Reviewerly/exHarmony huggingface dataset:
Mono Camera-based Localization of Objects to Guide Real-time Grasp of a Robotic Manipulator

Although robotization has grown a lot in recent years, still there is great necessity for simple, stable and repeated work to be done in quick sequence. Sorting of heterogeneous objects in real-time is one such example where robot-assisted technology can be a boon. The process of sorting can be done on the basis of color, size, weight or any other metric. In this study, image-based real-time color detection for the objects is performed, followed by sorting using a six degrees-of-freedom robotic system. The physical plane of colored objects is calibrated with the work-space of the robotic arm. Post-sorting, gripping of the desired objects is done by a gripper and timing of its actuation, vide open and close, is controlled by an indigenously-programmed controller kit. A Programmable Logic Controller is used for the sequencing that co-ordinates computer system, robotic controller and the controller kit with various embedded tools.
A taxonomy of weight learning methods for statistical relational learning

Abstract Statistical relational learning (SRL) frameworks are effective at defining probabilistic models over complex relational data. They often use weighted first-order logical rules where the weights of the rules govern probabilistic interactions and are usually learned from data. Existing weight learning approaches typically attempt to learn a set of weights that maximizes some function of data likelihood; however, this does not always translate to optimal performance on a desired domain metric, such as accuracy or F1 score. In this paper, we introduce a taxonomy of search-based weight learning approaches for SRL frameworks that directly optimize weights on a chosen domain performance metric. To effectively apply these search-based approaches, we introduce a novel projection, referred to as scaled space (SS), that is an accurate representation of the true weight space. We show that SS removes redundancies in the weight space and captures the semantic distance between the possible weight configurations. In order to improve the efficiency of search, we also introduce an approximation of SS which simplifies the process of sampling weight configurations. We demonstrate these approaches on two state-of-the-art SRL frameworks: Markov logic networks and probabilistic soft logic. We perform empirical evaluation on five real-world datasets and evaluate them each on two different metrics. We also compare them against four other weight learning approaches. Our experimental results show that our proposed search-based approaches outperform likelihood-based approaches and yield up to a 10% improvement across a variety of performance metrics. Further, we perform an extensive evaluation to measure the robustness of our approach to different initializations and hyperparameters. The results indicate that our approach is both accurate and robust.
Bilateral Symmetry Detection in Perspective Based on Vanish Features

Symmetry detection is an important research field in image analysis and computer vision. It plays an important role in object match, recognition and location. However, the change of perspective projection angle increases the difficulty of symmetry detection. Therefore, it is of great significance to detect the symmetry of objects under perspective projection. Current feature point-based methods mostly use multiple sets of matched point pairs to detect the symmetry axis line, which not only introduces massive calculation but also interference between feature point pairs. Hence, we propose a novel feature point match-based symmetry axes lines detection method of planar graphs in perspective. This approach detects matched pairs based on the BEBLID descriptor and AdaLAM mismatch removal, and adopts binocular cameras to get a potential symmetry axis line from a pair of matched points, avoiding the influence of other matched points. Experimental results show that this method can achieve accurate and efficient detection of symmetry axis.
Processing Speed Comparison of the Least Significant Bit (LSB) Steganography Algorithm on FPGA and Matlab

Digital images are images in digital formats or digital media such as hard drives. Digital images consist of bits (0 or 1) called pixels and have a high capacity for storing data and information. Steganography techniques try to hide the existence of confidential data. The Steganography technique perfectly closes secret messages in carrier images with high level security. Information and data will be manipulated so that it can be detected by human eyes. Least Significant Bit (LSB) is the method used in this study. The embedding and extracting processes in the proposed algorithms are performed using Matlab software and FPGA-based hardware simulation using Xilinx ISE. The purpose of this study is to compare the process speed of the LSB steganography algorithm in the two implementations. The input data on the encryption used is data in binary form and Red Green Blue (RGB) images and output data in stego images. Meanwhile, the input data in the decryption is a stego image and the output data is in the form of binary data. The experimental results showed that the steganography algorithm had been successfully performed on FPGA and Matlab. However, the process on FPGA is faster than the process on Matlab. The result of the encryption process is 696000 times faster than Matlab. Meanwhile, the decryption process is 236000 times faster than Matlab.
Implementation of Triple Exponential Smoothing (TES) Method for Forecasting the Number of Dengue Patients Web-based

Dengue is a disease with the number of cases increasing every year. In Indonesia, Dengue is one of the main problems in public health. One of the factors causing the increase in dengue cases is climate change which is uncertain and makes the disease difficult to predict. Forecasting is important to predict future events by using past data references. The purpose of this research is to implement and provide a systematic description of data forecasting for Dengue patients. The Triple Exponential Smoothing (TES) method can be used to predict the number of dengue patients based on existing historical data. This study uses historical data for patients with dengue in the city of Bandung in 2016 &#x2013; 2019. With sub-district variables, male patients and female sufferers are used. The results obtained that the smallest MAPE value of 0.78% was in Buah Batu sub-district and the largest MAPE was 13.70% in Cinambo sub-district for data on male Dengue patients. Data for female Dengue patients shows that the lowest MAPE value is 1.59% in Batununggal sub-district and the largest MAPE is 15.10% in Bandung Wetan sub-district and the average MAPE value in all sub-districts in Bandung is 5.74% for data for female patients and 5.66% for male patients.
TIEboard: Developing Kids Geometric Thinking through Tangible User Interface

This research is based on the concept of computing being embedded within the tangible product that acts as both input and output device eliminating the need of traditional computers for any feedback or guidance.The idea is inspired from traditional geoboard that focuses on the age group from 5 to 8 years old. The main goal is to integrate technology seamlessly into physical manipulative and while using this product kids will be able to make complex shapes that offer kids with memorable learning experience.
The threat of lingua globalization towards vernaculars in South Sulawesi

Indonesia seems to face the crisis of local language, especially in urbanization. The emergence of English as a lingua franca is undeniable to decrease the existence of vernaculars. The research aims to reveal the reason for urbanization giving the biggest impact on the Indonesian’s local language in South Sulawesi and how to maintain the use of local language is facing extinction in this century. This study applied a descriptive qualitative design by interviewing three subjects. They are lecturers and students. The interview result showed that globalization is one of the plagues that had the impact on vernaculars becoming threatened in this present day, influenced people to obey their vernaculars, and also affected the youths’ psychological order and their perception. It is due to the significant use of international or national language and the less effort from the government in maintaining the vernaculars, particularly in South Sulawesi. One suggestion can be proposed that local content needs to be involved in the Education sector e.g., intensifying vernacular use in school and family, and sustainably promoting cultural heritage to attract the society.
A Chaos-Based Encryption Scheme for OFDM-IM Systems

In this paper, we propose a physical layer encryption scheme for OFDM-IM systems based on chaotic maps. The chaotic maps are employed not only on the data symbol modulation, but also on the subcarrier index selection. The initial values of the chaotic maps serve as the secret keys, which are extracted from the wireless channel. What's more, the secret key space is large enough to resist the exhaustive attacks. Mathematical analysis and Monte Carlo simulations have been performed to validate the performance of the proposed encryption scheme. Corresponding analysis and simulation results indicate that the proposed encryption scheme outperforms the existing counterparts in terms of security. Meanwhile, the introduce of chaotic maps has no deteriorating effect on reliability. In particular, the needed modification to conventional OFDM-IM systems is minor in our proposed scheme.
Development of Quality Management System of Construction Engineering Testing Laboratory Based on Ontology

In developing the testing business of construction engineering testing institutions, strict laboratory management is needed first. Currently, because of the research of China’s construction engineering testing quality management system or platform, there is a few refine management which is specific to the whole process of testing laboratories, and many problems often occur, such as chaotic measuring management of instruments and equipment, mismanaged consumables and dangerous chemicals, and so forth. To improve the quality of construction engineering laboratory testing, this paper provides an information management system to improve energy efficiency. The systematic research is based on ontology theory, and the purpose is to turn the theory into practice, complement, and refine the laboratory management system in the field of construction engineering testing, and provide a reference for its development direction. This system would give tangible benefits to the testing institutions, which can get them from the quality management information system.
Methodological and Scientific-Methodological Foundations of Diagnostics of the Psychological and Pedagogical Potential of a Teacher

The most important theoretical and methodological issues related to the identification, assessment, formation and development of the teacher’s psychological and pedagogical potential include adequate methods of his psychodiagnostics. Until now, almost all diagnostic methods used in psychology, including pedagogical, were focused mainly on assessing the current professional level of a specialist’s development, and his potential psychological and pedagogical capabilities, which were not manifested or were not clearly manifested in practical activity, was considered rather within the framework of theory. The article offers for discussion a number of tasks that require methodological and methodological solutions, and are aimed at studying the psychological and pedagogical potential of the teacher.
Text Normalization for Indonesian Text-to-Speech (TTS) using Rule-Based Approach: A Dataset and Preliminary Study

Text-to-Speech (TTS) is a technology that is currently widely used for several purposes both for academic/ non-commercial and industry/commercial purposes. In several cases, some researchers on the TTS field adding a text normalization process for normalizing text that will be used for TTS input to enhance the TTS performance itself. In this paper, we present a rule-based approach to make an Indonesian text normalization dataset that has a raw text and a spoken form of it for enhancing Indonesian Text-to-Speech (TTS) performance. We conduct a set of rule-based for normalizing Indonesian text as an input for the TTS system. Using those rule-based, we generated a dataset and correct it manually so that we have a gold standard for text normalization for Indonesian TTS input. Our approach shows a rule-based can give a good performance for normalizing text for Indonesian TTS with 0.0805 of Word Error Rate (WER).
The use of fuzzy sets to determine the parameters of genetic algorithms that provide approximately the same execution time on the CPU and GPU

Abstract The problem under consideration consists in choosing the number of k individuals, so that the time for processing k individuals by the genetic algorithm (GA) on the CPU architecture is close to the time for processing l individuals on the GPU architecture by the genetic algorithm. The initial information is data arrays containing information about the processing time of a given number of individuals by the genetic algorithm on the available hardware architectures. Fuzzy numbers are determined based on these arrays?~ ? and?~ ? , describing the processing time of a given number of individuals, respectively, on the CPU and GPU architectures. The peculiarities of the subject area do not allow considering the well-known methods of comparison based on the equalities of the membership functions and the nearest clear sets as adequate. Based on the known formula “close to Y (around Y)” the way to compare fuzzy numbers?~ ? and?~ ? was developed in order to determine the degree of closeness of the processing time of k and l individuals, respectively, on the hardware architectures of the CPU and GPU.
Research on Target Tracking Algorithm of Twin Networks Integrating Attention Mechanism

Aiming at the current stage of the twin network target tracking algorithm, the tracking target is occluded, the tracking is affected by illumination, and the target's scale change from far to near or from near to far causes tracking failure. This article will optimize and improve from two directions. The twin neural network first uses an adaptive detailed feature extraction, adds a residual network to the twin network, and embeds a detailed feature retention module in each layer, amplifies the changes in the target feature, and retains the important structure of the original target feature Details: Secondly, the introduction of a spatial attention mechanism allows the main branch to pay more attention to the area to be matched, improves the ability to distinguish features, and makes the tracking effect better. In order to verify the effectiveness of this experiment, this experiment was tested on the data set OTB2015. The experiment proved that the proposed algorithm performs better in accuracy and success rate.
The risk interdependence of cryptocurrencies: Before and during the COVID-19 pandemic

In this paper, we measure the risk interdependence of 12 major cryptocurrencies before and during the COVID-19 pandemic, based on a GARCH-Copula-VaR approach and a dynamic network analysis. We find that cryptocurrencies generally show high levels of volatility, speculation, homogeneity and tail risk contagion. Furthermore, the COVID-19 pandemic has a continuous impact on the cryptocurrency market. When financial institutions are increasingly investing in crypto assets, the hidden risks in the cryptocurrency market remain high. Therefore, this paper calls for attention on the cryptocurrency market from both investors and regulators.
Flight operation anomaly detection based on one-class SVM

Safety is the primary prerequisite of air transport. It is important to detect flight operations anomaly through weather, flight, air traffic and other data in advance. In this paper, we propose a One-class SVM-based anomaly detection method for flight operation. We first construct a flight operation dataset based on the factors of abnormal flight operation, and apply data pre-processing methods to standardize the dataset. Then, a flight operation anomaly detection model based on One-class SVM is proposed to predict the risk. The experimental results show that, compared with LOF and DBSCAN, the proposed model has better performance in Precision, Recall and F1 score, which fully demonstrates the effectiveness and superiority of our model.
Operational Deflection Shapes Magnification and Visualization Using Optical-Flow-Based Image Processing

Much information can be derived from operational deflection shapes of vibrating structures and the magnification of their motion. However, the acquisition of deflection shapes usually requires a manual definition of an object's points of interest, while general motion magnification is computationally inefficient. We propose easy extraction of operational deflection shapes straight from vision data by analyzing and processing optical flow information from the video and then, based on these graphs, morphing source data to magnify the shape of deflection. We introduce several processing routines for automatic masking of the optical flow data and frame-wise information fusion. The method is tested based on data acquired both in numerical simulations and real-life experiments in which cantilever beams were subjected to excitation around their natural frequencies.
Disruption-Limited Planning for Robot Navigation in Dynamic Environments

Path planning in the presence of dynamic obstacles is a fundamental problem in robotics with widespread applications. A typical approach to such problems is that a robot predicts the trajectories of dynamic obstacles, and plans its path while avoiding them. Such a formulation becomes limiting though for scenarios where an agent cannot complete its task efficiently, without disrupting the movement of dynamic obstacles. For example, when merging in heavy traffic or navigating through crowded corridors. In this paper, we propose a paradigm for planning in dynamic environments, called Disruption-Limited Planning (DLP), that allows a robot to disrupt the motions of dynamic obstacles in order to accomplish its task. DLP builds on the premise that while a robot may have to disrupt others’ trajectories to achieve its goals, it should try to limit the disruption. DLP assumes that it can estimate others’ response to its own actions/plans, and plans its own path while ensuring that no other agents’ disrupted trajectory cost gets worse than w-times their initial trajectory costs. While our formulation is motivated by the Stackelberg competitions, we show that DLP can be both more expressive and computationally more efficient compared to a Stackelberg planner. We present DLP paradigm, develop its efficient implementation based on A*, analyze its theoretical properties, and apply it to multiple planning in dynamic environment problems, including x,y,time planning, planning for self-driving, and planning for arm manipulation. We compare DLP with purely altruistic, purely egocentric, and optimal Stackelberg planners, demonstrating the efficacy of DLP over these alternatives.
Fast Particle Swarm optimization for Balanced Clustering

There are balanced priorities in various engineering fields (e.g. medicine, statistics, artificial intelligence, and economics, etc.). Some clustering algorithms cannot maintain the natural balanced structure of data. This paper proposes a soft-balanced clustering framework, which can achieve a balanced clustering for each cluster. The model can be formulated d as a mixed-integer optimization problem. We transform the problem into several subproblems and utilize PSO to search the global solution. Experiments show that the proposed algorithm can achieve satisfactory clustering results than other clustering algorithms.
Inductions of Usernames’ Strengths in Reducing Invasions on Social Networking Sites (SNSs)

The internet allows people to make social contacts and to communicate among different Social Networking Sites (SNSs). If users are ignorant of their exposures, it may reveal their identities and may enhance cyber-attacks. Hence, password secrecy is usually prioritized to protect our personal information. Besides, usages of the same usernames across many SNSs expose users’ identities to other users and intruders. Hackers can use usernames to track usage patterns and manipulate social media accounts or systems. As a result, in terms of security, usernames must be treated the as same as passwords. This empirical study illuminates the analyses of usernames’ strengths by predicting weak usernames with machine learning models to limit poor username selections. We have analyzed the Reddit usernames dataset (83958) to see how frequently people choose weak usernames for their accounts. Our predictive models correctly categorize strong and weak usernames with an average accuracy of 87%.
Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing

The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.
Learning Latent Dynamics from Multi-View Observations for Image-Based Control

In this paper, we tackle the problem of efficient data-driven control of a system, of which the observations are given by raw images. By learning latent representations of image observations and their dynamics model, the image-based control problems can be handled by planning a sequence of controls in the learned latent space. However, these methods have a disadvantage that large amounts of transition data should be collected individually whenever the details of the observation models change (e.g. view-point, color of robots or backgrounds), even if the intrinsic system dynamics is the same. To tackle this issue, we present a method to leverage the data collected from different observation models to enhance the sample efficiency for learning the latent representation of the target domain. To this end, our method utilizes individual encoders for each observation model, and the encoders are trained with a cyclic loss function we propose, to learn the shared latent representation of the observations. The proposed method is demonstrated in a table-top manipulation task using Robosuite simulator [1], and shown to be able to reduce the sample complexity for learning the dynamics model with the data collected from a new view-point.