Reviewerly/exHarmony huggingface dataset:
X-Ray Image Enhancement Using Blind Denoising Neural Networks

X-ray imaging is a common medical imaging technology, which plays an important role in assisting doctors in diagnosis. However, the quality of X-ray images is often disturbed by noise in practical applications, which affects the diagnosis of the disease. Although several works have discussed the X-ray images denoising algorithms, their performance needs further improvement. In order to improve the quality of X-ray images, we propose a blind denoising algorithm based on convolutional neural network (X-BDCNN) with a more reasonable noise model. The noise model is designed according to the physical principle of the X-ray imaging, which can generate more realistic noisy X-ray images for training. X-BDCNN consists of a noise level estimation subnetwork and a non-blind denoising subnetwork. The noise level estimation subnetwork estimates the noise level of input noisy image so as to promote the performance of denoised image in the other subnetwork. Additionally, we add a SSIM loss function for X-BDCNN to further improve the quality of denoised images. The experiments under different noise levels demonstrate that our X-BDCNN has a superior performance in various evaluation metrics compared with existing denoising methods.
Processing of sheath shape of the Fatmawati MRT station in South Jakarta using Parametric Method

Abstract This research aims to reduce solar radiation on elevated MRT station Fatmawati in South Jakarta. The method used is a parametric method using simulation software to determine the solar radiation and the Mean Radiant Temperature (MRT). Analysis building skin forms processing through exploration of shapes and use of building materials as a parameter. The result achieved is by using the data of the analysis results can produce skin shape building that can reduce the impact of solar radiation on the station building. (BL)
A Comparison of Evolutionary and Neural Attention Modeling Relative to Adversarial Learning

State-of-the-art machine learning, for computer vision applications, is based on data-driven feature learning. While these extraction paradigms often yield impressive results that outperform human hand-crafted solutions, they unfortunately suffer from a lack of explainability. In response to this, both the neural network and evolutionary communities have provided techniques tailored to visually explain how machines process new observations. Examples range from gradient-weighted class activation mapping to guided backpropagation, and from convolutional matrix transpose to improved evolutionary constructed computing. Previously, we put forth a framework called the Adversarial Modifier Set (AMS). In AMS, adversarial imagery is generated based on evolutionary feature identified regions. In this article, we seek to improve both AMS and general adversarial systems by performing a comparative analysis between neural attention modeling techniques and our previously used evolutionary strategy. Preliminary results on a computer vision dataset show that while the neural techniques are faster, evolutionary algorithms yield diverse and higher fidelity attention maps that give rise to improved features for adversarial learning.
Formulating Infostructure Maturity Model for Disaster Management

The utilization of newly defined term infostructure in disaster management are not adequately explored. A proper evaluation tool need to be developed to assess the infostructure usage in meeting the disaster activities objectives. This paper highlights the development of a maturity model within the context of a disaster process maturity. This paper describes the processes taken to construct a maturity model as an assessment tool to help disaster agencies understand the infostructure capability in managing disaster. The maturity model was built by collecting data from selected disaster agencies in Malaysia. The data collected was analyzed using Rasch Algorithm using Winstep software that produced four output statistics that include the logit values, standard error, infit and outfit of each items. The outputs were used to construct a maturity model of five levels, adopted from Capability Maturity Model. This paper aims to produce a maturity model to assess maturity levels of infostructure used in disaster activities.
Competitive Drone Racing Using Asymmetric Games

This paper presents a game theoretic approach to solve the problem of drone racing. A game theory planner (GTP) strategizes against an opponent by using an iterated best response learning method from game theory. To complement the functionality of the GTP, a minimum jerk polynomial trajectory generation algorithm is used to generate a reference track. Moreover, a time-varying linear model predictive controller (MPC) is used to execute the strategic path generated by the GTP. The performance of the GTP is compared against a pure MPC, a Policy Improvement (PI) racer, and itself under different parameters. Intuitive competitive behaviors such as blocking and overtaking came naturally as a result of the algorithm. Also, interesting match-up and lead-dependent performance advantage is observed.
Text and Web Content Mining: A Systematic Review

Text and content mining are the subcategory of data mining. This category of data mining is used to extract the information from web or web pages of a website. This mining identifies useful information from web content like web pages, search logs, and other website-related content. The extracted information can be used in many applications, for example, we can extract opinions from online sources and web hierarchy which provides better insights and knowledge. In this paper, we conducted a systematic review that included 18 research papers that are relevant to the topic and matches the inclusion criteria of this study. From these research papers we were able to answer the research questions that we identified. The questions are related to the applications, techniques and issues of the text and web mining. The findings suggest that many research papers made a good foundation for this topic, and gave an informative explanation of each type of techniques used in text and web mining, as well as some issues that can be a future work for researchers who are interested in the topic.
Detection of Kinship through Microexpression Using Colour Features and Extreme Learning Machine

Kinship detection based on human face image is something new and quite challenging problem in computer vision pattern recognition. There have been many applications developed to analyze social media and adopted children. Most of the existing kinship methods assume that each pair of images with a positive facial image (with an image confirming kinship) has a greater score for the group of non-negative kinship images. In practice, however, these assumptions are usually over-sampled from real-life palettes. In this research, activity involving microexpression is used as an alternative to the other parameters in kinship detection. Using several reference methods such as color features and extreme learning machines as feature extraction and classification. This set of methods offers the advantages in identifying kinship relationship. Microexpression parameters are widely used as a reference. ELM itself has the advantage of extensive training time and faster performance than other methods. The purpose of this study is to maximize classification performance and minimize errors commonly associated with the compilation of existing decisions on each ELM architecture. The results show that the proposed model produces an accuracy for the ELM training process of 80.06%. This is coupled with satisfactory value for the ELM testing process of 76.31%.
A Robust and Automatic Recognition Method of Pointer Instruments in Power System

Currently, the research on substation automation inspection relies on inspection robots and computer vision technologies. However, due to limitations of existing methods, some existing methods cannot meet the requirements of identifying instruments of wide varieties. Additionally, the applications of these methods are hindered by interferences, such as illumination and shadow. This paper presents an automatic reading method for pointer instruments. First, it proposes an instrument location method based on correlation filtering, which exhibits a good tolerance of severe lighting changes. Second, to identify the pointer angle, which is the major task in inspection, a human vision-inspired method is proposed, which directly reads the orientation of the pointer by Gabor-based filtering. This one-stage pointer angle identification algorithm is superior compared with a popular method based on Hough-transform in terms of robustness and accuracy. Because the latter includes several steps which lead to be fragile in practical applications. The experimental results show that the proposed method exhibits strong robustness against illumination variations and shadows, which is verified in different kinds of instruments.
Scalable Peer-to-Peer Fog Computing Integrated to a Fast-Searching Distributed Blockchain System

Internet of Things (IoT) is becoming a promising area to support communication of all type of devices. Blockchain aids to ensure higher security in this field, but the increase in ubiquitous connectivity leads to increase load and hence it requires a perfect search for the nodes. This paper addresses the issue by the design of a three tier P2p fog-IoT architecture using distributed blockchain. The tier-1 employs an authenticator responsible to authenticate IoT nodes with identity, IP address and physical unclonable function (PUF). To balance IoT nodes request, super peers are dynamically selected from multi-criteria ranking based optimal points (MC-RBOP). Further the requests are forwarded to blockchain present in tier-3. In tier-3 the Master node performs storage and searching. Due to the possibility of redundant data storage, Jaro-Winkler measures a similarity in the data before storing it. An Adaptive Chord with fuzzy neural (AC-FNN) is incorporated to search the lightweight U-QUARK algorithm-based hash key-values in the directory. The design of fog-IoT with new chord algorithm is implemented in network simulator-3 and the results are evaluated in terms of latency, response time, blockchain size and network usage.
Decomposition-and-Fusion Network for HE-Stained Pathological Image Classification

Building upon the clinical evidence supporting that decomposing a pathological image into different components can improve diagnostic value, in this paper we propose a Decomposition-and-Fusion Network (DFNet) for HE-stained pathological image classification. The medical goal of using HE-stained pathological images is to distinguish between nucleus, cytoplasm and extracellular matrix, thereby displaying the overall layouts of cells and tissues. We embed this most basic medical knowledge into a deep learning framework that decomposes a pathological image into cell nuclei and the remaining structures (that is, cytoplasm and extracellular matrix). With such decomposed pathological images, DFNet first extracts independent features using three independent CNN branches, and then gradually merges these features together for final classification. In this way, DFNet is able to learn more representative features with respect to different structures and hence improve the classification performance. Experimental results on two different datasets with various cancer types show that the DFNet achieves competitive performance.
A comparison of cybersickness symptoms across 360-degree hazard perception and hazard prediction tests for drivers

Hazard perception assessment may benefit from VR-presentation by removing field-of-view restrictions imposed by single-screen tests. One concern is whether VR-induced ‘cybersickness’ will offset any benefits. Self-reported cybersickness ratings were recorded from 77 participants viewing two variants of a 360-degree hazard test: hazard perception and hazard prediction. The latter was hypothesised to be particularly susceptible as clips abruptly cut to a probe question at hazard onset. Such sudden occlusions are thought to increase cybersickness. Overall cybersickness levels were low, with only four participants excluded for above-threshold sickness ratings. The remaining participants showed unexpectedly lower symptoms for the hazard prediction test and rated this test format as more comfortable and engaging. These findings mitigate concerns over the use of 360-degree videos in formative hazard assessments, even when clips involve sudden occlusions. Nonetheless, removal of any participants due to cybersickness raises problems for using VR for formal assessments of hazard perception skill.
Virtual Machine Scaling in Autonomic Cloud Resource Management

Virtualization is a technique that uses the hypervisor to create an abstract layer over the underlying hardware and allows the resources of this host such as memory, processor to be multiplexed among the guest operating system. It allows full utilization of the underlying hardware of the physical computer system. The guest operating systems that run over the host are called virtual machines. Sometimes, the load on these virtual machines may increase or decrease suddenly which requires to manage the resources multiplexed to the virtual machine accordingly. This feature is called virtual machine scaling. VM scaling is required for the proper management of the resources or underlying hardware and also for the efficient processing of the tasks in the virtual machines. The main factors that contributed to the need of virtual machine scaling included performance, cost, increased capacity, energy, and availability. Virtual machine scaling technique can be implemented through two methods: horizontal scaling and vertical scaling. The modes or policies which are generally used for VM scaling are categorized as: reactive mode, proactive mode, and combination of both reactive and proactive mode called the hybrid mode. The chapter covers the details of virtual machine scaling, its relation with elasticity, different methods and modes for implementing VM scaling, and also the research challenges faced in this sub-domain of cloud computing. The chapter will cover the main state of the art algorithms under methods and modes for virtual machine scaling.
A Deep Learning Framework for Blended Distortion Segmentation in Stitched Images

The visual quality of stitched images plays an important role to provide the high-quality immersive viewing experience of Virtual Reality (VR) contents. There are several image stitching algorithms that generate panoramas from multiple images taken with different cameras and angle of views. The performance of these stitching algorithms can be measured by estimating the quality of generated panoramas. This paper presents a segmentation-based Stitched Image Quality Assessment (SIQA) approach that captures the blended distortion in stitched images and segments the distorted region using binary mask. The segmented regions provide the location and total area of the distorted region. The results obtained from the experimental evaluation validate the reliability of our method for capturing the blended distortions in stitched images.
Intuitionistic Fuzzy Probability and Almost Everywhere Convergence

The aim of this paper is formulate an almost everywhere convergence using an intuitionistic fuzzy probability. We compare two concepts of almost everywhere convergence and we study $$\mathcal P$$ -almost everywhere convergence of sequence of intuitionistic fuzzy observables induced by Borel measurable function.
Towards Rule-ranking Based Fuzzy Rule Interpolation

Recently proposed methods of TSK inference extension (TSK+) and K closest rules based TSK (KCR) are able to potentially perform fuzzy rule-based inference with a sparse rule base, for regression problems. However, in certain real-world applications, observations may be rather far away from any rules that may be fired in order to implement the required regression and hence, these state-of-the-art techniques may not work satisfactorily. To investigate an alternative to address such practical problems, this paper presents a novel fuzzy rule interpolation (FRI) approach. It works based on integrating feature selection and feature aggregation to attain a rule-ranking list within the given sparse rule base. The process of acquiring an ordered rule list is accomplished offline, with an ordered rule base ready for use prior to the arrival of any observation online. It boosts the computational efficiency of closest rule selection during the FRI process. Initial experimental results demonstrate that the proposed approach is able to address the problems that TSK+ and KCR fail to do while having a similar time efficiency to them. Further, only two nearest rules to the observation are required to derive interpolated results, thereby significantly improving the automation level of the interpolative reasoning system.
Research on Path Planning Algorithm for Mobile Robot Based on Improved Reinforcement Learning

This paper proposes an improved Q-learning algorithm to solve the problem that the traditional Q-learning algorithm is applied to the path planning of mobile robot in complex environment, the convergence speed is slow due to large number of iterations, and even the actual reward signal is sparse so that the agent cannot get the optimal path. The improved algorithm reduces the number of iterative runs of the agent in the path planning process to improve the convergence speed by further updating the iterative formula of the Q-value. At the same time, adding sparse reward algorithm leads to finding the optimal path successfully. In order to verify the effectiveness of the algorithm, simulation experiments are carried out in two groups of environments: the simple and the complex. The final simulation results show that the improved algorithm can avoid obstacles effectively and find the optimal path to the target position after less iterations, which proves that the performance of the improved algorithm is better than the traditional Q-learning in the path planning.
Semantic-Driven 3D Scene Construction Based on Spatial Relationship and Case-Base

At present, 3D scene construction technology has been widely used in various fields. However, 3D scene construction is increasing on the human and material costs, and the production process is also complicated. First, we use a semantic analysis method to achieve better Chinese automatic word segmentation, which is bidirectional matching Chinese word segmentation based on N-gram model. Next, to solve the problems of low degree of automation and intelligence, we propose a new method of 3D scene construction based on spatial relationship and case-base. The objects and spatial relationships extracted from the scene description texts form a spatial constraint in the form of a triple, which is stored in the spatial relationship template library. Then the 3D model in the case-base is invoked to build the scene. This method takes the spatial constraint as the smallest module of the scene construction, which not only accelerates 3D scene construction, but also improves the rationality of the scene layout. At last, we apply this method to scene generation in a strategy game, in which the effectiveness and efficiency of the new method are proved.
Research on Chinese Word Segmentation Based on Conditional Random Fields

Word segmentation is the first step in Chinese natural language processing. The accuracy of segmentation has substantial impacts on subsequent tasks such as part-of-speech tagging, semantic analysis, etc. This research explores the Chinese word segmentation based on Conditional Random Fields (CRFs). First of all, we apply different character templates to conduct feature selection. Baseline and eight templates are combined to construct nine groups of features. Also, Tsai's feature set and our proposed feature set are raised to refine the experimental results. Moreover, several parameters of model are adjusted to make the CRF model more effective and efficient. Finally, different tag set of characters are verified according to different dataset. The 2-Tag set is chosen for character tagging in this research. Experimental results demonstrate that the CRF-based Chinese word segmentation with proposed feature set achieves the best F score.
Case study : Millenium SKP Sdn. Bhd. / Mohamad Asyraf Fahmi Yahya

This study basically is to give an idea on how technology solutions can be apply in real life based on what students had learned about technology entrepreneurship. As a student fromstatistical fields, it is a must for everyone to experience a case study for actual business activity. In order to complete this case study, I got the opportunity to interview the owner of Millenium SKP Sdn Bhd which run business base on plaster ceiling selling and installation services located in Paka, Dungun, Terengganu.Before an actual study was done, some study on articles related to plasterboard industry but there is not much available. This is important to increase the understanding on this industry. After that, collection of the company information is done through interviews using WhatsApp application because of the current situation about pandemic Covid-19. The information gatheredare about company background information, services offered and technologies use in their business. Other than that, the marketing strategies of the product and the services also was asked. Lastly, the company’s strengths, weaknesses, opportunities and threats had been identified through analysis of SWOT.Briefly, some problems facing by the company had been noticed when evaluated the company through SWOT analysis. The company has facing issues and problem on company manpower, their marketing strategies, competitors and issues on supply shortage. Lastly, few great potential and chances to raise company production and sales had been identified if upgrade thetechnology and method use for their services. Therefore, in this report some improvement and technologies will be explained that can help Millenium SKP Sdn Bhd.
Increasing Robustness for Machine Learning Services in Challenging Environments: Limited Resources and No Label Feedback

The importance of deployed machine learning solutions has increased significantly in the past years due to the availability of data sources, computing capabilities and convenient tooling. However, technical challenges such as limited resources and computing power arise in many applications. We consider a scenario where a machine learning model is deployed in an environment where all computations need to be performed on a local computing unit. Furthermore, after deployment, the model does not receive any ground truth labels as feedback. We develop a two-step prediction method which combines an outlier detection with a robust machine learning model. This approach is evaluated based on a data set from a large German OEM. We can show that the prediction performance is increased significantly with our approach while fulfilling the restrictions in terms of memory and computational power. This way, we contribute to the practical applicability of machine learning models for real-world applications.