Miracle-dz/UniFAR-FTUs huggingface dataset:
New Adaptive UKF Algorithm to Improve the Accuracy of SLAM

SLAM (Simultaneous Localization and Mapping) is a fundamental problem when an autonomous mobile robot explores an unknown environment by constructing/updating the environment map and localizing itself in this built map. The all-important problem of SLAM is revisited in this paper and a solution based on Adaptive Unscented Kalman Filter (AUKF) is presented. We will explain the detailed algorithm and demonstrate that the estimation error is significantly reduced and the accuracy of thenavigation is improved. A comparison among AUKF, Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) algorithms is investigated through simulated as well as experimental dataset. An indoor dataset is generated from a two-wheel differential mobile robot in order to validate the robustness of AUKF-SLAM to noise of modeling and observation, and to examine the applicability of the method for real-time navigation. Both experimental and simulation results illustrate that AUKF-SLAM is more accurate than the standard UKF-SLAM and the EKF-SLAM. Finally, the well-known Victoria Park dataset is used to prove the applicability of the AUKF algorithm in large-scale environments.
A conceptual comparison of NSGA-II, OMOPSO and AbYss algorithms

The optimisation of multi-objective problems is currently an important area of research and development. The importance gained by this type of problem has given rise to the development of multi-objective metaheuristics to attain solutions for such problems. In this paper, an experimental comparison of non-dominated sorting genetic algorithm-II (NSGA-II), archive-based hybrid scatter search (AbYss), and optimised multi-objective particle swarm optimisation (OMOPSO) using ZDT benchmark, has been done to determine which multi-objective metaheuristic has the best performance with respect to a problem. The results thus obtained are compared and analysed based on three performance metrics namely hypervolume, GD, and IGD that evaluate the dispersion of the solutions on the Pareto front and its proximity to it.
Software-Based Self-Test of Register Files in RISC Processor Cores using March Algorithms

Beside external test and hardware Built-In Self-Test techniques, a non-intrusive low-cost strategy has been developed: Software-Based Self-Test. SBST component-oriented approach for microprocessor allows to reach, with small self-test programs, a high fault coverage. In this context, this paper focuses on the test of the Register File of RISC processor cores. This component has the structure of a small memory. Then, using the appropriate March tests, 100% fault coverage for numerous fault models can be reached. This method was applied to test the Register File of a MIPS microprocessor. Results show that the method allows to obtain small-sized self-test programs while the use of March tests ensures a high test quality.
Thresholds of Absorbing Sets Under Scaled Min-Sum LDPC Decoding

In this paper, the definition of threshold of elementary absorbing sets is extended to scaled Min-Sum low-density parity-check (LDPC) decoding. Based on the analysis of the behavior of scaled Min-Sum LDPC decoders in the Tanner graph of the absorbing set, it is proven that correct decoding is guaranteed with received channel messages above threshold. A fast algorithm for the threshold computation is derived. Many examples of absorbing sets taken from LDPC codes of various variable node degrees are investigated, and it is shown that all of them can be deactivated with low enough scaling factors.
Audio–Visual Deep Clustering for Speech Separation

Speech separation aims to separate individual voices from an audio mixture of multiple simultaneous talkers. Audio-only approaches show unsatisfactory performance when the speakers are of the same gender or share similar voice characteristics. This is due to challenges on learning appropriate feature representations for separating voices in single frames and streaming voices across time. Visual signals of speech (e.g., lip movements), if available, can be leveraged to learn better feature representations for separation. In this paper, we propose a novel audio–visual deep clustering model (AVDC) to integrate visual information into the process of learning better feature representations (embeddings) for Time–Frequency (T–F) bin clustering. It employs a two-stage audio–visual fusion strategy where speaker-wise audio–visual T–F embeddings are first computed after the first-stage fusion to model the audio–visual correspondence for each speaker. In the second-stage fusion, audio–visual embeddings of all speakers and audio embeddings calculated by deep clustering from the audio mixture are concatenated to form the final T–F embedding for clustering. Through a series of experiments, the proposed AVDC model is shown to outperform the audio-only deep clustering and utterance-level permutation invariant training baselines and three other state-of-the-art audio–visual approaches. Further analyses show that the AVDC model learns a better T–F embedding for alleviating the source permutation problem across frames. Other experiments show that the AVDC model is able to generalize across different numbers of speakers between training and testing and shows some robustness when visual information is partially missing.
The Effects of Cost Management Quality on the Effectiveness of Internal Control and Reliable Decision-Making

This study investigated the consequences of cost management quality on the effectiveness of internal control and reliable decision-making in Thai industrial firms. With this information, the firms were then tested against performance. A sample of 354 new manufacturing industries in year 2017 of Thailand was chosen and data was collected through mailed questionnaires. Only 340 (96.05%) respondents contributed to the database of this report. The result of ordinary least squares regression revealed that the cost management quality was positively related to the internal control effectiveness and decision-making reliability. In addition, internal control effectiveness and decision-making reliability also had positive effects on firm performance. This implied that without accounting information system quality, a firm had a greater chance of failure. Contributions and suggestions for future research are presented.
A Phoneme Sequence Driven Lightweight End-To-End Speech Synthesis Approach

This paper develops an end-to-end neural network model for text-to-speech (TTS) system based on phoneme sequence. Inspired by the Tacotron-2, the proposed model adopts an encoder-decoder model with attention mechanism and applies mel-spectrogram to measure the intermediate acoustic feature. Phoneme sequence is used to replace the character sequence in order to overcome the shortage of the character feature used in Tacotron-2. Unlike the conventional concatenate methodology based TTS system, our model can generate waveform directly from phoneme sequence. In addition, analogue from text analysis, a new analysis methodology is proposed for phoneme analysis. Experiment result on LJ Speech dataset shows that, compared with char-based model, our model can get a comparative or better performance.
SM-RC: A New Security Measurement Method for Inter-Domain Routing System

Inter-domain routing system (IRS) is the key infrastructure of the Internet. It is of great significance to perceive and identify the security threats of IRS for the stable operation of the Internet. Security quantification is an important part of security threat perception. The existing methods are unable to construct accurate abnormal state quantitative models because of limited data. Therefore, only the similarity between the current state and the normal state of IRC can be considered, and the types of security threats cannot be distinguished. Aiming at the above problems, this paper proposes a security measurement method based on the abnormal state modeling. By analyzing the security indicators, when IRS suffering from security events and extracting the common characteristics of the abnormal state, a qualitative model of IRS’s abnormal state is established. Thus, the similarity between the current state and the specific abnormal state has been taken into consideration in measuring the security of IRS. Experiments based on SMW-4 cable fault event and Malaysian route leak event show that the highest detection and classification precision of the former are both 100%, and the highest detection and classification precision of the latter is 96% and 95.8%, respectively, which proves that the measurement results can indicate the occurrence of security threats and their types.
Parallel particle swarm optimization classification algorithm variant implemented with Apache Spark

With the rapid development of technologies such as the internet, the amount of data that are collected or generated in many areas such as in the agricultural, biomedical, and finance sectors poses challenges to the scientific community because of the volume and complexity of the data. Furthermore, the need of analysis tools that extract useful information for decision support has been receiving more attention in order for researchers to find a scalable solution to traditional algorithms. In this paper, we proposed a scalable design and implementation of a particle swarm optimization classification (SCPSO) approach that is based on the Apache Spark framework. The main idea of the SCPSO algorithm is to find the optimal centroid for each target label using particle swarm optimization and then assign unlabeled data points to the closest centroid. Two variants of SCPSO, SCPSO‐F1 and SCPSO‐F2, were proposed based on different fitness functions, which were tested on real data sets in order to evaluate their scalability and performance. The experimental results revealed that SCPSO‐F1 and SCPSO‐F2 scale very well with increasing data set sizes and the speedup of SCPSO‐F2 is almost identical to the linear speedup while the speedup of SCPSO‐F1 is very close to the linear speedup. Thus, SCPSO‐F1 and SCPSO‐F2 can be efficiently parallelized using the Apache Spark framework.
Hierarchical k-Means Algorithm(hk-Means) with Automatically Detected Initial Centroids

— Unsupervised learning is a technique to organize the data into meaningful way having similarity. Cluster analysis is the study of clustering techniques and algorithms which are helpful to discover important patterns from fundamental data without knowledge of category label for further analysis. k-Means algorithm is one of the most popular clustering algorithm among all partition based clustering algorithm to partition a dataset into meaningful patterns. k-Means algorithm suffers from the problem of specifying the number of clusters in advance and often converges to local minima and therefore resulted clusters are heavily dependent on initial centroids. Various methods have been proposed for automatic detection of initial centroids to improve the performance and efficiency of k-Means algorithm. This paper presents an overview of clustering, clustering techniques and algorithms, addressing problems of k-Means algorithm, comparison of different methods for automatic detection of initial centroids and propose a new Hierarchical method(hk-Means) for automatically detection of initial centroids in k-Means algorithm, implementation of traditional k-Means with automatic pre-process dataset to remove noise.
Resonances associated to a closed hyperbolic trajectory in dimension 2

We consider resonances in the semi-classical limit, generated by a single closed hyperbolic orbit, for an operator on ${\bf R}^2$. We determine all such resonancess in a domain independent of the semi-classical parameter As an application we determine all resonances generated by a saddle point in a fixed disc around the critical energy.
Gateway Selection for Downlink Communication in LoRaWAN

The Long Range Wide Area Network (LoRaWAN) standard was mainly developed to meet the requirements of many emerging Internet of Things (IoT) applications as it provides low power and low cost connectivity over long distances. In LoRaWAN, when an end-device transmits an uplink message, gateways that receive this message transmit it to the network server, which, in turn, selects a single gateway to reply to the end-device. LoRaWAN does not specify how to select the gateway. In this paper, we focus on the gateway selection for downlink communications in LoRaWAN in order to improve the throughput of the network. We aim to present and evaluate several algorithms for selecting the best gateway for downlink while increasing LoRaWAN throughput for different types of gateway deployment. We show that the system throughput depends on this deployment and that balancing the number of end-devices per gateway improves the performance compared to choosing the gateway with the highest signal quality.
A game‐theoretic model for users' participation in ephemeral social vehicular networks

Ephemeral social vehicular networks allow for short‐lived communications between occupants. While such transient interactions may provide important usage benefits such as traffic warnings, the reality of short‐lived interactions also poses challenges for deciding to participate in such social networks. In this paper, we develop a game‐theoretic model to understand the role of occupants' incentives to contribute to these ephemeral social networks. We rigorously study the properties of the model and resulting equilibrium outcomes. A further key objective of our work is to evaluate the robustness of our model to different notions of intrinsic motivation for network participation. As a baseline case, we assume that users are homogeneous regarding their motivation. Next, we investigate the impact of different motivational distributions and explore their impact on users' behaviors in a comprehensive simulation setup. We identify the circumstances under which ephemeral social networks are viable and can successfully sustain an increased level of network participation.
GPU implementation of discrete particle swarm optimization algorithm for endmember extraction from hyperspectral image

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.
Cost Forecasting Analysis on Bored and Cast-In-situ Piles in Sri Lanka: Case Study at Selected Pile Construction Sites in Colombo Metropolis Area

Installation of bored and cast-in-situ (CIB) piles is a complicated process and relates to a large number of factors connected with subsurface uncertainties, contractor experience and sitespecific factors. Such factors make it difficult for the estimator to predict the cost of a CIB pile in the tendering stage. This study was carried out to identify most influential factors on cost of CIB pile construction with relevance to local conditions and to develop a cost prediction criterion for CIB construction in Sri Lanka. It was initiated with a study on cost influential factors and estimation models developed in the international context. Then the study proceeded to identify additional specific factors relevant to the local conditions using a questionnaire survey and analysis. Cost prediction models were then developed for local context using regression analysis based on the identified most influential factors, and finally, developed models were validated using actual cost data. It was revealed that the most critical cost affecting factors are pile size, pile drilling time, depth of pile, concrete pouring time, rock socket length, drilling type and weather conditions. Linear cost models developed for each of these factors were amalgamated to one overall cost prediction model. This was found to be successful during the validation with actual costs of executed piling projects. Moreover, it is recommended to be used along with the intuitive judgment of the decision maker and the model should be timely readjusted with updated market rates at least in three-month periods.
SSFD+: A Robust Two-Stage Face Detector

Face detectors based on deep learning have demonstrated great progress in detecting multi-scale faces by using multi-scale feature maps and input pyramids. However, using input pyramids and multi-scale feature maps increases the training difficulty and complexity of the network. In this paper, we focus on achieving comparable performance and simplifying the network architecture for detecting multi-scale faces. To enable network learning of multi-scale facial features from a single-scale feature map and a single-scale input image: 1) we conducted a comparative study to investigate which layer contributes more to detecting multi-scale faces and 2) we designed and implemented a simple network structure to improve the performance of detecting multi-scale faces by incorporating additional contextual information. SSFD+ achieves mAPs of (91.3%, 90.3%, 83.1%) and (92.4%, 90.9%, 83.7%) on the (easy, medium, and hard) subsets of the WIDER FACE validation and testing datasets, respectively, and promising results on the FDDB, PASCAL Faces, and AFW datasets.
Mining big data to help make informed decisions for designing effective digital educational games

ABSTRACT More research is needed on how to best use analytics to support educational decisions and design effective learning environments. This study was to explore and mine the data captured by a digital educational game designed for middle school science to understand learners’ behavioral patterns in using the game, and to use evidence-based findings to inform the design and implementation of the game’s next iteration. The data was from 3,963 sixth-grade students from 31 teachers across nine U.S. states who used the game. The dataset consisted of 3,063,267 lines of log data. The findings showed significant positive relationships between students' performance scores and their tool usage in terms of both frequency and duration of tool use. This was further confirmed by the relationships between different levels of students’ performance and tool usage: the more frequently and longer that students used the tools, the higher their performance scores, and there were significant differences between students with high performance scores and those with low performance scores. Comparing three different cases over the individual days of game use provided further evidence that in all three comparisons, the students who used more essential tools and for longer time had higher performance scores. Implications are discussed.
Online Task Offloading with Bandit Learning in Fog-Assisted IoT Systems

In fog-assisted IoT systems, to achieve best quality of service with ultra-low latency, resource-limited IoT user nodes may offload some tasks to nearby fog nodes, a.k. a. task offloading, to accelerate their processing. However, it remains non-trivial and challenging to decide when and which fog node to offload to. If offloaded, user tasks may experience unexpectedly long latency in face of system uncertainties, such as wireless channel dynamics, variety in task processing time, and resource contention on fog nodes. Moreover, feedback signals such as processing latency can be delayed and even go outdated due to non- stationarity, thereby degrading the effectiveness of system statistic learning and decision making. In this paper, we study task offloading problem for fog-assisted IoT systems in a non-stationary environment with delayed feedback. By leveraging a drift detector and queue methods, we propose TOS-BB and TOS-BS, two online task offloading schemes with bandit learning that endeavor to achieve ultra-low task latency. Simulation results show that both schemes outperform the benchmark while achieving close- to-optimal performance with short task latency.
Anti-Occlusion Moving Target Tracking Method

In the artificial intelligence field, using computer vision to track an object is an important research topic. Especially when the target reappears after being occluded for a while, it is hard to precisely track the moving target again. Therefore, this paper proposes an anti-occlusion target tracking strategy that can overcome the occluded problem. Firstly, to make the target clearer, we design a moving target detection method using the Gaussian mixture background subtraction method based on the wavelet transform, which removes the high-frequency noise of video images. Then, in the tracking process, altered strategies are taken to cope with different occlusion situations, which include three cases: no occlusion, partial occlusion, and severe occlusion. For the first two cases, we use the distance-based Kalman filter method to track the moving target. For the third case, we designed a method that combines the Camshift method with the distance-based Kalman filter method to track moving targets, which is more efficient than only using the distance-based Kalman filter method. According to one of the cases, our program automatically selects the corresponding method. Experimental results show that our strategy can track moving targets accurately whether targets are in occlusion situation or not.
Towards Transforming User Requirements to Test Cases Using MDE and NLP

The behavior, attributes and properties of a software system is represented in a set of requirements that are written in structured natural language and are usually ambiguous. In large development projects, different modeling techniques are used to create and manage these requirements which aid in the analysis of the problem domain. Requirements are later used in the development process to create test cases, which is still mainly a manual process. To automate this process, we plan to use several of the techniques used in model-driven software development and Natural Language Processing(NLP). The approach under consideration is to use a model-to-model transformation to convert requirements into test cases with the support of Stanford CoreNLP techniques. Key to this transformation process is the use of meta-modeling for requirements and test cases. In this paper we focus on creating a comprehensive meta-model for requirements that can represent both use cases and user stories and performing preliminary analysis of the requirements using NLP. In later work we will develop a set of transformation rules to convert requirements into partial test cases. To show the feasibility of our approach we develop a prototype that can accept a cross-section of requirements written as both use cases and user stories.