Reviewerly/exHarmony huggingface dataset:
Blockchain Disruption: Digital Assets Are Changing How We Do Business

Digital assets are changing the way businesses think about equity, labor, business models, and business organization. Digital assets, like Bitcoin or Ethereum, provide incredible opportunities to further align shareholders with the objectives of the entity. Each time humanity advances its technology for ledgers, markets explode, and we witness immense wealth creation. Digital assets like Bitcoin and Ethereum are the next great step forward for ledger technology. While there are incredible opportunities to leverage this new technology, there are also incredible risks. There are many public examples of “hacks” of prominent blockchains like Ethereum and Solana. Blockchain technology has captured the imagination of the public. Blockchain, therefore, must develop a robust security system and intelligently distribute and limit liability for institutional and retail investors to reap the rewards of public attention. Part of the risk that comes from digital assets is its newness. Blockchains that run smart contracts have many incredible uses that could eliminate middlemen in many industries. But courts are yet to develop case law surrounding smart contracts. The way that smart contracts self-execute presents a new question which courts must address: how should a court allocate risk between two smart-contracting parties? No matter how the courts decide, the market needs an answer. This article attempts to explore some of the opportunities in digital assets and how these opportunities are fundamentally different from their traditional equivalents.
Prototype of Fire Detection Tool Using Short Message Service (SMS) Notification as A Disaster Mitigation Effort Against Environmental Damage Due to Forest Fires

In relation to the threat of drought, it is necessary to do a drought analysis to evaluate the drought that has occurred. Another disaster caused by water drought is forest fires. The purpose of this study using the prototype of a Forest Fire Detection Tool (FFDT) are to early detection of forest fires and providing Short Message Service (SMS) notification of forest fires to the officer. The method of this study used a prototype of a FFDT with SMS Notification. This tool is controlled using the ATmega328 microcontroller or better known as Arduino Uno R3 and also the GSM 800L v2 module so that this tool will provide notification to forest guards in the form of sirens and SMS sounds when there are indications of smoke or fire that are identical to the event of a forest fire. Monitoring uses fire sensors, namely fire for the detection of flames that are often associated with forest fires and uses sensors as soon as possible, namely MQ2 and MQ9, all of which are equipped in one device, a forest fire detector using the Arduino Mega microcontroller. When there is a good sign that the flame or as soon as possible is read, the tool will send an SMS notification to the forest officer, the process of sending the SMS makes the fire process can detect even though it is still in a small condition or not widened.
Analisa Evaluasi Sistem Pembelajaran Online Di SMK Budhiwarman 1 Jakarta Menggunakan Metode AHP

Covid 19 is an epidemic that is hitting right now, in the midst of the covid 19 pandemic, the government issued a policy to study from home, the online learning system has a large number of problems, this is what makes students unable to follow learning optimally. The problems experienced both in terms of facilities, the internet as well as in terms of assignments and the environment, are the cause of students being unable to follow lessons optimally, SMK Budhiwarman 1 is one of the schools where students feel the impact of online learning, the purpose of this research is to conduct a survey to be able to measure how much influence the problems experienced have on student scores, in addition to testing the current system. AHP is a method that allows to measure the level of problems experienced during online learning and can provide ratings to find out the main problems faced. By combining surveys with AHP, it is found that the main problems that are most often experienced are from the internet and from the facilities used for learning, this proves that AHP can be the main method for finding the main problems of the many problems of the online learning system..
Increasing motivation of students to work independently through gamification

The article examines the issues of increasing students motivation for independent work. The article considers presence of internal motivation among students to carry out independent educational and cognitive activities in the classroom on the discipline "Foreign language". The importance of e-learning, namely introduction of gamification elements into e-learning, courses is assessed. The necessity of using gamification to improve performance and motivation of students during the transition from traditional to distance learning is substantiated.
CONTEMPORARY MEDIA AND THE DEMAND FOR THE CLASSICAL CANON

The article examines the phenomenon of the classical canon in the context of the development of modern media. The modern media environment is increasingly turning to classical literature. The number of projects devoted to canonical texts is growing, while their popularity is increasing by attracting the attention of the audience of non-professional readers – the largest audience group. Classical / canonical literature in the media is presented as one of the many types of content – sites, portals, groups in social networks, mobile applications, etc., and therefore has to compete for the attention of the audience along with other media, that is, to enter into competitive media relations (“struggle” according to P. Bourdieu) with other types of literary content, including entertainment content and content dedicated to contemporary literature – from literary reviews and recommendation lists to marginal phenomena at the intersection of shows and naive poetic creativity. More and more popular is the “repackaging” of the classics: visualization, tests, games. Being in conditions of competition with other media for the attention of visitors, modern media devoted to literature are forced to focus primarily on the demand of the mass audience, in the formation of which the so-called school canon plays an important role – the idea of an unchanged number of works of art for compulsory reading. In this context, the question of the interaction of three agents of the cultural field is considered: institutions of power (school) – audience (non-professional readers) – media (critics). Given that the school canon is becoming more conservative, the author discusses the prospects for its narrowing.
Pemanfaatan Arabic Pad Untuk Penulisan Bahasa Arab Pada DKM Masjid Jami Darul Hikmah

The use of the Arabic Pad application is very useful for activities in religion, especially all activities in the Prosperity Council of the Jami Darul Hikmah Mosque, Depok where many activities require writing in Arabic and are very difficult things in non-Arabic windows. One of the advantages of this Arabic Pad is that it is a freeware application where the developer of this application is Ebta Setiawan on May 25, 2007 version 1.00 is the first version with rapid development in the following years. Without any installation and is a lightweight application, making this application very popular. At this community service, it is hoped that participants will gain useful knowledge for activities at the Jami Darul Hikmah Mosque Prosperity Council, Depok.
Improving Speaking Abilty by Using Turn Taking Strategy (a collaborative classroom action research to the second year students of SMAN 1 Nisam)

Language is the ability of human to communicate with other humans using sign, such as word and movements with the aim of develomping their potential. The research was conducted at SMAN 1 Nisam, North Aceh District. The purpose of the research is to know whether Turn Taking Strategy can improve students’ ability in speaking and to know the class atmospheres when the teacher teaches speaking by using Turn Taking Strategy. The design applied in this research is a qualitative approach and the type is an action research collaborative. This research will do in several phase: 1) preliminary study, 2) planning and action, 3) implementation of the action, 4) observation, and 5) data and reflection. The result showed, From the results of speaking test, there was an improvement of students’ speaking ability. It could be seen from the results of the posttest. There was an improvement in students’ interest in teaching learning process. It was proven they were more active and attention in instructional process. Students were more enthusiastic in English lesson. They did the task and assignment seriously
Identifying Objects in Real-Time at the Lowest Framerate

The practice of finding instances of semantic objects of a certain class, including people, cars, and traffic signs, in digital photos and videos is known as object identification or detection. Due to the development of high-resolution cameras and their widespread usage in everyday life, the detection is one of the most difficult and rapidly expanding study fields in computer science, particularly in computer vision. For automatic object recognition, several researchers have experimented with a variety of techniques, including image processing and computer vision. In this research, we employed a deep learning based framework YOLOv3 using Python, Tensorflow, and OpenCV to identify objects in real time. We do a number of tests using the COCO dataset to verify the effectiveness of the suggested strategy. The results of the experiments show that our suggested solution is resource and cost effective since it uses the fewest frames per second.
An Analysis On Students Perception of English Reading Anxiety at Grade Xi of SMA Negeri 1 Aek Songsongan Academic Year 2021/2022

Most students of English Education of UMN Al Washliyah faced many difficulties to communicate in English. Their lack of confidence was because students’anxiety which affecting students reading skill. The objective of this reseach was to investigate the factors affecting students’ anxiety in reading skill. The Qualitative Research Method was used in this research.The subjects of this research chosen were 6 students as the representative of students in the eleventh grade MIPA 1 of SMA Negeri 1 Aek Songsongan academic year 2021-202. Technique of collecting data used the interview in which the students were interviewed after finishing reading test. Reading in the class was the dominant indicator foundon the students’ anxiety because they did not feel confidence to read in the class. Negative background experience, unfamiliar cultures, and fear of making mistake and apprehension about other evaluation were the indicators which more seldom foundbecause the condition was not formal. Moreover, all the data showed that students who were higher in reading ability got the lower score, students who were moderate in reading got moderate score, and students who were lower in reading got higher score. Shortly, this finding of the research was appropriate with the assumption in this research that was the higher students’ anxiety the lower their reading skill and the lower students’ anxiety the higher their reading skill
Brain Shape Correspondence Analysis Using Functional Maps

Modeling neurodevelopment in brain asphyxia is a crucial task in pediatric medical imaging. One of the significant challenges is that brain structures that are affected by encephalopathy lose their mass due to the injury. Thus, quantifying neurodevelopmental changes is often performed using 2D approaches where the neuroradiologist performs fiducial landmarks to monitor clinical outcomes. We showed how non-rigid correspondence analysis allows relevant quantification of shape relations on brain structures. We use functional maps to automatically compute shape correspondences based on partially observed 3D shapes (i.e., affected brain areas). We perform the brain structure representation as partial functional correspondences between the affected brain area at a post-injury time and model shapes related to the first brain representation (i.e., the first month after birth). Hence, eigenfunctions with the Laplace-Beltrami operator are computed from the training shapes. Then, we apply an optimization process where fine-tuning the functional maps results in meaningful matches between brain structures. The experimental results show that our approach computes reliability correspondences, and establishes candidate matches for robust neurodevelopmental quantification.
Research on data acquisition method of multi-sensor robots based on network coding

In order to improve the efficiency of robot data acquisition, a data acquisition method of multi-sensor robot based on network coding is proposed. By initialising the coefficient matrix and packet cache in sensor nodes, the encoded vectors and encoded packets of the collected data are stored. Then, with the reliable transmission technology of source coding, the collected data are transmitted, and the transmitted data are received according to the one-time Gaussian elimination method. Finally, the data acquisition of the multi-sensor robot is completed through the data packets collected by linear network coding and the global coding vector. The experimental results show that when the error of sensor data collected by this method is kept within 0.39% and the coding coefficient is 4, the collection efficiency is the highest, and the data collected by multi-sensor robots can be transmitted well under different packet loss probabilities, with high data transmission reliability.
The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm

COVID-19 pandemics for as long as two years ago since 2019 gives many insights into various aspects, including scientific development. One of them is the fundamental research of computer science. This research aimed to construct the best model of COVID-19 patients’ mortality and obtain less prediction errors. We performed the combination methods of time series, SARIMA, and Evolutionary algorithm, PARCD, to predict male patients who died because of COVID-19 in the USA, containing 1.008 data. So, this research proposed that SARIMA-PARCD has a powerful combination for addressing the complex problem in a dataset. The prediction error of SARIMA-PARCD was compared with other methods, i.e., SARIMA, LSTM, and the combination of SARIMA-LSTM. The result showed that the SARIMA-PARCD has the smallest MSE value of 0.0049. Therefore, the proposed method is competitive to implement in other cases with similar characteristics. This combination is robust for solving linear and non-linear problems.
Learning Level Prediction System Based on Student Academic Ability By Looking at the Highest Similarity Value

There is a diversity of academic abilities of students in each Indonesian educational institution, each student has different learning abilities according to the level of learning. Educators in providing learning level predictions are done manually, so it takes a long time in predicting student learning levels. In this study, it was able to predict students' academic abilities in an easy and fast way. The method used in predicting learning levels is Case Based Reasoning. This method is able to predict the student's learning level to be (1) Very Bad, (2) Bad, (3) Medium, (4) Good, and (5) Very Good. This level of learning will be used as a benchmark for educators to provide appropriate values to students. The results of this study for the academic performance of the very poor category are 0 students, the bad category is 3 students, the medium category is 79 students, the good category is 16 students and the excellent category is 12 students. The accuracy of academic performance recommendations using confusion matrix is 91.82%.
ACADEMIC STAFF ADAPTATION TO THE CHALLENGES OF THE COVID-19 PANDEMIC: A CASE OF PAVLO TYCHYNA UMAN STATE PEDAGOGICAL UNIVERSITY

Currently, higher education system is characterized by the growing role of personal ability to adapt to the challenges of COVID-19 and finding opportunities for further development considering the global changes in all spheres of life. It puts pressure on the institutions in general and academic staff in particular. This paper aims at studying the challenges academic staff of the English Language and Methods of Teaching Department of Pavlo Tychyna Uman State Pedagogical University has faced during the COVID-19 pandemic and staff’s preparedness and consequent adaptation amid new conditions that are constantly appearing because of the pandemic. The article presents a case study research to answer the following questions: What is the level of academic staff readiness to teach under conditions of the COVID-19 pandemic from a diachronic perspective? In what ways has academic staff adapted to the challenges of the COVID-19 pandemic? The study uses predominantly quantitative and occasionally qualitative analysis to gain insights into our researched issues. The research has shown a steady increase in the number of department lecturers who feel ready for the challenges of online and blended teaching caused by the pandemic and comfortable with their professional competence due to sustainable adaptation to the current situation. The research prospects are clearly seen in the further process of adaptation and professional activities paradigm re-evaluation under the constantly changing global and local conditions as well as researching the issue in a broader context of similar or corresponding cases.
Disentangling Patterns and Transformations from One Sequence of Images with Shape-invariant Lie Group Transformer

An effective way to model the complex real world is to view the world as a composition of basic components of objects and transformations. Although humans through development understand the compositionality of the real world, it is extremely difficult to equip robots with such a learning mechanism. In recent years, there has been significant research on autonomously learning representations of the world using the deep learning; however, most studies have taken a statistical approach, which requires a large number of training data. Contrary to such existing methods, we take a novel algebraic approach for representation learning based on a simpler and more intuitive formulation that the observed world is the combination of multiple independent patterns and transformations that are invariant to the shape of patterns. Since the shape of patterns can be viewed as the invariant features against symmetric transformations such as translation or rotation, we can expect that the patterns can naturally be extracted by expressing transformations with symmetric Lie group transformers and attempting to reconstruct the scene with them. Based on this idea, we propose a model that disentangles the scenes into the minimum number of basic components of patterns and Lie transformations from only one sequence of images, by introducing the learnable shape-invariant Lie group transformers as transformation components. Experiments show that given one sequence of images in which two objects are moving independently, the proposed model can discover the hidden distinct objects and multiple shape-invariant transformations that constitute the scenes.
GeoEmotionsMAPPING App; An Ai approach to DE-coding the qualitative human emotions into quantitative system of information networks in the studies of immersive &amp; Psychogeography lived experience.

We are witnessing cities undergo increasingly complex transformations (spatial-physical-socialgeographical), emerging from new forms-functions that surface, compromise, hamper previous dynamics that directly affect user's experience; moreover, the predictions of urban population increase promises drastic changes-challenges in terms of cities' ability to welcome-manage new inflows of people with; intense circulation of citizens, needs for housing-work-entertainment and other demands that may not be satisfied with existing infrastructures, because this new population will form new social diversities, inequalities, new-patterns and spatial segregation with differentiation hitherto unknown.Hence, the need for user engagement in user centric participatory design approach; cities and people cannot be siloed in their treatments because human habitus is intertwined with its environment.GeoEmotionCapture-App proposes to address multi-layered, multidimensional, highly complex socio-technical-temporal systems using the advancements of environment scanning Apps with the habitus-emotions capture, and user' livefeedback to support a healthier environmental design.Various psychogeography factors can trigger user's reactions and trajectories, making it difficult to understand the phenomenal relationship and interplay "user-object".This research involves the user through recording spatiotemporal behavior and instant feedback in the corps-à-corps experience with the environment to spatialize intangible and complex emotions generated by the users, which are crucial clues t h a t i n f o r m t h e design process toward a therapeutic and systemic place and space making.
Privacy Preserving Multi-Zonal Vehicle Surveillance Enabled by Federated Consortium Blockchain

With roads interconnecting not just cities but also multiple countries, transportation capabilities have improved to the point where vehicles can reach any far-off destinations within a shorter amount of time. For public security purposes, in the instance of crimes such as theft, a road chase vehicle, or a high security clearance vehicle, it has become a challenge to identify and track them at different security operational centers. Typically, traffic zones form some sort of centralized data access approach for monitoring, surveillance, and tracking. This can be vulnerable to manipulation, and privacy can be at stake due to unauthorized or uncontrolled availability to limited stakeholders. The blockchain network provides a decentralized alternative to provide integrity and confidentiality with access control for vehicle surveillance. Further, automated surveillance can be performed using computer vision algorithms to re-identify or track the vehicles in question in a private manner. In this paper, an alternate framework is proposed as opposed to a centralized solution encompassing multiple surveillance zones connected through an access controlled blockchain-based design. For this reason, traditional approaches to vehicle re-identification through image similarity matching are considered rather than heavy-weight unsupervised models due to the requirements of private and real-time inference. Compared to existing frameworks, this framework produces higher accuracy and shorter inference times with the given constraints.
Single atom photon pair source

Sources of entangled photon pairs are a crucial ingredient for many applications in quantum information and communication. Of particular interest are narrow-band sources with bandwidths that are compatible with solid state systems such as atomic media for storage and manipulation of the photons. Here, we experimentally realize a source of energy-time entangled photon pairs where the photons pairs are generated by scattering light from a single two-level atom and separated from the coherently scattered light via a narrow-band filter. We verify the performance of our pair-source by measuring the second order correlation function of the atomic fluorescence and we observe that one can continuously tune the photon statistics of the atomic fluorescence from perfect photon anti-bunching to strong photon bunching expected for a photon pair source. Our experiment demonstrates a novel way to realize a photon pair source for photons with spectral bandwidths and resonance frequencies that are inherently compatible with atomic media.