Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Agile Supply Chain Framework with IoT

Agile supply chains facilitate sustainability through higher effectiveness and lower wastage. Agile supply chain frameworks in extant literature are designed for static supply chains and high-granular data that are only available at fixed periods. With the increasing interest in the Internet of Things (IoT) and blockchain technology for supply chain visibility and trust and the potential availability of fine-granular real-time data from related sensor networks, there is a need to reconsider existing agile supply chain frameworks. IoT and blockchain technology together reinforce the provision of trusted visibility that facilitates ease of tracking and tracing that is required in agile supply chains. We study existing agile supply chain frameworks and their characteristics – both positive and negative – and use this information to develop an agile supply chain framework that incorporates recent developments in IoT and blockchain technology.
REDESAIN ALAT PEMBUKA FILTER MOBIL DENGAN METODE MORPHOLOGICAL CHART

The development in the world of the automotive industry is currently increasing rapidly, this can be seen from the number of industries in the existing automotive sector that can even be seen from the many types of vehicles such as the latest types of cars from various brands that have emerged. With the increase in automotive industry, this is certainly a good opportunity for automotive companies and workshops. In the face of an increase in the volume of vehicles that will service, companies often experience problems with vehicles that will service, because the number of vehicles that service is quite a lot and queues, this is because the old car filter opener is considered less efficient and productive. The objectives of this study are (1) to determine the dimensions of the redesign tool, (2) to calculate the efficiency of the filter opening tool, (3) to calculate the productivity of the new tool. The method used in this research is the morphological chart method. The results of this research are (1) the dimensions of the redesigned car filter opener are 4 cm long, 5 cm wide, 7 cm high, (2) the efficiency level of the redesigned car filter opener is 90 (3) the productivity of the new filter opener which has been redesigned is 4.28.
Analisis keterampilan menulis teks negosiasi dengan metode quantum learning

Negotiation text is a text that contains a bargain which aims to reach an agreement between two parties who have common interests. This study aims to determine the form of the structure of the negotiating text, namely the form of orientation, request, fulfillment, offer, approval, purchase, closing. This research is a qualitative descriptive study using a case study approach with 23 students as subjects. The results showed that the tenth grade students of MA Asy-Syukuriyah wrote the structure of the negotiating text on the orientation, request, fulfillment, offer, approval, purchase, closing sections categorized as very good. The conclusion of this study is that users of the quantum learning method can make students write negotiating texts well.
Design of the Recovery and Release Structure for the Unmanned Aerial Vehicle Carrier System

In this paper a novel recovery and release structure (RRS) for the unmanned aerial vehicle (UAV) Carrier System based on infrared location and precise control is proposed. The system is used to connect a large UAV as a carrier to the quadrotors placed on it. The mechanical connection part includes XY mobile platform, manipulator and connection parts of tower structure. The signal transmission part is composed of infrared light emitting diodes and photodiodes. The positioning mode is navigation and infrared hybrid guidance. SolidWorks is used for 3D modeling, and carbon plate, aluminum and other materials are used to make the quadrotor. It is believed that, the presented RRS will effectively broaden the application of quadrotor in military field and make up for its shortcomings of small endurance and low load.
Image Composition Using Data Encryption Standard (DES) Output Result Over the Internet

Data protection is a major concern for information security professionals. Due to the rapid development of the means of exchanging information over the Internet and its large quantities, it is necessary to develop methods to protect the Information from intruders (Hackers or Crackers) by using cryptographic algorithms such as (AES, DES, RSA ...etc.) with fast processing and data protection. There are other ways to protect the data by hiding it in a certain medium, such as using an image or video or audio file.This search uses Data Encryption Standard (DES) to encrypt the data then convert this data to a picture of a particular pattern used to randomize the distribution of data in the form of different point’s colors and sites and send it over the internet. The proposed system has been implemented on the local network between two devices running on (Windows10) as well as applied on the Internet in a continuous manner.
A History of Intellectual Property in 50 Objects

What do the Mona Lisa, the light bulb, and a Lego brick have in common? The answer - intellectual property (IP) - may be surprising, because IP laws are all about us, but go mostly unrecognized. They are complicated and arcane, and few people understand why they should care about copyright, patents, and trademarks. In this lustrous collection, Claudy Op den Kamp and Dan Hunter have brought together a group of contributors - drawn from around the globe in fields including law, history, sociology, science and technology, media, and even horticulture - to tell a history of IP in 50 objects. These objects not only demonstrate the significance of the IP system, but also show how IP has developed and how it has influenced history. Each object is at the core of a story that will be appreciated by anyone interested in how great innovations offer a unique window into our past, present, and future.
Unity is Strength: A Formalization of Cross-Domain Maximal Extractable Value

The multi-chain future is upon us. Modular architectures are coming to maturity across the ecosystem to scale bandwidth and throughput of cryptocurrency. One example of such is the Ethereum modular architecture, with its beacon chain, its execution chain, its Layer 2s, and soon its shards. These can all be thought as separate blockchains, heavily inter-connected with one another, and together forming an ecosystem. In this work, we call each of these interconnected blockchains domains, and study the manifestation of Maximal Extractable Value (MEV, a generalization of Miner Extractable Value) across them. In other words, we investigate whether there exists extractable value that depends on the ordering of transactions in two or more domains jointly. We first recall the definitions of Extractable and Maximal Extractable Value, before introducing a definition of Cross-Domain Maximal Extractable Value. We find that Cross-Domain MEV can be used to measure the incentive for transaction sequencers in different domains to collude with one another, and study the scenarios in which there exists such an incentive. We end the work with a list of negative externalities that might arise from cross-domain MEV extraction and lay out several open questions. We note that the formalism in this work is a work in progress, and we hope that it can serve as the basis for formal analysis tools in the style of those presented in Clockwork Finance, as well as for discussion on how to mitigate the upcoming negative externalities of substantial cross-domain MEV.
MAINTENANCE AND OTHER OPERATING EXPENSES (MOOE) AND SCHOOL-BASED MANAGEMENT (SBM) PERFORMANCE OF SECONDARY SCHOOLS IN SAMAR ISLAND

The study investigated the extent of Maintenance and Other Operating Expenses (MOOE) utilization relating to School-Based Management (SBM) performance of public secondary schools in Samar Island for school years 2016-2019. A mixed-method, explanatory research design was employed utilizing 159 school heads. Focus Group Discussion (FGD) was conducted for selected School heads, disbursing officers or bookkeepers and teacher representatives from six divisions in Samar Island. Quantitatively, this study made use of percentages, weighted mean, and Pearson r for the correlative data; while thematic analysis enriched the qualitative investigation. Findings of this study highlight a high extent of Maintenance and Other Operating Expenses utilization to ensure students’ access to complete basic education; priority for school ceremonies like the Moving Up of the Junior High School, Closing Ceremonies, and Recognition Programs; to support the needs of the learners for their learning activities; to fund the rentals and minor repairs of tools and equipment that are important to classroom activities; and to fund the expenses related to graduation rites. The results showed that the least priority in the expenses were for the Internet connection of the school, telephone bills, salaries for the janitors, and the security guards. In addition, it was also found out that the School Based Management performance of the schools have improved in the past three years, from Good to Better. Finally, no substantial connection prevailed between the extent of utilization of Maintenance and Other Operating Expenses and School Based Management performance of secondary schools in Samar Island.
Estimating Dense Optical Flow of Objects for Autonomous Vehicles.

Autonomous vehicles need to be able to perceive both the presence and motion of objects in the surrounding environment to navigate in the real world. In this work, we propose to solve the tasks of identifying objects and estimating the corresponding motion by viewing them as a single unified task known as instance flow. Instance flow provides the pixel-wise instance mask of an object and the dense optical flow within it. To achieve this, we extended the state of the art object detection model to include a dense optical flow estimator. The estimator is used to estimate the optical flow for each region of interest only, instead of the entire image. We tested the approach by carrying out experiments on publicly available datasets for autonomous driving research, VKITTI, KITTI and HD1K. Furthermore, we also introduced a new instance flow quality metric to evaluate the instance flow estimation.
Is the Number of Trainable Parameters All That Actually Matters

Recent work has identified simple empirical scaling laws for language models, linking compute budget, dataset size, model size, and autoregressive modeling loss. The validity of these simple power laws across orders of magnitude in model scale provides compelling evidence that larger models are also more capable models. However, scaling up models under the constraints of hardware and infrastructure is no easy feat, and rapidly becomes a hard and expensive engineering problem. We investigate ways to tentatively cheat scaling laws, and train larger models for cheaper. We emulate an increase in effective parameters, using efficient approximations: either by doping the models with frozen random parameters, or by using fast structured transforms in place of dense linear layers. We find that the scaling relationship between test loss and compute depends only on the actual number of trainable parameters; scaling laws cannot be deceived by spurious parameters.
코로나19에 따른 대학 교양체육 실기수업의 온라인 수업 운영과 강사의 어려움에 대한 질적 사례 연구

Purpose: The purpose of this study is to examine the difficulties of operating online classes, efforts and solutions to overcome them, and the value of meaning in the process due to COVID-19. Method: Using the purposive sampling, six instructors who teach liberal sports classes at universities in the metropolitan area were selected and case study were conducted. Data were collected through interviews and participation observations and so on. The collected data were subject to inductive category analysis and continuous comparative analysis. Results: The results of this study are as follows. 1) Participants in the study had difficulties with online lectures. 2) Participants in the study operated the class in a new way with a sense of responsibility. 3) Participants in the study continued to learn, made efforts and challenged to improve their professionalism. Conclusion: In summary, instructors should make an effort to develop the professionalism to provide high quality online classes. And universities should provide administrative and financial support for instructors. Also the government should provide various teaching material.
Oversight and Regulation of Cryptocurrencies: BitLicense

The question of regulation in the domain of cryptocurrencies has been tackled in various ways, exhibiting therein a desire to strike a balance between fostering innovation and promoting oversight. This chapter examines the case of BitLicense, issued by the New York Department of Financial Services (DFS), with the aim of contextualizing the relative merits of regulatory and oversight initiatives in the domain of cryptocurrencies. This includes an examination of the impact and critiques regarding BitLicense since its promulgation, along with the use of perspectives from public value theory (PVT) to contextualize the value creation efforts of the DFS using BitLicense as a regulatory instrument. The findings of the chapter suggest that contrasting views exist on the value creation of cryptocurrency regulations, and this is reflected both in a PVT approach as well as the evolving praxis of virtual currency regulatory and oversight efforts.
PROBLEMS OF HIGHER EDUCATION IN THE CONTEXT OF GLOBAL DIGITALIZATION AND THE DEVELOPMENT OF DISTANCE EDUCATION TECHNOLOGIES

The article considers the system of digitalization of higher education, where the problems associated with the introduction of digital technologies and tools into the educational process with the transition to online learning are identified. It is shown that digitalization makes significant changes in modern higher education. The transition to online education creates a need to change the management of an educational organization.
Architecting and Visualizing Deep Reinforcement Learning Models.

To meet the growing interest in Deep Reinforcement Learning (DRL), we sought to construct a DRL-driven Atari Pong agent and accompanying visualization tool. Existing approaches do not support the flexibility required to create an interactive exhibit with easily-configurable physics and a human-controlled player. Therefore, we constructed a new Pong game environment, discovered and addressed a number of unique data deficiencies that arise when applying DRL to a new environment, architected and tuned a policy gradient based DRL model, developed a real-time network visualization, and combined these elements into an interactive display to help build intuition and awareness of the mechanics of DRL inference.
Cross-Domain Object Detection via Adaptive Self-Training

We tackle the problem of domain adaptation in object detection, where there is a significant domain shift between a source (a domain with supervision) and a target domain (a domain of interest without supervision). As a widely adopted domain adaptation method, the self-training teacher-student framework (a student model learns from pseudo labels generated from a teacher model) has yielded remarkable accuracy gain on the target domain. However, it still suffers from the large amount of low-quality pseudo labels (e.g., false positives) generated from the teacher due to its bias toward the source domain. To address this issue, we propose a self-training framework called Adaptive Unbiased Teacher (AUT) leveraging adversarial learning and weak-strong data augmentation during mutual learning to address domain shift. Specifically, we employ feature-level adversarial training in the student model, ensuring features extracted from the source and target domains share similar statistics. This enables the student model to capture domain-invariant features. Furthermore, we apply weak-strong augmentation and mutual learning between the teacher model on the target domain and the student model on both domains. This enables the teacher model to gradually benefit from the student model without suffering domain shift. We show that AUT demonstrates superiority over all existing approaches and even Oracle (fully supervised) models by a large margin. For example, we achieve 50.9% (49.3%) mAP on Foggy Cityscape (Clipart1K), which is 9.2% (5.2%) and 8.2% (11.0%) higher than previous state-of-the-art and Oracle, respectively
The Historical Reflection on the Origin of Korean Mission and the First Missionary Event

This study is about the historical reflection on the origin of Korean mission and the first missionary event. The description of mission history is gradually changing from a Western to a non-Western perspective history. However, the Korean church considers 1884 or 1885 when Allen and Appenzeller came to Korea as the origin of mission. The missionaries who came to Korea brought the gospel book of Luke translated from Japan. Therefore, the Korean church should consider the ‘Historiography,’ which considers both the driving force from the outside and the attraction force from the inside. After that, the history of Korean mission must be re-established from the perspective of God’s mission (missio Dei) that includes the first missionary event that occurred before missionaries came to Korea.
Competencies in Digitalization: An Experiment in an International Course

This research aims to explore competencies in the digital global context through an international experiment conducted at the Veracruzana University in Mexico and the Public University of Navarre in Spain. In this paper, we report test scores of a course from both universities (N = 85). The course was grounded on Industry 4.0 and digital transformation. Students were required to work in international teams through virtual communication and coworking tools. The main findings show that digital competencies though necessary, do not seem sufficient. Additionally, the research identifies the most developed competencies (decision-making and cognitive development competencies) and the competencies that need to be strengthened (digital competencies). From a practical perspective, the international experiment facilitates a collaborative learning scenario.
Videojuego gcompris para el desarrollo del pensamiento matemático en 2° de preescolar

Pre-elementary education in the Mexican educational system was integrated into compulsory education in the 2004-2005 school year, leading to important changes at this level. These changes considered that the foundations of behavior, personality and intelligence development are created during the first five years of life. In this regard, early mathematical instruction is intended to promote the development of cognitive abilities as well as the strengthening of logical, analogical, inductive, and deductive thinking (SEP., 2017). This research presents video games as an alternative and flexible form of instruction that allows children to learn at their own pace by navigating different levels of difficulty and solving problems autonomously through play. Therefore, this qualitative action-research study addressed the question: How does the video game GCompris contribute to the development of digital skills that promote mathematical thinking in pre-elementary education? Evidence was obtained of the contribution of the GCompris video game in the development of digital skills, mathematical thinking, and cognitive abilities in preschool students.
A New Design for Software Defect Prediction based on Random Forest Classifier with Recurrent Neural Networks

Software systems have become increasingly complicated and adaptable in recent years. As a result, identifying and resolving software defects are critical. One of the most active study fields in software engineering is software fault prediction. As a result, software defect prediction is critical for improving software quality. It also aids in the reduction of software testing time and cost. As a result, many firms use it to predict software problems in order to save time, improve quality, and schedule resources to meet deadlines. The goal of software defect prediction is to forecast faults based on past data. As a result, it is difficult to anticipate in the real world because it necessitates a greater amount of data variables, metrics, and historical data. As software projects grow in scale, defect prediction techniques will play an increasingly essential role in assisting developers and reducing time to market with more dependable software products. The Random Forest with Recurrent Neural Networks (RF-RNN) techniques for forecasting software defects are introduced in this study. The performance of our suggested method for software defect prediction is measured using several characteristics such as True positive rate, false positive rate, precision, recall, accuracy, and execution time, which are all implemented on the MATLAB platform. Several existing strategies are compared to our proposed method. The graphical depiction of the comparative findings demonstrates that our proposed technique is a highly useful technique for forecasting software fault and provides improved prediction rates in a more efficient manner.
A Novel Online Subcarrier-Wise Extreme Learning Machine Receiver for OFDM Systems

Recently, Extreme Learning Machine (ELM) started gaining interest among researchers in wireless communications as an online training solution for machine learning based receivers. ELM has proven to provide high training speed and global optimization capabilities. However, the number of needed training pilots is still relatively high and increases rapidly with the number of subcarriers, thus rendering its deployment impractical. In this paper, we propose subcarrier-wise ELM receivers that are robust to the increase in the number of used subcarriers; we then extend them to exploit adjacent channel knowledge, hence providing superior performance in frequency selective channels. In addition, we propose a novel training architecture based on interpolated training that saves more than 50% of the computational and spectral resources of conventional ELM receivers. We show the robustness of the proposed technique in different channel scenarios and OFDM settings by means of both practical channel measurements and numerical simulations.
Sistem Pakar Diagnosa Penyakit Kulit pada Manusia dengan Metode Cased Based Reasoning (CBR)

Skin Care Clinic & Laser Therapy located at Jl. Ali Malaka No.15, Maloku, Kec. Makassar, Makassar City, South Sulawesi, Indonesia, in addition to serving skin care consultations also performs a series of diagnoses of symptoms on the skin but limited in serving customers, especially during the current COVID-19 pandemic so that there are obstacles that cause difficulty in consulting diseases but can be overcome by technological advances. At this time the development of technology has been very rapid, not only in the field of information, industry, but in the field of medicine. Implementation that can be applied in the field of medicine, one of which is the system of experts in the diagnosis of skin diseases by using case-based reasoning (CBR) method in the ability of expert knowledge so as to facilitate patients to see the initial diagnosis and provide response (answer) in the form of solutions to problems that occur.