduces a transfer component analysis algorithm. Furthermore, the model combines the handcrafted and deep-learning-generated features and then feeds them into the classifier to train a defect prediction model. A sufficient number of experiments demonstrate that TBCNN-THFL is superior to referential models on 72 pairs of CPDP tasks formed by 9 open-source projects.
Using of Metaheuristics for Optimizing Satellite Image Registration

The work presented in this paper focuses on the use of optimization techniques for the satellite images registration (SIR). The idea was reduction in treatment execution time and improving the quality of results. Our contribution is mainly in the introduction of the concept for interesting points in an image (characteristic points) which, once extracted, will be associated with an estimator similarity type SSD (Sum of Squared Differences) for the assessment of the digital image registration operation. In addition to the above work, we did the mathematical modelling of the images registration problem, as well as we adapted and implemented the resolution method to the image registration problem. Our main contribution enables us to; adapt two optimization metaheuristics, namely Simulated Annealing and genetic algorithms, in their sequential and parallel versions; and the substantial reduction in the complexity of processing of image registration. This work can be extended to other fields like, the medical imaging, territory surveillance, the environmental protection etc
An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing.

Background Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. Purpose Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. Methods We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X. Results During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. Conclusions Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.
CNNP-v2: A Memory-Centric Architecture for Low-Power CNN Processor on Domain-Specific Mobile Devices

An energy-efficient memory-centric convolutional neural network (CNN) processor architecture is proposed for smart devices such as wearable devices or the internet of things (IoT) devices. To achieve energy-efficient processing, it has two key features: First, 1-D shift convolution PEs with fully distributed memory architecture achieve 1.5TOPS/W energy efficiency, and it can be boosted up equivalent 3.1TOPS/W energy efficiency with separable filter approximation and transpose-read SRAM. Compared with conventional architecture, even though it has massively parallel 1024 MAC units, it achieves high energy efficiency by scaling down the voltage to 0.46V due to its fully local routed design. Second, fully configurable 2-D mesh core-to-core interconnection support the various size of input features to maximize utilization. The proposed architecture is evaluated 16mm2 chip, which is fabricated with a 65nm CMOS process. As a result, it performs real-time face recognition with the only 68.9mW at 40MHz and 0.6V.