Reviewerly/exHarmony huggingface dataset:
Implementation of Smart Contracts for E-Certificate as Non-Fungible Token using Solana Network

The use of blockchain technology and the use of non-fungible tokens has seen a significant uptick this year. As a consequence of this, the author intends to implement the usage of blockchain technology as a method that is capable of preserving the authenticity value of a certificate and preventing harmful things from being associated with certifications. The author offers solutions by utilizing the blockchain system, specifically by utilizing smart contracts and the Solana network. Specifically, the author offers these solutions. This research intends to provide a solution for the problem of certificate packaging by combining three technologies-specifically, blockchain, Smart-Contracts, and Non-Fungible Tokens-so that certificates can be converted into e-certificates, which then qualify as NFTs. Specifically, blockchain is the technology that will be combined with Smart-Contracts and Non-Fungible Tokens to achieve this goal. In particular, the technology that will be integrated with Smart-Contracts and Non-Fungible Tokens is called blockchain. To be more explicit, the goal is to work toward the realization of these certificates’ potential as a method of monetary exchange. This will be accomplished by working toward the realization of this potential.
Stakeholders’ Transparency Requirements in the software engineering process

Transparency remains a new and emerging concept in software engineering that the stakeholders of a software system, especially developers, must deal with during the software engineering process. One of the transparency concerns is the transparency of software products and processes. This paper proposes a definition of transparency and stakeholders’ transparency requirements relating to software artefacts and the processes that birth them. The requirements were elicited based on a well-defined systematic literature review strategy. The proposed requirements would enable the evaluation of the transparency of SDLC products and processes and their improvement towards achieving effective communication, software maintainability, and stakeholders’ productivity.
Systematic Literature Review: Toxic Comment Classification

Over the last decade, deep learning models have surpassed machine learning models in text classification. However, with the continuity of the digital age, many are exposed to the dangers of the internet. One of the dangers would be cyberbullying. In an attempt to decrease cyberbullying, much toxic text detection and classification research has been done. In this paper, we aim to understand the effectiveness of deep learning models compared to machine learning models along with the most common models used by researchers in the last 5 years. We will also be providing insight on the most common data sets utilized by researchers to detect toxic comments. To achieve this, we have compiled the datasets of research papers and analyze the algorithm used. The findings indicate that Long Term Short Memory is the most routinely mentioned deep learning model with 8 out of26 research papers. LSTM has also repeatedly yielded high accuracy results with above 79% for around 9000 data which could be adjusted depending on the pre-processing method used. There have been attempts to combine more than one deep learning algorithms, however these hybrid models might not result in a better accuracy than an original model. Furthermore, the most frequent sources of datasets came from Kaggle and Wikipedia datasets and a total of 13 researchers that used Wikipedia's talk page edits as their dataset.
Gaze Preference Decision Making Predictor Using RNN Classifier

This paper presents a gaze preference decision making predictor using a recurrent neural network (RNN) classifier from human eye movement when someone needs to choose one of two targets. When the two target objects are displayed side by side on a screen of a VR head mount display (HMD), this predictor predicts his/her final decision in real time by inputting data of the eye movement to the RNN classifier. It is well known that a chosen likelihood increases rapidly approximately 0.5s in advance from when the participants made decision. This result is based on a statistic analysis. In other words, this is not prediction but postdiction. This phenomenon is called the gaze cascade effect. This effect means that people has a positive feedback loop, i.e., the more someone looks at something he/she likes better by choosing one from two candidates according to left or right eye movement, the better he/she likes it. This paper aims at achieving not to postdict but to predict someone’s decision making in real time. In the experiments, we used Blu-ray packages as a target of the gaze preference decision making. First, two packages were chosen randomly from the 78 Blu-ray ones, and were displayed on the VR HMD side by side. 220 sets of eye movement data were measured from 11 participants by an eye-tracking device equipped on the VR HMD. They were recorded with the participants’ final decision of left or right for training the RNN classifier. The 11 participants were instructed to choose one package they like better. Each participant repeated this trial 20 times. With respect to the experimental results, the authors classified all the eye movement data into three categories according to types of movement and data length. Our RNN classifier predicted the participants’ decision making successfully by approximately 91 percent, when the eye data were longer than 3 seconds and include a switching motion between left and right.
Development of cart with constant steerability regardless of loading weight or position : 1st Report: Proposal of an active steering caster and its arrangement

The steerability of shopping cart varies with loading weight or position, affecting comfort and safety. The proposed active steering caster offers constant steerability of a cart. This caster consists of an electromagnetic clutch and a motor. The steering shaft is driven by the motor, and the transmission of that drive can be switched by the clutch. These casters are mounted at two diagonal points on the cart. By controlling the steering shaft of these casters according to the user's pushing force on the cart, constant steerability can be provided without being affected by the loading weight or position. In this paper, we propose the active steering caster with passive function and its arrangement on the cart with mixing the conventional casters.
Learning Cooperative Multi-Agent Policies with Multi-Channel Reward Curriculum Based Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms based on the Centralised Training Decentralised Execution (CTDE) approach have seen a great deal of interest in recent years. Most of the recent works focus on a specific class of environments built on the StartCraft Multi-Agent Challenge (SMAC) environment suite. However, experiments with the PettingZoo multi-particle environments show poor performance in an interesting subset of tasks. In this paper, the nature of these environments and the reward structures are analyzed. It shows that poor performance in these tasks is not due to a lack of representation power in the individual Q function or mixing functions, but rather a result of convergence to a suboptimal equilibrium of dual channel rewards and the issue of agent-reward decoupling that can be a common problem for many MARL environments. We present reward curriculum-based versions of QMIX and VDN as a solution to these problems and compare their results with the standard algorithms. The results show a clear performance gain in terms of a common cumulative reward metric.
Energy Optimized Path Planning and Decision Making for Multiple Robots in Rescue Operations

Path planning strategies exist today for producing trajectories in Unmanned ground vehicles (UGV) that incorporate various navigation concerns, such as 3D navigation, obstacle avoidance, and path re-planning. These path generators mostly depend on the dynamics of the UGV’s position and orientation. However, one significant issue with these methods is that it ignores the relationship between the path planning task and the energy consumption associated with battery performance. This paper presents a path planning algorithm for multiple unmanned robots that take into account battery performance. Subsequently, using the optimal trajectory of each robot within a multi-robot system and monitoring the battery state of each robot, an algorithm for determining the suitable robot for the instantaneous task during rescue operations is proposed. The simulation results show that the proposed energy optimization algorithm can be used to predict the energy consumption of the mobile robot maneuvering processes, in addition, to efficiently supporting to make decisions for mobile robots in rescue operations.
AI for Energy: A Blockchain-based Trading Market

With the emergence of distributed energy generation through residential and commercial solar PV applications, new energy markets are created where consumers and producers are no longer separated, giving rise to the concept of energy prosumers. In a distributed database architecture that utilizes cryptographic hashing and consensus-based verification, blockchain technology offers utilities, consumers and prosumers with a novel, secure, and cost-effective energy-trading solution that automates direct energy transactions. A blockchain-based energy trading simulation environment integrated with a Geographic Information System (GIS) is proposed in this study for Qatar’s Education City Community Housing (ECCH). A comprehensive amount of daily energy activity data is collected and analyzed as part of the approach for recreating spatiotemporal characteristics of trading in a small marketplace. Through this type of simulation, stakeholders can better understand the dynamics of a real trading market, and thus make better decisions for developing a decentralized energy market. Using GIS information and an agent-based design, the results indicate that the characteristics of transactions executed in a local housing market can be easily tailored by adjusting parameters. It is possible to improve model performance by employing high-performance computing to conduct large-scale simulations with many agents to provide more realistic outcomes. The model offers a scalable environment for analyzing an energy blockchain from the perspective of Qatari society, finance, and technology.
Day-Ahead PV Power Forecasting for Control Applications

The percentage of solar energy among all installed energy sources is increasing each year.Photo-voltaic (PV) power forecasting is key to the application of control methods in systems with PV panels.In this paper, we present a method for dayahead PV power forecasting at each time step that is easy to train and can be applied to different power data types (e.g data from hot and cold climates, with various sampling times).Predictions made before and after sunrise are handled separately.Exponentially Weighted Moving Average (EWMA) is applied on the normalized daily power data to estimate the shape of the next-day power curve for the predictions before sunrise.Then, the multiplier value which would expectedly produce the best forecast when multiplied with the estimated shape is predicted using a time-series approach.After sunrise, the observed power data is leveraged to improve the previous forecasts.The proposed method is shown to perform well on multiple data sets with varying characteristics.Also, the method is compared with some benchmarks algorithms, and the results are presented.
Chip-SAGAN: A Self-Attention Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer

The transfer of artistic style is a major and demanding task in computer vision. Compared with western paintings, Chinese ink wash paintings have unique characteristics that prevent existing methods from yielding satisfactory results, such as voids, brush strokes, and ink wash tone and diffusion. The main problems include: 1) the generator does not concentrate on key global features; 2) the generated paintings lose the color of the original content image; 3) the generated paintings do not have bright edges and smooth shading. In this paper, we propose Chip-SAGAN, an innovative approach to transforming real-world pictures into Chinese ink wash paintings, which is trained with unpaired photos and Chinese ink wash paintings. We introduce a self-attention module into the generator to capture global dependencies between features. Furthermore, we introduce an edge-promoting adversarial loss and a color reconstruction loss to ensure that the generated painting matches the content image’s edges and colors. The experimental results show that our method can transform real-world pictures into high-quality Chinese ink wash paintings, and surpass state-of-the-art algorithms.
Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Prediction for Robot Pose Prediction

Deep robot vision models are widely used for recognizing objects from camera images, but shows poor performance when detecting objects at untrained positions. Although such problem can be alleviated by training with large datasets, the dataset collection cost cannot be ignored. Existing visual attention models tackled the problem by employing a data efficient structure which learns to extract task relevant image areas. However, since the models cannot modify attention targets after training, it is difficult to apply to dynamically changing tasks. This paper proposed a novel Key-Query-Value formulated visual attention model. This model is capable of switching attention targets by externally modifying the Query representations, namely top-down attention. The proposed model is experimented on a simulator and a real-world environment. The model was compared to existing end-to-end robot vision models in the simulator experiments, showing higher performance and data efficiency. In the real-world robot experiments, the model showed high precision along with its scalability and extendibility.
Using simulation to evaluate a concept drift detector for condition based maintenance

Industry 4.0. has empowered the integration of real-time sensor readings and process knowledge for early identification of faults. In this paper, simulation was used to evaluate the process of classifying data streams as drifting towards defective behavior. Focusing on the challenges imposed by real-word, unlabeled and non-stationary data, the proposed method was assessed by conducting a case-study in the plastic extrusion domain. The knowledge gained from initial tests on generated synthetic data sets was used to isolate symptoms of a clogged screen pack from the concept drifts detected by the Adaptive Windowing algorithm. Experimental results for 40 days of running time of an extruder show that the proposed method helped in identifying a defective screen pack state upon the appearance of defects in the extrudate.
Comparative Study on Collision Avoidance Methods in Path Planning for Warehouse Robots Using MPC

With the development of the Electronic Commerce (EC) business, the need for automation of warehouse transport is increasing, and transport robots are being introduced in various warehouses. This study addresses the problem of generating a path for multiple robots operating in a warehouse to move from arbitrary initial positions to target positions. To optimize the efficiency of all robots’ movements without collisions in a warehouse, we propose a path generation method using Model Predictive Control (MPC). In actual operation, it is important to generate paths that take into account the uncertainty of each robot’s self-position measurement and consider safety margins so that robots do not collide with each other. In this study, we devise two methods for considering safety margins in MPC: penalty functions and constraints, and evaluate the advantages and disadvantages of these methods.
Power Saving Techniques for Wearable Devices in Medical Applications

Many people in the world are living with chronic diseases, demanding continuous monitoring, diagnosis, and treatment. Continuous physiological monitoring is key to providing preventive healthcare and accurate disease diagnosis, which leads to a growing demand for autonomous wearable technology. Wearable devices acquiring physiological information from the patient demand high-power efficiency to operate in a continuous acquisition mode. While power-saving techniques are applied in wearable devices for many application, very few are considered for biomedical applications. In this work, we explore existing techniques of power reduction for wearable medical devices. Our analysis addresses the power reduction of wearable medical devices and their generalization for different medical signal processing applications. In addition, we propose a taxonomy for power-saving techniques. The common categories of power-saving techniques are task scheduling, clock management, signal compression, and energy awareness. The presented analysis identifies the most appropriate and combined low-power techniques in wearable devices to reduce power consumption.
Obstacle Based Fast Marching Tree for Global Motion Planning

In this paper we introduce a novel method to improve the general performance of fast marching tree for collision-free path planning called obstacle-based fast marching tree. Our proposed OB-FMT* samples the map in discussion initially to obtain the vertexes of all obstacles and connect those vertexes to build a collision-free roadmap, an A*algorithm is adopted to find an optimal path in the built roadmap. Then multiple prolate hyper spheroids with focuses on vertexes of optimal path are established to limit the sampling range. Within the limited sampling point set, we utilize FMT* to find optimal solution with high efficiency. Theoretical analyses are given to prove the probabilistic completeness and computational complexity of our proposed method. Experiments in multiple scenarios show that our algorithm has a better performance comparing with other existing algorithms including RRT*, FMT*, Informed RRT*.
Creating Virtual Knowledge Graphs from Software-Internal Data

Due to their capability to model information about various domains in a semantically rich way, knowledge graphs gain importance in the engineering of automated systems. Still, the integration of engineering artifacts, which are usually created by software, is a challenge, due to missing interfaces of engineering software. In this paper, we propose a method for extracting information directly from running engineering software. The core concept of this method are virtual graphs, which do not physically store data, but instead have capabilities to access data from the software’s internal information model on demand. This information can then be accessed by other engineering software, which avoids typical, tedious workflows consisting of data exports and imports from and to different tools. For this concept, a generic concept and an architecture for a landscape of engineering tools are presented. They are validated by a prototypical implementation, which creates a virtual graph of the internal data of a SysML-tool and makes it accessible by a graph database.
Data Migration Techniques across DBMS by Using Metadata

Abstract: The data migration process is usually needed when there is a change in system, format, or storage type. Currently, there are several techniques and tools for migrating data, for example, CSV files, ODBC, SQL Dump, and so on. Inappropriately not all of these techniques can be implemented for migrating data between two different DBMS. This research describes a data migration technique that can be used to migrate data across DBMS. The data migration technique described makes use of existing metadata in each DBMS. The data migration process described here goes through three stages, namely capture, convert and construct. A prototype was built to test this data migration technique. By using the HR schema, cross-DBMS data migration trials were carried out between Oracle and MySQL. Using this technique, full-schema data migration takes an average of 20.43 seconds from Oracle DBMS to MySQL and 12.96 seconds for the reverse scenario. As for partial data migration, it takes an average of 5.95 seconds from Oracle DBMS to MySQL and 2.19 seconds for the reverse scenario
The hybrid average subtraction and standard deviation based optimizer

In this paper, we propose a new metaheuristic algorithm named the Hybrid Average Subtraction and Standard Deviation based Optimizer (HASSO) to solve the optimization problem. The proposed algorithm uses the amount of information gathered from averages, subtraction, standard deviation, and hybrid population members to explore the search space in order to reach the near-optimal or quasi-optimal solution. The mathematical modeling of the proposed algorithm has been presented in detail. We solve twenty-three well-known important functions containing the unimodal and multimodal functions to obtain their quasi-optimal solution. The results obtained for unimodal and multimodal functions indicate the high exploitation ability of HASSO in converging towards the global optima. The convergence plot of the algorithm shows that the use of five phases in the algorithm makes the algorithm tend towards the optimal or quasi-optimal solution in fewer iterations. In addition to this, the obtained results are compared with nine classical algorithms, and the sensitivity of the algorithm to the population size and number of iterations is also evaluated. Our algorithm is also statistically analyzed for superiority over other algorithms. From this analysis, HASSO’s superiority over the other nine classical algorithms in providing a highly accurate solution with a lesser number of iterations is clearly shown.
An Aerial Crowd-Flow Analyzing System for Drone Under YOLOv5 and StrongSort

Crowd detection has recently been a critical issue in machine vision. In response to the recent epidemics' impact, people tend to avoid crowded places, such as markets and bazaars. Crowd analysis can significantly solve this issue and help epidemic prevention. In this study, we proposed a deep learning-based crowd-tracking system using YOLOv5 with StrongSort and OSNet for detection and analyzing the obtained data, such as crowd tracking, counting and plotting crowd trajectories, and motion maps, and hotspot maps to analyze crowding levels. Regarding data training, we use LabelGo for semi-automatic annotation and training. We also tested the size of the pixels that can detect the smallest object (1.0 x 20.0 pixels) and the effect of the number of people on the screen on the time we spent detecting, tracking, and plotting. The detection, tracking, and plotting time is less than 3 seconds for a crowd of more than 100 people, so the results can be presented quickly.