Reviewerly/exHarmony huggingface dataset:
A Contingent of Blockchain-IoT - An Organized Review

The physical objects start to act intelligent and smart, also by self, automatic, integrating, etc., because of many evolving new technologies called IoT, AI, CC, fog computing, CPS, BPM, SOA, etc. All these aspects can be achieved with security and privacy with the collaboration of BC. As all we are robots? Of course not but machines and electronic gadgets are acting smart in our day-to-day life because there will be no people without the Internet and a phone nowadays. Thus the new aspect of new technology BC, IoT characteristics, functionalities, definitions, types, infrastructures are discussed in detail below. The use cases of BC have been defined. The integration of the BC-IOT combo has been defined.
AES Modes of Operation

Cryptography is the science of secret codes. Previously we used DES algorithms in order to secure but cannot encrypt completely. Thus, we referred AES Algorithms to create a ciphertext in encryption and is given as an input in decryption. In present scenario, everyone sharing their data in online using internet alsoonline transactions like e-banking for money transfers, in shopping malls, restaurants, and many more. While transferring a huge amount or any confidential data there are many chances to hack the data.There are different modes of operations used in AES in accordance with the efficiency to blur the data. The design has been done in VHDL (Very High-Speed Integrated Circuit Hardware Description Language) and simulated and synthesized using Xilinx.
Balancing Bureaucracy and Communal Values in Diffusing the Innovation Process: Strategic Communication Challenges from the Malaysian Perspectives

The transmission of agricultural messages such as innovation in agricultural settings requires good planning from a strategic communication viewpoint. Moreover, there has been a call from various quarters that demand a holistic approach to solve this miscommunication issue. Thus, this study aims to understand the experiences of senior officers who are in charge of managing transmission of agricultural knowledge and training in various agriculturally based organisations. In-depth interviews were conducted with experts representing local and federal research-based and execution agencies. The data were analysed thematically. The interview protocol was developed from a literature review and tested on an expert who was also in the field of agriculture. The findings indicate that the bureaucracy system negates transfer of technology to the recipients, while communal culture presents as stimuli for successful diffusion of innovation. Thus, balancing bureaucracy and communal values offers a positive impact on the transfer of new technology to the recipients. The results offer a new understanding on the complexity of transfer of innovation knowledge and practices in terms of planning and implementing phases faced by the officers. To add, within the context of the study, top-down and bottom-up communication strategies need to be realigned to ensure the sustainability of effective innovation transfer in Malaysia. Future research could address the different scope of communication aspects in these organisations and extend our in-depth interview approach to various officers at national and regional levels. Keywords: Bureaucracy, communal values, strategic communication, experts, innovation.
Quantum information processing of entangled photons with a programmable multi-plane light converter

We demonstrate a reconfigurable and scalable processor of entangled photons based on multi-plane light conversion and perform key tasks of quantum information processing including high-dimensional entanglement certification, arbitrary state transformations, and mode conversion.
Readiness, Preparation, and Effectiveness of Online Teaching and Learning in Vietnam Tertiary Education

This study aims at examining whether university students are ready to transform and adapt to the emergence of online teaching and learning, which serves as a response to the Covid-19 pandemic.The factors that usually influence students' ability to change their learning habits and adapt to these changes readily have usually been proposed from theoretical foundations.In this study, a quantitative method was conducted, with around 188 participants who were requested to complete an online survey with a seven-part questionnaire.Each part of the questionnaire was considered a factor affecting an ideally effective online teaching and learning model.The authors employed the Partial Least Squares (PLS) to analyze the data collected from the survey.The results indicated that Resources Readiness and Strategy Readiness played an important role in managing anxiety and were the main factors that affected online teaching and learning readiness.The pedagogical model was drawn from students' readiness and effectiveness during their online learning in comparison with their efforts.The results also showed that students enrolled in social and humanity courses like English language and linguistics were more resistant to online learning than those in engineering courses.The model with precise details of factors hopes to introduce universities and educational institutions when conducting their emergency online teaching and learning during the COVID-19 pandemic.
(AIANTAS) for IoT Cyberspace —An Upcoming Cloud-based Security Architecture for Police Authorities

In this research work, we investigate the effects of cybercrime and focus on concerns about cybercrime expansion. Since black-hat hackers are constantly one step ahead of white-hat hackers and exploit new sophisticated hack-tools to intrude to devices and networks to commit cybercrime for different reasons, the utilization of the available emerging technologies can provide better privacy and protection level, while they can be used to combat cybercriminal attacks. This shift to new technologies and methods is regarded to be an urgent necessity today. Therefore, we demonstrate a potential novel security mechanism for Police Authorities, named “Artificial Intelligence ANTi-Attack System” or simply “AIANTAS” that aspires to improve the detection rate of cybercriminals and lead to their faster arrest. This model can be integrated into the other National Security defense mechanisms both in Greece and worldwide.
THE ECONOMIC IMPACT OF DIGITALISATION IN INDIAN SOCIAL AND EDUCATION SECTOR

Digital technology is the usage of modern scientific technology and its implementation in most of the sectors, e-administration and the education sector can be the better example for this digitalisation. In the history of digitalisation we can find by sources that, most of the developed economies in the world were using digital technology from the pat decades, but developing countries in the world are using digital implementation a positive factor for sustainable development for the social development of a nation with good legal system in a country and leads to Indian economic growth and social development. Digitalisation benefits in e-education like conferences, MOOC (Massive Open Online Courses), Digital libraries can be taken as a better example for academic progress by the usage of digitalisation, In India digitalisation is playing a vital role from almost 1.5 years, because of lot of demand due to pandemic covid-19 and lockdown situation, India has to take a appositive steps to implementation of digitalisation in all public and private sectors for positive social-economic growth, for digital education, as koutilya mentioned economics and social-political system and law in Arthashastra
Suppression of the Transfer Efficiency for a Noisy Path-Entangled Single Photon in the LH1-RC Unit

The transfer efficiency of the LH1-RC is determined for an incident path- entangled photon with dephasing noise. We have found transfer efficiency suppression due to dephasing noise and the phase difference between the entangled photon states.
An Efficient Technique for Scene Text Extraction from Videos

Text Detection from natural scene images and videos is imperative for applications in real world domain analysis. However the text detection process isperplexingbecause of exigent scenarios that the text exhibit. The information present in the video is either perceptual or it is either in semantic form. Amongst the different content that exists in the video, the text information is a major important content that describes more about the nature of the video. The text present in the video can be categorized into Caption text and Scene text. The caption text is the artificial text that is easy to detect while scene text are natural text which is difficult to identify. In this paper text extraction in natural images by edge based method is implemented. The algorithms are estimated with a set of images of natural scenes that differ alongside the scope of font size, illumination, scale and text direction. Precision, accuracy and recall rates are determined to evaluate the performance. The proposed system worked for all difficult scenarios of varied text and gave better results than the existing methods.
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Double-blind • Conference submission management system: AI Scholar System • Number of submissions received: 139 • Number of submissions sent for review: 132 • Number of submissions accepted: 73 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 52.5% • Average number of reviews per paper: 2 • Total number of reviewers involved: 50 • Any additional info on review process: • Step 1. Each of selected paper will be reviewed by two/three professional experts in the related subject area. • Step 2. Review Reports received from the experts will be judged by one of the editors either Review Reports are logical or not? • Step 3. If not logical, then editor can assign new reviewer or can also judge at his/her own. • Step 4. If logical, then Review Reports will be sent to authors to modify the manuscript accordingly. • Step 5. Authors will be required to revise their papers according to the points raised. • Step 6. Revised version will then be evaluated by the editor for the incorporation of the points raised by the reviewers. • Step 7. Then the editor will send the revised manuscript to the reviewers again for re-evaluation. • Step 8. If the reviewers approve the revise version of the manuscript, then will be accepted for publication. • Contact person for queries: Xuexia Ye publication@keoaeic.org AEIC Academic Exchange Information Centre
Creating a Solar Radiation Measuring System (SRMS) Operated by a Programmable Logic Controller (PLC)

In this study, it is aimed to create a PLC controlled SRMS to be used in rural areas. Firstly a SRMS hardware was prepared consisting of power supply, PLC, analogue module and pyranometer units. Then, a SRMS software was written using CODESYS programming language to measure and record data and to control the hardware by PLC. SRMS software firstly collected the solar radiation in cumulatively by measuring every 30 minutes during the one-day period, and determined the daily total solar radiation. Then it calculated the daily average solar radiation by dividing the daily total solar radiation by the number of measurements. It recorded the daily total and average solar radiation amounts on the SD card. SRMS was tested in Kahramanmaraş Sütçü İmam University (KSU) during the July-November period of the 2019. The daily average solar radiation data recorded at KSU were compared with the data measured in the same period at the Eastern Mediterranean Transition Area Agricultural Research Institute (DAGTEM), located 10 km away. The daily average solar radiation data measured in KSU and DAGTEM varied between 3.63-33.48 MJ m-2 day-1 and 3.00-33.00 MJ m-2 day-1, respectively. Five-month averages of daily solar radiation data measured at both regions were determined 20.20 MJ m-2 day-1 and 19.64 MJ m-2 day-1, respectively. The difference between the mean of KSU and DAGTEM data groups was not found to be statistically significant (p&amp;gt; 0.05). This result revealed that the daily average solar radiation values measured in both regions can be used interchangeably. As an expression of the deviation between data groups measured in both regions, the MAPE and RMSE were determined as 14.57% and 2.68 MJ m-2 day-1. The compatibility level of the data groups was obtained as “good” (MAPE= 10-20%). It was concluded that SRMS could measure the daily average solar radiation with high accuracy and could be used in sensitive measurements.
State of the Art of Machine Learning Techniques: A Case Study on Voting Prediction

Voting methods are the primary methods of evolutionary techniques where machine learning (ML) plays a vital role. Ensemble methods help to create multiple models then combined them into to accurate resultant set. It is one type of classification technique used for voting prediction model to reduce the difference (bagging/(boosting) or improves forecast (stacking). Linear regression techniques are used to construct the optimization model as well as implemented to predict the training data set, accessed from a Kaggle site. The prime substance of this chapter is to produce the basic concept of ensemble learning by using the voting technique. For a better understanding of this concern, we are describing the advanced algorithms by using hands-on training on a real-life problem.
Non-linear autoregressive neural network with exogenous variable in forecasting USD/IDR exchange rate

In the history of economic development, the Rupiah exchange rate is considered one of the key factors in economic stability in Indonesia. Bank Indonesia’ policies play an important role in the Indonesia's economy. The purpose of this study is to explore the relationship between time effect and the presence of exogenous variable that affect the response variable in the non-linear model. The methodological approach taken in this study is Nonlinear Autoregressive Exogenous Neural Network Method (NARX NN). NARX NN is a powerful method for forecasting of time series data and dynamic control problems. NARX NN method extracts generic principles from the past values of the time series to predict its future values so that can be used to forecast the Rupiah exchange rate to the dollar based on the BI rate variable. The obvious finding to emerge from this study are: 1) Feed Forward Neural Network (FFNN) with rprop+ training algorithm, the best model is FFNN (12-3-1). The best FFNN model applied to all BI 7-day (Reverse) Repo Rate data has an Adjusted R2 of 98.8%; 2) Forecasting the USD/IDR exchange rate and its relation to the BI 7-day (Reverse) Repo Rate using the NARX NN series parallel model with the rptop+ training algorithm, obtained the best model NARX NN (13-4-1). The NARX NN model applied to all USD/IDR exchange rate data has an Adjusted R2 of 96.19%; 3) Based on forecasting results for the next 6 periods, the USD/IDR exchange rate tends to experience a downward trend, meaning that the Rupiah exchange rate strengthens, while the BI 7-day (Reverse) Repo Rate increases.
A Curiosity Algorithm for Robots Based on the Free Energy Principle

In this paper, we propose a curiosity algorithm for sample efficient data collection in robotic task learning. Reinforcement learning (RL) is one of the most commonly used methods for robotic task learning. However, when the RL agent explores the environment randomly, it results in low exploration efficiency, especially in learning robotic object manipulation tasks with high-dimensional and continuous state and action space. To improve the sample efficiency, it is necessary to urge the robot to pursue unseen states where exploration is lacking. By making the robot curious, the robot would become interested in the unknown region and explore that region. In order to make the robot curious, we utilize the free energy principle, which has been attracting attention in the field of neuroscience. We propose a method that combines curiosity based on the free energy principle with Soft Actor-Critic, a state-of-the-art reinforcement learning method, and verify the effectiveness of the proposed method in simulation and real-world experiments of robotic object manipulation task learning.
Rearch on quantitative evaluation technology of equipment battlefield environment adaptability

For the performance evaluation, environmental adaptability evaluation, system contribution rate evaluation and other evaluation activities in the equipment test process, the mathematical methods such as uncertainty quantification and Bayesian network are used to quickly build the evaluation index system and quantitative evaluation model related to the evaluation task, design the evaluation prototype, and support various evaluation activities in complex scenarios, Quantifying the impact of uncertain factors as much as possible can speed up the progress of evaluation activities and improve the credibility of evaluation results.
Discussion on Inquiry Learning Activities Based on Augmented Reality Technology

Augmented reality technology uses computers to generate virtual simulation objects that can be manipulated to extend and expand the real world. The integration of augmented reality technology and teaching activities can improve the teaching effect, but the current application of augmented reality technology in teaching activities is still at an immature stage. From the perspective of activity theory and inquiry learning theory, the article makes an in-depth study on the inquiry learning activity and teaching application model based on augmented reality technology. The article summarizes the concept of inquiry learning activities, analyzes the characteristics of inquiry learning based on augmented reality technology, and systematically puts forward the design scheme of inquiry learning activities based on augmented reality technology.
Preparation of Papers for IFAC Conferences &amp; Symposia: Research on model predictive control of lane keeping based on particle swarm optimization

Aiming at the problems of large deviation and long calculation time in the process of lane departure, a lane keeping model predictive control (MPC) method based on particle swarm optimization (PSO) is proposed, which uses MPC to effectively controls the vehicle, and uses particle swarm optimization algorithm to optimize the control time domain NP and prediction time domain Nc of MPC, which can reduce the number of iterations of model predictive control. Firstly, the particle swarm optimization algorithm is used to optimize the model predictive control. When the objective function reaches the minimum value, stop the iteration to reduce the amount of calculation; Then, the optimized lane keeping controller is Co-simulated by CarSim and Simulink for testing the control effect. The simulation results show that compared with the other three control methods proposed in this paper, this control method can control the vehicle driving on the lane more accurately, and the control accuracy can be improved by about 16%. In addition, the model predictive control based on particle swarm optimization improves the control accuracy and takes into account the stability of vehicle driving and the smoothness of control.
Keyword Spotting System using Low-complexity Feature Extraction and Quantized LSTM

Long Short-Term Memory (LSTM) neural networks offer state-of-the-art results to compute sequential data and address applications like keyword spotting. Mel Frequency Cepstral Coefficients (MFCC) are the most common features used to train this neural network model. However, the complexity of MFCC coupled with highly optimized machine learning neural networks usually makes the MFCC feature extraction the most power-consuming block of the system. This paper presents a low complexity feature extraction method using a filter bank composed of 16 channels with a quality factor of 1.3 to compute a spectrogram. It shows that we can achieve an 89.45% accuracy on 12 classes of the Google Speech Command Dataset using an LSTM network of 64 hidden units with weights and activation quantized to 9 bits and inputs quantized to 8 bits.
Anyonic two-photon statistics and hybrid entanglement with a semiconductor chip

We employ SPDC in an AlGaAs chip to engineer the wavefunction and exchange statistics of photon pairs directly at the generation stage. We simulate fermions, anyons, and we generate hybrid frequency-polarization entangled states for applications in quantum information.
Influence of Participatory Decision Making on Job Satisfaction: The Moderating Effect of Staff Experience

The relationship between participatory decision-making (PDM) and job satisfaction (JS) has attracted the attention of researchers not only in the field of business industries but also education sector. However, the issue has not been given noteworthy priority in educational administration research let alone in Islamic tertiary institution context. Thus, the purpose of this study is threefold: 1) to validate the psychometric properties of administrative staff’s PDM and JS constructs, 2) to examine the degree of influence of administrative staff’s PDM on their JS, and 3) test the moderating role of staff’s working experience on the influence of PDM on JS. Using a quantitative survey approach, data were collected from 255 administrative staff working in different faculties, divisions, and centers of International Islamic University Malaysia (IIUM). The study employed co-variance-based SEM by using AMOS version 24 in order to achieve the objectives. First, Confirmatory Factor Analysis (CFA) was employed to test the psychometric properties (such as convergent validity, divergent validity, and reliability) for both PDM and JS. Second, a Full-Fledged SEM was conducted to test the influence of PDM on JS. Finally, to examine the moderating effect of the staff working experience in the relationship between PDM and JST, a multi-group analysis (MGA) was applied. The results confirmed that the multidimensional hypothesized model of PDM and JS were empirically valid and reliable. The results also revealed a statistically significant impact for PDM on JS. Besides, the working experience was found to have a moderating role in the effect of PDM on JS. The study suggests that administrative staff involvement in decision making process is highly required as it increases their job satisfaction.
Calibration and 3D Reconstruction of TOF Camera Based on Marker

The pointcloud registration is the first step of 3D reconstruction. However, the classical iterative nearest point (ICP) algorithm and its variants have some problems, such as unable to deal with lowoverlapping pointcloud data, relying too much on normal vector, curvature and other features, and slow speed. To solve these problems, this paper proposes a multi-camera calibration algorithm based on marker. Firstly, Azure-Kinect devices from different perspectives detect the corners of the marker and identify the ID. After converting the 2D corners into 3D corresponding points, combined with the manually entered world coordinates, the rigid transformation parameters of the corresponding camera are calculated through Procrustes analysis to complete the global registration. Secondly, the local registration is performed by using the Colored-ICP algorithm to optimize the gap between adjacent view pointclouds. In order to intuitively perceive the quality of reconstructed model, Hololens2 is deployed for holographic display. The experimental results have shown that the proposed algorithm framework improves the accuracy and speed of 3D reconstruction, and simplifies the operation process of traditional offline calibration algorithm to a certain extent.
Quantum cryptography with a full-fledged photonic integrated chip system

Integrated photonics can enable a wide deployment of quantum cryptography but this integration is technologically challenging. We addressed the challenges and built a complete, real-time and deployable quantum key distribution system based on integrated photonics.