Reviewerly/exHarmony huggingface dataset:
Evaluation by Simulation of Reaction Forces that Occur in Spherical Joints of Parallel Topology Robots

The paper shows a method of evaluation by simulation of reaction forces that occur in the spherical joints of a parallel topology robot. The geometrical model of the parallel topology robot, operated as a flight simulator, is presented. The structure of the model is of FP3 + 6·SPS + MP3 (or MSSM) type. Based on the input parameters, which are the relative displacements of the driving kinematical joints links versus time, certain motions of the mobile platform are studied. The modeling and simulation processes were accomplished by using the SolidWorks software.
Vision based real-time obstacle avoidance for drones using a time-to-collision estimation approach

Considering the growth of drone usage in various applications in safety, security, and rescue domains, the need to obtain a collision-free flight is a central problem in autonomous drones. In addition, being the environments in these applications mostly unstructured, dynamic, and unpredictable, the capability of reacting in real-time to the environment is paramount. Using the visual information from a monocular camera, this paper proposes an obstacle avoidance technique based on Time-to-Collision (TTC) estimations. An optimal control problem is formulated and solved in real-time using a Model Predictive Control (MPC) approach, in cascade with an attitude controller, to determine the rotors' actuation signals. The MPC algorithm uses the target information from the vision method to lead the drone to a position that ensures a correct tracking in real-time and, simultaneously, use the TTC data to generate potential fields in the vehicle reference frame ensuring obstacle avoidance. The results in simulation show that the drone can accurately compute the TTC, track a target, and maintain a safe distance from obstacles. Moreover, the strategy reveal to be computationally non-demanding, indicating that the obstacle avoidance approach presented can be easily integrated in a real-time solution.
Managing Credit Card Fraud Risk by Autoencoders : (ICPAI2020)

This paper introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder. The trained autoencoder shows the well fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false positive normal transactions is controlled and the loss of false negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained autoencoder on normal transactions. To align the risk assessment of economical and financial estimation, Risk manager can adjust the threshold to meet the risk control requirements. Using 95th percentile as the threshold, the rate of wrongly detecting normal transaction is controlled at 5% and true positive rate is 86%. For 99th percentile threshold, the well controlled false positive rate is around 1% and 83% for the truely detecting fraud activities. The performance of false positive rate and true positive rate are competitive with other supervised-learning algorithms.
A ReMeDI for Microstructure Noise

We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary moments of the microstructure noise process based on high-frequency data, where the noise process could be serially dependent, endogenous, and nonstationary. We characterize the limit distributions of the proposed estimators and construct confidence intervals under infill asymptotics. Our simulation and empirical studies show that the ReMeDI approach is very effective to measure the scale and the serial dependence of microstructure noise. Moreover, the estimators are quite robust to model specifications, sample sizes and data frequencies.
A contemporary investigation of NTFS file fragmentation

There is a significant amount of research in digital forensics into analyzing file fragments or reconstructing fragmented data. At the same time, there are no recent measurements of fragmentation on current, in-use computer systems. To close this gap, we have analyzed file fragmentation from a corpus of 220 privately owned Windows laptops. We provide a detailed report of our findings. This includes contemporary fragmentation rates for a wide variety of image-, video-, office-, database-, and archive-related extensions. Our data substantiates the earlier finding that fragments for a significant portion of fragmented files are stored out-of-order. We define metrics to measure the degree of “out-of-orderedness” and find that the average degree of out-of-orderedness is non-negligible. Finally, we find that there is a significant group of fragmented files for which reconstruction is insufficiently addressed by current tooling.
Sentiment Level Evaluation of 3D Handicraft Products Application for Smartphones Usage

Three-dimensional (3D) technology has attracted users’ attention because it creates objects that can interact with a given product in a system. Nowadays, Thailand’s government encourages sustainability projects through advertising, trade shows and information systems for small rural entrepreneurship. However, the government’s systems do not include virtual products with a 3D display. The objective of this study was four-fold: (1) develop a prototype of 3D handicraft product application for smartphones; (2) create an online questionnaire to collect user usage assessment data in terms of five sentiment levels—strongly negative, negative, neutral, positive and strongly positive—in response to the usage of the proposed 3D application; (3) evaluate users’ sentiment level in 3D handicraft product application usage; and (4) investigate attracting users’ attention to handicraft products after using the proposed 3D handicraft product application. The results indicate that 78.87% of participants’ sentiment was positive and strongly positive under accept using 3D handicraft product application, and evaluations in terms of assessing attention paid by participants to the handicraft products revealed that positive and strongly positive sentiment was described by 79.61% of participants. The participants’ evaluation results in this study prove that our proposed 3D handicraft product application affected users by attracting their attention towards handicraft products.
FaceAUG: A Cross-Platform Application for Real-Time Face Augmentation in Web Browser

This paper presents FaceAUG, a cross-platform application for real-time face augmentation in a web browser. Human faces are detected and tracked in real-time from the video stream of the embedded or separated webcam of the user device. Then, the application overlays different 2D or 3D augmented reality (AR) filters and effects over the region of the detected face(s) to achieve a mixed virtual and AR effect. A 2D effect can be a photo frame or a 2D face mask using an image from the local repository. A 3D effect is a 3D face model with a colored material, an image texture, or a video texture. The application uses TensorFlow.js to load the pre-trained Face Mesh model for predicting the regions and landmarks of the faces that appear in the video stream. Three.js is used to create the face geometries and render them using the material and texture selected by the user. FaceAUG can be used on any device, as long as an internal or external camera and a state-of-the-art web browser are accessible on the device. The application is implemented using front-end techniques and is therefore functional without any server-side supports at back-end. Experimental results on different platforms verified the effectiveness of the proposed approach.
Panel Shear of Plywood in Structural Sizes - Assessment Improvement Using Digital Image Correlation

This paper introduces a new test configuration for the determination of panel shear properties in structural sizes. This original test configuration has been successfully applied to calculate the shear properties of beech plywood. A numerical model has been developed to evaluate the influence of such a novel setup in comparison to the common standard. The research includes the mechanical characterization of a total of 36 samples using Digital Image Correlation (DIC) to measure the in plane displacements. The use of DIC has been proven to be efficient to measure the shear properties and also acts as a tool to ensure that the solicitation was adequate during the test. Finally, the results highlight the interest to actually perform the proposed test instead of using the alternative density-based equivalencies provided by the standards.
Integrating Mission-Centric Impact Assessment to Operational Resiliency in Cyber-Physical Systems

Developing mission-centric impact assessment techniques to address cyber resiliency in the cyber-physical systems (CPSs) requires integrating system inter-dependencies to the risk and resilience analysis process. Generally, network administrators utilize attack graphs to estimate possible consequences in a networked environment. Attack graphs lack to incorporate the operations-specific dependencies. Localizing the dependencies among operational missions, tasks, and the hosting devices in a large-scale CPS is also challenging. In this work, we offer a graphical modeling technique to integrate the mission-centric impact assessment of cyberattacks by relating the effect to the operational resiliency by utilizing a combination of the logical attack graph and mission impact propagation graph. We propose formal techniques to compute cyberattacks' impact on the operational mission and offer an optimization process to minimize the same, having budgetary restrictions. We also relate the effect to the system functional operability. We illustrate our modeling techniques using a SCADA (supervisory control and data acquisition) case study for the cyber-physical power systems. We believe our proposed method would help evaluate and minimize the impact of cyber attacks on CPS's operational missions and, thus, enhance cyber resiliency.
A Secure and Policy-Controlled Signature Scheme With Strong Expressiveness and Privacy-Preserving Policy

The policy-controlled signature (PCS) scheme uses the access policy to control signature verification permission. However public access policy that may contain private information will leak user privacy. At the same time, the expressiveness of access structures in the PCS schemes is weak. Therefore, we propose a policy-controlled signature scheme with strong expressiveness and privacy-preserving policy (PCS-PP), in which linear secret sharing schemes is to design access structure which has strong expression, the three primes composite order bilinear groups is used to hide the attribute value into the attribute name that may expose the privacy data by data distortion concept. The proposed PCS-PP scheme not only has correctness and privacy-preserving policy, but also supports fine-grained signature verification. In addition, the unforgeability is proved in the random oracle model. Compared to the related schemes, the proposed PCS-PP scheme has superiority in features, computation cost and storage.
Density estimation for circular data observed with errors

Abstract Until now the problem of estimating circular densities when data are observed with errors has been mainly treated by Fourier series methods. We propose kernel‐based estimators exhibiting simple construction and easy implementation. Specifically, we consider three different approaches: the first one is based on the equivalence between kernel estimators using data corrupted with different levels of error. This proposal appears to be totally unexplored, despite its potential for application also in the Euclidean setting. The second approach relies on estimators whose weight functions are circular deconvolution kernels. Due to the periodicity of the involved densities, it requires ad hoc mathematical tools. Finally, the third one is based on the idea of correcting extra bias of kernel estimators which use contaminated data and is essentially an adaptation of the standard theory to the circular case. For all the proposed estimators, we derive asymptotic properties, provide some simulation results, and also discuss some possible generalizations and extensions. Real data case studies are also included.
The Impact of Tourism Development on Economic Growth in Sub-Saharan Africa

This study examines the dynamic impact of tourism development on economic growth in sub-Saharan Africa (SSA) using the Generalized Method of Moments and data covering the period from 2002 to 2018. The increasingly important role of tourism and the limelight the tourism sector has been enjoying of late, on the one hand, and the lack of sufficient coverage of tourism-growth nexus studies in Africa in general and in SSA in particular, motivated this study. Unlike most of the known panel data-based studies on tourism development and economic growth, this study has split the sub-Saharan African countries into low-income and middle-income sub-Saharan African countries. The results of the study show that tourism expenditure negatively affects economic growth while tourism receipts have the opposite effect in SSA. The findings are robust to the low-income sub-sample while only the effect of tourism expenditure is robust in the middle-income sub-sample.
Image-Label Recovery on Fashion Data Using Image Similarity from Triple Siamese Network

Weakly labeled data are inevitable in various research areas in artificial intelligence (AI) where one has a modicum of knowledge about the complete dataset. One of the reasons for weakly labeled data in AI is insufficient accurately labeled data. Strict privacy control or accidental loss may also cause missing-data problems. However, supervised machine learning (ML) requires accurately labeled data in order to successfully solve a problem. Data labeling is difficult and time-consuming as it requires manual work, perfect results, and sometimes human experts to be involved (e.g., medical labeled data). In contrast, unlabeled data are inexpensive and easily available. Due to there not being enough labeled training data, researchers sometimes only obtain one or few data points per category or label. Training a supervised ML model from the small set of labeled data is a challenging task. The objective of this research is to recover missing labels from the dataset using state-of-the-art ML techniques using a semisupervised ML approach. In this work, a novel convolutional neural network-based framework is trained with a few instances of a class to perform metric learning. The dataset is then converted into a graph signal, which is recovered using a recover algorithm (RA) in graph Fourier transform. The proposed approach was evaluated on a Fashion dataset for accuracy and precision and performed significantly better than graph neural networks and other state-of-the-art methods.
Status and Challenges of Frequent Itemsets and Association Rules Mining Methods

Extracting frequent itemsets and association rules mining very important field in data mining applications. It is very useful for support making strategy decisions for any organization or company. As well as, there is very close relationship between frequent itemsets and association rules mining, where extracting frequent itemsets is originally one of the two main procedures for creating association rules. In this paper we introduce first, study of more important parameters for frequent itemsets as well as association rules mining. After that we present classification of more popular types of frequent itemsets and association rules mining.
Improving Efficiency of Self-care Classification Using PCA and Decision Tree Algorithm

Self-care classification for children with physical disability remains an important and challenging issue. It needs the support from occupational therapists to make decision. Data-driven decision making have been widely adopted to make decision based on the data with help of expert systems and machine learning algorithms. In this study, we developed an efficient self-care classification model based principal component analysis (PCA) and decision tree (DT). PCA is used to extract the significant features, while the DT is used to build the classification model. We measure several metrics to evaluate the performance of proposed model as compared to other models and previous study results. Based on 10-fold cross-validation results, the proposed model outperformed other models and previous study results by achieving accuracy of 94.29%. Furthermore, PCA-based feature extraction has shown positive result on improving the model's performance with average accuracy improvement as much as 1.7% as compared to classifiers without PCA-based feature extraction method. Finally, it is projected that the outcomes of the study could assist the occupational therapist on enlightening the efficiency of self- care classification and children therapy.
An Approach for Refactoring in Model Layer on MVC Based Web Application

Software maintenance and evolution are important processes in software development. Developers usually face issues in the process of maintaining and evaluating software. The common problem is software code not well structured and organized. This will have an impact on the difficulty of the process of maintaining and evolving software. Maintenance and evolution process is done in all types of applications. In this research, we focus on web-based applications. Web-based applications are chosen because the development of web-based applications is very evolving. One of the most widely used design patterns in web applications is the Model View Controller (MVC) design pattern. This research focuses on the model layer in web applications. Because this layer automatically changes if there is a change in the data structure or database. Therefore, to make the maintenance and evolution process easier, it is necessary to analyze and reorganize the model layer. In this study, we trying to group the functions on the model layer by looking at the similarity of table reference access in the query. For the analysis process of the reorganization of the function structure, we use DSM (Design Structural Matrix). in this research, we use DSM in a new field that is in the model layer on MVC based application. The preliminary result, the reorganization of the function structure provides better contex recommendations that following the reference table for each function.
Cloud Governance

Corporate governance has received a lot of prominence since the accounting scandals of the early 2000s. Corporate governance not only includes maintaining correct financial records, but also the information technology aspects of the corporation. These include cyber security governance, artificial intelligence governance and cloud governance. Most corporations are migrating their data, software and processes to the cloud and therefore it is critical that the cloud be governed by appropriate policies and procedures. We also need cloud governance frameworks. The roles and responsibilities of the people involved have to be clearly identified. In this paper we discuss some of the essential points of cloud governance.
Collective intelligence for smarter neural program synthesis

We study the problem of automatically generating source code from different forms of user intents. Existing methods treating this problem as a language generating task of the neural network, known as Neural Program Synthesis (NPS). Most of these methods struggle with achieving high generating accuracy, one reason for that is the incompleteness and inaccuracy of user intents for a specific programming task. Inspired by the Swarm Intelligence (SI) and Collective Intelligence (CI) techniques, we proposed an automatic task-specific user intent merging framework combining both the bio-inspired algorithm in SI and CI merged from multiple developers. Empirically, we show that our approach is able to provide more accurate and adequate input for NPS, and our experiment on CI indicates that knowledge merging among isolated software developers in our approach has a significant influence on NPS.
Deep learning based methods for estimating distribution of coalescence rates from genome-wide data

Abstract Demographic and population structure inference is one of the most important problems in genomics. Population parameters such as effective population sizes, population split times and migration rates are of high interest both themselves and for many applications, e.g. for genome-wide association studies. Hidden Markov Model (HMM) based methods, such as PSMC, MSMC, coalHMM etc., proved to be powerful and useful for estimation of these parameters in many population genetics studies. At the same time, machine and deep learning have began to be used in natural science widely. In particular, deep learning based approaches have already substituted hidden Markov models in many areas, such as speech recognition or user input prediction. We develop a deep learning (DL) approach for local coalescent time estimation from one whole diploid genome. Our DL models are trained on simulated datasets. Importantly, demographic and population parameters can be inferred based on the distribution of coalescent times. We expect that our approach will be useful under complex population scenarios, which cannot be studied with existing HMM based methods. Our work is also a crucial step in developing a deep learning framework which would allow to create population genomics methods for different genomic data representations.
ARchemist

Blending theoretical knowledge with applicable skills is one of the most important challenges in modern higher education, especially in STEM areas. Therefore, new approaches emerge to develop educational capabilities. We aim to facilitate classes in experimental chemistry to enhance reflection and attitude towards the subject. We present ARchemist - laboratory task management tool, designed to enhance students' self-awareness and control over performed hands-on exercises. ARchemist takes advantage of mobile augmented reality solutions to provide in-situ guidance for laboratory procedures. We contribute a research prototype designed in cooperation with students and lecturers and present reflections from early evaluation in real-case class scenario. Results show that interactive guidance tools may successfully boost students' confidence and contribute to more efficient and organized classroom workflow.
A Synergized Education 4.0 Ecosystem Sustainably Aligned With Industry 4.0

Education is about creating sustainable innovations for future learning. This tagline emphasizes the role of education policymakers and educators to ensure successful learning for digital natives and neo digital natives as the education landscape is currently challenged by the great disruptors. Knowledge, as capital and technological innovations, has empowered the industry. As such, Industry 4.0 has created a vacuum in the education landscape today at education prepares to handle and embrace Industry 4.0. Therefore, this chapter discusses the role of education sector globally and selectively in some Asian countries such as Malaysia, Thailand, Myanmar, Singapore, etc. in improving its delivery in terms of being an Education 4.0 standard and equipping the global workforce citizens of the future to work in parallel with intelligent systems and robots.