Reviewerly/exHarmony huggingface dataset:
DroneStick: Flying Joystick as a Novel Type of Interface.

DroneStick is a novel hands-free method for smooth interaction between a human and a robotic system via one of its agents, without training and any additional handheld or wearable device or infrastructure. A flying joystick (DroneStick), being a part of a multi-robot system, is composed of a flying drone and coiled wire with a vibration motor. By pulling on the coiled wire, the operator commands certain motions of the follower robotic system. The DroneStick system does not require the user to carry any equipment before or after performing the required interaction. DroneStick provides useful feedback to the operator in the form of force transferred through the wire, translation/rotation of the flying joystick, and motor vibrations at the fingertips. Feedback allows users to interact with different forms of robotic systems intuitively. A potential application can enhance an automated `last mile' delivery when a recipient needs to guide a delivery drone/robot gently to a spot where a parcel has to be dropped.
The Potential for Forecasting Cryptocurrency Price Movements Using the LSTM Method

The Bitcoin (BTC) is a decentralized cryptocurrency that represents a new and unique medium currency, and it is often considered as the currency of the future. Therefore, it is important to understand how Bitcoin is valued and how different factors influence its extreme volatility. Several studies had discussion whether BTC prices are predictable, but the time periods of these studies were limited by data. Therefore, we believe that a current study is necessary when we consider the volume of the BTC price movements in 2021. This research is assessing the potential for forecasting cryptocurrency price movements using machine learning approach of LSTM method and closing price of BTC in five-year period. The results show that it is possible to forecast the BTC price and even potential rise and fall using LSTM.
Rule-Based Sliding-Mode Fuzzy Logic Control

Fuzzy logicFuzzy logic has proved itself as an advanced model-free approach with tremendous impact on control community. Fuzzy logicFuzzy logic has the ability to handle uncertainties, lack of modeling, and operational disturbances in a control system using expert knowledge. In this chapter, rule-based sliding mode fuzzy logic controllers are designed to control the system despite imprecisions and uncertainties.
SACRED SPACES: JAMES HUTTON'S SCOTTISH LOCALES

Geology developed as a science around the central theme of creating an earth-history. Regarding the natural world as having a history apart from human observation was a new concept. Reconstruction of the earth’s past produced divergence of opinion and theory. Perhaps one of the most controversial theories was that of James Hutton (1726-1797). First published in the 1788 Transactions of the Royal Society of Edinburgh, Hutton concentrated on the acquisition of principles that would enable geologists to distinguish natural operations that maintained the habitability of the earth. The cornerstone principles of the theory, later called Uniformitarianism, were the forces of heat and pressure coupled with the abstract concepts of causation and time. Combined, these principles of geology provided a mechanical explanation for the process of decay and renovation by which strata were formed. The outcrops at Siccar Point, Glen Tilt, and Jedburgh in Scotland confirmed Hutton’s theories. Today, these sites have become holy places for geologist around the globe. Siccar Point is considered by many to be the birth place of modern geology. Certainly, it confirmed Hutton’s theory about causation which moved geology from reliance on catastrophic events to the development of mechanical explanations that were consistent in action and degree across earth’s history. The purpose of this paper is to examine the theories that James Hutton developed and explain why these sights were of importance to the reception of the theories. The research for this paper is based largely on primary sources and observations from James Hutton’s, including but not limited to “Theory of the Earth” Transactions of the Royal Society of Edinburgh (1788), Theory of the Earth Vols. I and II (1795), Observations on Granites (1790), and An Investigation of the Principles of Knowledge Vols. I, II, and III (1794). Because of Covid restrictions, actual observance of the sites is limited to secondary sources.
Research and Implementation of Medical Image Edge Detection Algorithm

Medical imaging has become an important reference for the diagnosis of various diseases, and the role of medical imaging will become more and more important in the future. The interpretation of medical images is of paramount importance. With the continuous development of medical imaging technology, image interpretation has become more and more important. At present, it is directly inferred by doctors that in order to solve problems more effectively and deal with fuzzy data, it is necessary to research and implement an algorithm-based medical image edge detection assistant system. The current mainstream algorithms for edge detection include: Roberts, Sobel, Prewitt, etc. Most of these algorithms construct operators for small neighborhood pixels of the original image. The problem is that the algorithm is relatively sensitive to noise in the image and does not automatically select the appropriate threshold, resulting in a result that is not as expected. This is a disadvantage of current algorithms. The thesis elaborates on the theory and algorithm of image edge detection. At the same time, from the perspective of the original edge detection algorithm, the Canny algorithm is mainly studied, and the optimized MTM algorithm and Ostu algorithm are combined to study and optimize in the filtering denoising research. Finally, the algorithm is implemented in C++ language, which realizes the automatic extraction of the edge function of medical images under noise conditions. The improved algorithm performs an edge replacement effect change complared to the conventional algorithm.
Interactive Attendance System for Modern Education Using Computational Intelligence

The modern age is marked exclusively by advancements in digital technology.Modern education is not untouched by this advancement.However, there are few systems at present, which use computational intelligence in modern education.Currently, digital technology is bounded by commercial uses.In particular, face recognition is mainly used in criminal identification, security, payment, advertising, and healthcare.This project, Interactive Attendance System For Modern Education Using Computational Intelligence, is an attempt to combine modern education with modern computing.It uses the concept of Face Recognition to identify the person and to create an attendance file.The coding is done in the Python language on a Raspberry Pi 3B+ kit with Raspbian.Image pre-processing is used to convert captured images into grayscale and to extract areas of interest, i.e., faces.Machine learning is used to train the system to recognize faces using the LBPH Face Recognizer.This system can be installed in any modern educational institute for smart attendance.
Review of Cryptocurrencies Implementations in the Cloud Environment: Ethereum in the Cloud

All organizations desire to adopt blockchain for their structure due to the degree of security it provides and due to the success it had in the economic industry when it introduces the Bitcoin and after Ethereum. It’s crystal clear that the blockchain technology is a robust technology against many of the attack attempts but still has some gray area for discussion and challenges. In this chapter, the Ethereum blockchain description will be provided in the first place, and then we will be going around the types of blockchain and which one to adapt for each case. The third section will be about the integration of cloud and blockchain where we are going to detail the benefits and vulnerabilities of the integration and present an example for Azure blockchain. In the end, we will be presenting a simulation of the 51% attack.
Persepsi Guru Mengenai Penggunaan Pembelajaran Daring WAG Terhadap Pencapaian Tujuan Pembelajaran IPS Sekolah Dasar di Tengah Pandemi Covid-19 (Penelitian Deskriptif Kualitatif Pada Guru Kelas Tinggi SDN Pasarean Kecamatan Sumedang Selatan)

After the government issued a new policy, there were approximately 68 million students studying from home. Based on study, that in the implementation of online learning in elementary school, most teachers use WAG. In the implementation of this, of course raises a teacher’s perception of obstacle and others. So that this study focuses on the teacher’s perception of the use of WAG in achievening social studies learning objectives. By using descriptive qualitative research methods and data collection tools interviews and observation. For the results of the teacher’s perception research related to the application of online learning. The reason of the teacher’s use WA is because it is familiar and students can already operate it. However, with this policy of course, there are obstacles to be faced, including difficulties for teachers in conveying learning to studies so to measure achievement of studies learning objectives, it is still not optimal.
Enhancing DIY musical instruments with custom circuit boards

This paper explores the use of bespoke printed circuit boards (PCBs) for enhancing Do-It-Yourself (DIY) making of electronic sound devices. With the tools and manufacturing costs now within reach of amateur makers, the production of PCBs for DIY projects can add stability and reproducibility to the growing number of custom instruments used in ubiquitous music projects. The article discusses the impact of maker culture on the custom development of electronic musical instruments, and how incorporating PCB design can extend these developments. Several case studies are described and lessons from these for DIY makers are outlined.
THE ROLE OF PARENTS AND TEACHERS AS LITERACY SPONSOR FOR CHILDREN WITH DYSLEXIA DISSORDER IN TAARE ZAMEEN PAR MOVIE

The role of parents and teachers is less to be considered in developing children’s literacy skills such reading and writing. As literacy sponsors, both parents and teachers hold a critical role for the children, especially those with special needs such dyslexic ones, in acquiring literacy skills and knowledge. This study focuses on the role of parents and teachers as literacy sponsor portrayed in &lt;em&gt;Taare Zameen Par&lt;/em&gt; movie and how the application of multimodal literacy is depicted through the story of a dyslexic boy, Ihsaan. This study uses descriptive qualitative method to analyse and describe the data. The main data of this study are dialogues and screenshots taken from the movie. Furthermore, the data will be analysed using Brandt’s literacy and sponsorship theories to support the argumentation and interpretation in the analysis process. The result shows that the role of parents and teachers has a great impact toward the literacy development of dyslexic children like Ishaan. Likewise, multimodal literacy could give a better outcome for the children with dyslexia disorder.
MAGAN: Multi-attention Generative Adversarial Networks for Text-to-Image Generation

Although generative adversarial networks are commonly used in text-to-image generation tasks and have made great progress, there are still some problems. The convolution operation used in these GANs-based methods works on local regions, but not disjoint regions of the image, leading to structural anomalies in the generated image. Moreover, the semantic consistency of generated images and corresponding text descriptions still needs to be improved. In this paper, we propose a multi-attention generative adversarial networks (MAGAN) for text-to-image generation. We use self-attention mechanism to improve the overall quality of images, so that the target image with a certain structure can also be generated well. We use multi-head attention mechanism to improve the semantic consistency of generated images and text descriptions. We conducted extensive experiments on three datasets: Oxford-102 Flowers dataset, Caltech-UCSD Birds dataset and COCO dataset. Our MAGAN has better results than representative methods such as AttnGAN, MirrorGAN and ControlGAN.
MEDIA LITERACY AS A BASIC KNOWLEDGE FOR TEACHERS IN ELEMENTARY SCHOOL

The high penetration of the Internet and social media among young people is a serious concern for all groups, including teachers at the elementary school level. Media literacy at the elementary school level becomes the foundation or fundamentals in preparing young people, especially elementary students in facing the increasingly changing environment in the current information age. Media literacy for educators, especially teachers at the elementary school level is a must amid the current wave of information and technology. Teachers at Tirtomoyo 1 Elementary School are parents of students who are responsible for education or media literacy. Media literacy for elementary school children is an important way and strategy in counteracting the flow of negative information circulating through social media. This activity is expected to be beneficial for teachers and students of Tirtomoyo 1 Elementary School in understanding how to use social media responsibly.
Representation of software design using templates: impact on software quality and development effort

As a practice, software design seeks to contribute to developing quality software. During this software development stage, the requirements are translated into a representation of the software (also known as design), whose quality can be evaluated and improved. For undergraduate students, the design is difficult to understand and make. In fact, building a good design seems to require a certain level of cognitive development that few students achieve.The aim of this study is to know the effort dedicated to software detailed design and the effect on software quality when graduating students use templates to represent their design. We conducted a controlled experiment where students develop eight projects following a defined process and recording data from its execution in a software tool. We found that the use of design templates did not improve the quality of the code, measured as the defect density in the unit test phase. Also, the use of templates did not reduce the number of code smells in the analyzed code. Regarding the effort, students who use templates dedicated greater development effort to designing than to coding. Meanwhile, students who did not use templates dedicated four times less effort to designing than to coding.
Using Deep Learning for Whole Slide Image Prostate Cancer Diagnosis and Grading in South Florida Veteran Population

Abstract Introduction/Objective Prostate cancer is the most common non-cutaneous malignancy in veterans, with approximately 11,000 new prostate cancer cases diagnosed in the Veteran’s Affairs system each year. Prostate cancer diagnosis and grading can be challenging even for experienced pathologists. Although large VA medical centers have pathologists that specialize in urologic pathology, the vast majority have not. We hypothesized that the AI-augmented diagnosis and grading may provide the solution for such situations. Methods/Case Report Dataset consisted of 10,000 prostate biopsy whole slide images (WSI) from the Kaggle PANDA challenge, and 6,000 WSI from the James A. Haley Veterans’ Hospital. Two Classification models were trained on the combined Kaggle and VA datasets using whole slide labels, and not annotated slides that resemble semi-supervised training. Two-Class Classification to predict Benign: ISUP [0] / Cancerous: ISUP [1,2,3,4,5] Three-Class Classification to predict Benign: ISUP [0] / Low-grade: ISUP [1,2] / High-grade: ISUP [3,4,5] WSI split into “tiles” were used for training the models to reduce whitespace around samples, manage large images, and normalize dimensions/orientations. Results (if a Case Study enter NA) Models trained purely as binary and 3-class classifiers performed very well. Two-Class Model: Accuracy = 0.937 Precision = 0.965 F1 = 0.94 AUC = 0.979 Three-Class Model: Accuracy 0.89 o Benign: Precision=0.897 f1=0.928 o Low-grade: Precision=0.866 f1=0.841 o High-grade: Precision=0.91 f1=0.878 We plan to develop multi-stage prediction models using these 2-Class and 3-Class classifiers as the first stage and a cancer grade predictor in the second stage. Conclusion We successfully showed that AI can augment pathologist’s diagnosis and grading of prostate cancer.
Развитие системы научного книгоиздания в Сибирском отделении Российской академии наук (2001–2010 гг.)

The study of scientific book publishing, due to its enormous social and state significance, is an urgent task of modern research. This article is the first to consider the system of scientific book publishing in the Siberian Branch of the Russian Academy of Sciences (SB RAS) in the period of 2001–2010 thus determining and adding to the history of regional and Russian scientific book publishing. The purpose of the study is to present the development of the system for publishing scientific literature in the SB RAS during this period. The source base of the study is representative: these are numerous documents of the current archives of the Siberian enterprise “Nauka” RAS (Novosibirsk Printing House No. 4) and the Siberian Publishing Company “Nauka” RAS, regulatory acts of the Presidium of SB RAS, Annotated thematic plans for the release of scientific literature for 2002-2010, Publishing houses of SB RAS, materials of the weekly newspaper SB RAS “Science in Siberia” for the same period. The revealed data allow reconstructing the picture of the scientific book publishing system development in SB RAS and its thematic orientation in the first decade of the XXI century. The results obtained show that the prevailing part of published scientific literature – every third edition – during this period was on archeology, history, philology, philosophy, law. The study refined quantitative data on the release of serial publications, revealed the role of the Presidium SB RAS in developing the system of scientific book publishing, expanded the idea of cooperation between SB RAS and SIF “Nauka” RAS. The results obtained provide an opportunity to redefine and assess the evolution of their relationship during the period under review. The continuing decrease in the share of CIF in the total volume of publications of SB RAS can be interpreted as the crisis increase, but the implementation of significant and complex publishing projects speaks for a new paradigm of relations between them: mutually beneficial, effective and promising.
The Influence of System User Ethics in Increasing the Effectiveness of Accounting Information Systems

System User Ethics is the most important part in increasing the effectiveness of accounting information systems.Where ethics as a moral agent in making choices to guide his behavior, and when the system is applied ethics is an important part in increasing the effectiveness of accounting information systems.The purpose of this study was to determine how much influence the ethics of system users have on the effectiveness of accounting information systems.The research method uses descriptive verification, data collection techniques use questionnaires, and the data is tested for validity and reliability.The population is Islamic banking employees, with a sample of 59 Islamic Commercial Bank employees who are directly related to the research.The test tool used is simple linear regression.The results showed that the ethics of system users had a significant effect on the effectiveness of the Islamic banking accounting information system.
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments. Despite this potential, most of the robot learning systems today are deployed as a fixed policy and they are not being adapted after their deployment. Can we efficiently adapt previously learned behaviors to new environments, objects and percepts in the real world? In this paper, we present a method and empirical evidence towards a robot learning framework that facilitates continuous adaption. In particular, we demonstrate how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning, including changes in background, object shape and appearance, lighting conditions, and robot morphology. Further, this adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning, and that pre-training via RL is essential: training from scratch or adapting from supervised ImageNet features are both unsuccessful with such small amounts of data. We also find that these positive results hold in a limited continual learning setting, in which we repeatedly fine-tune a single lineage of policies using data from a succession of new tasks. Our empirical conclusions are consistently supported by experiments on simulated manipulation tasks, and by 52 unique fine-tuning experiments on a real robotic grasping system pre-trained on 580,000 grasps.
Artificial Intelligence Techniques of Fraud Prevention

In the current era of technology, the world at large has been benefitted from the technological progression in almost all the sectors. The advancement of technology, on the one hand, has brought about many boons to society at large, but on the other hand, the fraudsters have found new and faster ways to commit the crime. Hence there is a dire need to introduce new methods to detect and control frauds in the current scenario oftechnological development. The sector of society that is most affected by these Information Technology frauds is the financial sector. These financial frauds can range from small threats like non-payment for e-commerce orders to major coercions like the exposure of customer’s credit or debit card details. When fraud is detected, the organizations end up paying for the heavy losses, and their reputation decays. Hence the fraud detection and prevention methods need to adopt new-age technologies like artificial intelligence (AI) and machine learning (ML) to stop the fraudsters from committing heinous financial crimes. Once fraud is committed, one can only take corrective actions which involve tracking down the culprit and try to recover the defrauded amount. However, the much better option is to prevent the fraud before it occurs, and this exactly is where the role of AI steps in, which is supported by ML, comes into the picture and provides the best solutions available so far. ML can identify legitimate (genuine) and fraudulent behaviors by comparing themwith previously unseen fraud tactics. As far as the role of AI is concerned concerning the use of the models defined by ML, AI uses two models to identify fraudulent practices. One is a supervised model, while the other one is an unsupervised model. Though even after employing ML and AI, certain challenges complicate the fraud detection process, such as changing of fraud patterns by fraudsters, lack of technical knowledge to customers, model interpretations, and time needed to develop and review the already set models. However, ML models and AI increase system efficiency and reduce the instances of fraud occurrence.