Reviewerly/exHarmony huggingface dataset:
MEMORY ANALYSIS FOR IPFS IMPLEMENTATION ON ETHEREUM SMART CONTRACT

Abstract. Smart contract is an agreement between two entities established in the program code. All Smart contract transactions are stored on the Blockchain. But storing large data on the Blockchain is expensive, so many developers are currently creating a DApp (Decentralized Application) that integrates IPFS on smart contract Ethereum. Files will be stored on IPFS while the Blockchain only stores hash files from IPFS to access them again. Blockchain & IPFS are distributed peer-to-peer technologies for storing and distributing digital data supported by the confidentiality, integrity and authenticity of the data. The study was conducted to measure memory usage to run the DApp web that integrates IPFS on smart contract Ethereum and find out the effect of the file size uploaded via the web DApp and the number of nodes connected in the network. The memory usage test results will be used as a standard for the memory capacity planning to implement a DApp web system that integrates IPFS on smart contract Ethereum in an organization. Based on the research result, to run a web DApp that integrates IPFS on a smart contract requires 774MB of memory. The result proves that IPFS is suitable for handling large files. The efficiency of DApp's web performance that integrates IPFS on the smart contract Ethereum are obtained by a small file size and a large number of nodes connected in a network. The smaller the file size, the less memory usage. The more nodes that are connected in the network, the less memory usage. Keywords: Decentralized Application, IPFS, Smart Contract Ethereum, Blockchain, Memory Usage.
&amp;#65279;Climate Index Metadata and its Implementation

&amp;lt;p&amp;gt;Climate indices play an important role in the practical use of climate and weather data. Their application spans a wide range of topics, from impact assessment in agriculture and urban planning, over indispensable advice in the energy sector, to important evaluation in the climate science community. Several widely used standard sets of indices exist through long-standing efforts of WMO and WCRP Expert Teams (ETCCDI and ET-SCI), as well as European initiatives (ECA&amp;amp;D) and more recently Copernicus C3S activities. They, however, focus on the data themselves, leaving much of the metadata to the individual user. Moreover, these core sets of indices lack a coherent metadata framework that would allow for the consistent inclusion of new indices that continue to be considered every day.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the meantime, the treatment of metadata in the wider community has received much attention. Within the climate community efforts such as the CF convention and the much-expanded scope and detail of metadata in CMIP6 have improved the clarity and long-term usability of many aspects of climate data a great deal.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present a novel approach to metadata for climate indices. Our format describes the existing climate indices consistent with the established standards, adding metadata along the lines of existing metadata specifications. The formulation of these additions in a coherent framework encompassing most of the existing climate index standards allows for its easy extension and inclusion of new climate indices as they are developed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We also present Climix, a new Python software for the calculation of indices based on this description. It can be seen as an example implementation of the proposed standard and features high-performance calculations based on state-of-the-art infrastructure, such as Iris and Dask. This way, it offers shared memory and distributed parallel and out-of-core computations, enabling the efficient treatment of large data volumes as incurred by the high resolution, long time-series of current and future datasets.&amp;lt;/p&amp;gt;
Effectiveness of Using Video Media in Online Learning Process at SD Panca Budi Medan during Covid-19

The government issued various policies to break the chain of spread of Coronavirus Disease (Covid-19). Restriction of activities is one of the policies of the government must be lived by Indonesians. Activities that are usually done outside the house are now switched to the house through an online system or in the network. The restriction of this activity is felt by all sectors without exception the education sector that must do restrictions in educational activities. There is a shift in learning activities that were previously carried out in schools suddenly turned into online-based distance learning activities. One of the consequences of the online learning process that is done at home is the difficulty of students to learn and understand the subject matter independently, especially for elementary school students who should still need guidance directly in the learning process. This difficulty was also experienced by most students of Panca Budi Medan Elementary School. Therefore, solutions are needed to overcome it, one of which is the use of video learning media in the online learning process. This research was conducted with the aim of knowing the effectiveness of video media use in online learning during the Covid-19 pandemic, moreover this research was carried out at SD Panca Budi which in the implementation of learning is carried out through online learning. Based on previous observations made by researchers, SD Panca Budi has been conducting online learning starting from March 2020, this is done to suppress the spread of covid-19 among education especially in the environment of SD Panca Budi Medan. If you look at it now, it means that online learning at SD Panca Budi has been running for 1 year. Online learning process is not only focus from the material provided by the teacher to the students but there is also a considerable contribution from parents who act as supervisors in learning students at home, parents here act as supervisors as well as supervisors in the learning process. Without the support of parents, online learning will not be maximal even if the teacher has tried to provide the best teaching. So in order for this online learning process to run optimally, all students such as teachers, students and parents must support each other, giving their best efforts.
The Negative Pretraining Effect in Sequential Deep Learning and Three Ways to Fix It

Negative pretraining is a prominent sequential learning effect of neural networks where a pretrained model obtains a worse generalization performance than a model that is trained from scratch when either are trained on a target task. We conceptualize the ingredients of this problem setting and examine the negative pretraining effect experimentally by providing three interventions to remove and fix it. First, acting on the learning process, altering the learning rate after pretraining can yield even better results than training directly on the target task. Second, on the learning task-level, we intervene by increasing the discretization of data distribution changes from start to target task instead of “jumping” to a target task. Finally at the model-level, resetting network biases to larger values likewise removes negative pretraining effects, albeit to a smaller degree. With these intervention experiments, we aim to provide new evidence to help understand the subtle influences that neural network training and pretraining can have on final generalization performance on a target task in the context of negative pretraining.
The Selection of MOOCs While Planning a Career of an it Specialist in Russia

Digitalization, which has been so much talked about, contributes to the development of many industries in Russia and in the world, but at the same time dictates new requirements for digital personnel and their competencies. To keep pace with emerging trends and information technology and plan for future careers, information and communication technology (ICT) professionals should continually update their skill sets and develop new competencies with the help of the MOOC platforms that suggest appropriate courses. However, given the wide variety of platforms and courses, one can get confused about what to choose for the future development, which courses to take and what profession to follow. The authors conduct a research on user requirements, existing MOOC recommendation systems and their functions, and propose a recommendation system that allows users to select an existing MOOC platform based on assignments and skills for ICT career planning in Russia. The article proposes a modern approach that helps IT professionals plan their future development path based on MOOC recommendations corresponding to their development needs.
Peningkatan Kapasitas Masyarakat dalam Kesiapsiagaan Bencana Banjir di Kelurahan Rawang, Kecamatan Padang Selatan, Kota Padang

The problem of flooding is serious in the city of Padang. Padang Selatan sub-district is one of the sub-districts that has experienced one of the worst floods in 2018 in Rawang Urban Village each year. Flooding is a disaster that can be prevented and minimized its impact. However, avoiding and overcoming the flood disaster cannot be done by the government alone. The effect of floods on the community not only in the form of loss of property and buildings, also flooding also dramatically affects the community's economy and overall community development, especially health and education. The purpose of this activity is to provide training in increasing the capacity of the population in flood preparedness by using a focused lecture and discussion method by involving all stakeholders (elements of the community) in Rawang Village. The results obtained from this activity are that the community's awareness is still lacking towards flood prevention, This can be seen from the way of life of the population which most of them always do not reflect the culture of clean and healthy living. The community is accustomed to throwing garbage along the river and drainage without caring about the impact. With this activity, it is expected that in the future, the anticipation of flood disasters by increasing community capacity in preparedness. The community was targeted for capacity building because the population was affected by the natural flood.
Desain Perilaku Home Position Pada Robot Soccer Dan Kursi Bergerak Otonom Menggunakan Algoritma Q-Learning

Robot soccer is a type of wheeled car robot that has complexity in the design of the robot. The complexity in the design of the robot makes research development in the field of robotics increasing. This study discusses home position behavior (back to its original point) using the Q-learning algorithm which is implemented on robot soccer. Behavior system is implemented, the robot can move randomly in tracing the color box area to reach the target point. On the other side, using a chair as an object that will be randomly controlled with the Q-Learning algorithm combined with a webcam camera as a sensor to detect the color of the robot's movement area. Color acts as an initial identification so that the robot easily recognizes and knows the target point and area of ​​its movement.  From the results of tests that have been done, the optimal behavior of robots to reach the target point is purple-yellow-blue with a value of Q = 5.13 and the time response value is time peak (tp) = iteration 12, time rise (tr) = iteration 8, time settling (ts) = iteration 9, time delay = iteration 6, and the value of the steady state error = 0, while in an autonomous moving chair with a value of Q = 5.13, the number of iterations 9, the number of steps is small and the response time response as follows: time rise (tr) = 9 iterations, time peak (tp) = 7 iteration, time settling (ts) = 9 iterations, delay time (td) = 6 iterations and the steady state error value is zero (0) on the 9th iteration.
Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision

Current approaches for learning disentangled representations assume that independent latent variables generate the data through a single data generation process. In contrast, this manuscript considers independent causal mechanisms (ICM), which, unlike disentangled representations, directly model multiple data generation processes (mechanisms) in a coarse granularity. In this work, we aim to learn a model that disentangles each mechanism and approximates the ground-truth mechanisms from observational data. We outline sufficient conditions under which the mechanisms can be learned using a single self-supervised generative model with an unconventional mixture prior, simplifying previous methods. Moreover, we prove the identifiability of our model w.r.t. the mechanisms in the self-supervised scenario. We compare our approach to disentangled representations on various downstream tasks, showing that our approach is more robust to intervention, covariant shift, and noise due to the disentanglement between the data generation processes.
Prototype for Digitization of Fetal Monitoring, Integrable with Electronic Medical Records

Fetal monitoring is performed to check the fetus’ well-being. In the Hospital Italiano de Buenos Aires these studies are currently registered in thermal paper, which for various reasons is discarded once the monitoring is finished. Only the professional's evaluation of the study is registered in the electronic medical record. An application which identifies the patient, communicates with the fetal monitor and creates a graph with the received data was developed, so a digital record of the whole study can be obtained and added to the electronic medical records, thus eliminating the need for thermal paper.
Rethinking Product Liability Rules for Online Marketplaces: A Comparative Perspective

Over the past decade, the rise of online marketplaces such as Amazon.com has profoundly changed the retail landscape. The current pandemic has further accelerated consumers’ shift towards online buying in general and online marketplaces in particular. As a result, traditional distribution chains have been replaced by a market structure which is dominated by digital intermediaries. While this development has increased consumer choice, it has also enabled the influx of unsafe and defect products to US and EU consumer retail markets. It is a matter of controversy whether and how product liability rules, which have been designed for the traditional distribution chain model, can be applied to online intermediaries. Against this background, this Article explores how courts and lawmakers on both sides of the Atlantic are trying to adjust the existing product liability framework to the new reality of the platform economy.The comparative analysis shows that the regulatory strategies in Europe and the US differ considerably. While US law focusses on indirect regulation through product liability, EU law puts its emphasis on direct regulation through public enforcement of product safety rules and market surveillance by public authorities. In this regard, the proposal for a Digital Services Act, published by the European Commission in December 2020, will not bring much of a change. The Article calls for a reform of the EU Product Liability Directive that takes inspiration from recent developments in US product liability law. This could lead not only to a more balanced regulatory framework within the EU, but also to a trans-Atlantic convergence in the field of product liability. Building on the findings of the comparative analysis, the Article outlines several options for law reform and discusses how the regulatory design of product liability rules could affect the market structure and competition in the platform economy.The paper will be presented at the 2021 Consumer Law Scholars Conference in Boston (March 4-5, 2021).
Automatic morphological analysis of L-verbs in Palula

This study is exploring the possibilities of automatic morphological analysis of L-verbs in the Palula language by the help from Finite-state technology and two-level morphology along with supervis ...
Can Students Outperform Teachers in Knowledge Distillation based Model Compression

Knowledge distillation (KD) is an effective technique to compress a large model (teacher) to a compact one (student) by knowledge transfer. The ideal case is that the teacher is compressed to the small student without any performance dropping. However, even for the state-of-the-art (SOTA) distillation approaches, there is still an obvious performance gap between the student and the teacher. The existing literature usually attributes this to model capacity differences between them. However, model capacity differences are unavoidable in model compression. In this work, we systematically study this question. By designing exploratory experiments, we find that model capacity differences are not necessarily the root reason, and the distillation data matters when the student capacity is greater than a threshold. In light of this, we propose to go beyond in-distribution distillation and accordingly develop KD+. KD+ is superior to the original KD as it outperforms KD and the other SOTA approaches substantially and is more compatible with the existing approaches to further improve their performances significantly.
LibGuides: Advertising Research And Planning for Your Business: STEPS TO DEVELOPING THE ADVERTISING PLAN

This guide intends to show the basic thought process of creating an advertising plan and to help you find information in the library's databases.
A Scientometric Study on ‘Brain Tumor’ Research Publication during the Period 2010-2019

The study attempts to find out the research output on Brain Tumor publications in India during the period 2010-2019 using the Scopus database. A total of 2444 research papers were found in Brain Tumor research from India during the mentioned period. The year 2019 is the most productive year with 525 (21.48%) of publications, followed by the year 2018 with 399 (16.32%) publications. The highest number 1507 (61.66%) of records were in the type of Articles, followed by Conference Paper with 495 (20.25%) of records, however, Jalai, R. is the most productive author in Brain Tumor research with 44 (1.8%) publications. Medicine was the most popular subject with 1093 (25.51%), followed by Computer Science 813 (18.98%) contributions. All India Institute of Medical Sciences, New Delhi was the most productive organization with 153 (6.26%) publications and Department of Biotechnology, Government of West Bengal was funded with highest 35 (1.43%) publications and the most preferred keyword was Brain Tumor used in 1349 (4.98%) of publications during the period of study.
AE-SMOTE: A Multi-Modal Minority Oversampling Framework

Real-world binary classification tasks are in many cases unbalanced i.e. the minority class is much smaller than the majority class. This skewness is challenging for machine learning algorithms as they tend to focus on the majority and greatly misclassify the minority. Oversampling the minority using \emph{SMOTE} before training the model is a popular method to address this challenge. Inspired by \emph{SMOTE}, we propose \emph{AE-SMOTE}, which by using an autoencoder, (1) maps the features to a dense continuous latent space, (2) applies oversampling by interpolation in the latent space, and (3) maps the synthetic samples back to the original feature space. While \emph{SMOTE} supports discrete (categorical) features, almost all variants and extensions of \emph{SMOTE} do not. Wrapping any one of these \emph{SMOTE} variants with an autoencoder will enable it to support multi-modal datasets that include discrete features. We have empirically shown the effectiveness of the proposed approach on 35 publicly available datasets.
COLLABORATIVE LEARNING TO ENHANCE ARABIC SPEECH SKILLS AMONG STUDENTS OF BACHELOR’S DEGREE (ISM) IN ARABIC LANGUAGE WITH EDUCATION AT SULTAN IDRIS EDUCATION UNIVERSITY

Collaborative learning refers to learning activities that involve two or more people who learn something together. This learning requires no teacher supervision and is not structured. This is the opposite to the cooperative learning where the role of the teacher as facilitator is to structure and monitor group activities to achieve their objectives. This study aimed to identify the issues faced by the students of bachelor’s degree (ISM) in Arabic Language with Education at Universiti Pendidikan Sultan Idris (UPSI) to master Arabic speaking skills. This study also aimed to analyze the effectiveness of collaborative learning to enhance speaking skills and to analyze the degree of differences in the speaking skills before and after the implementation of collaborative learning. This quantitative study used a questionnaire and diagnostic test involving 29 Arabic language student respondents of the second semester in UPSI. In addition to conducting field studies, this study also made references to books, journals and theses on speaking skills and collaborative learning. The findings showed that collaborative learning methods could help students improve their proficiency in speaking Arabic. The results of this study are expected to have a positive impact and contribute to the teaching pedagogy of Arabic speaking skills for non-native speakers.
React app för Dreamtsoft : Integration av open-source-bibliotek i app-plattform

This project has been carried out to explore the possibilities of working with open-source frameworks and libraries in an existing system. For this project, the existing system is the web applicati ...
Study on Possibility of Estimating Smartphone Inputs from Tap Sounds

Side-channel attacks occur on smartphone keystrokes, where the input can be intercepted by a tapping sound. Ilia et al. reported that keystrokes can be predicted with 61% accuracy from tapping sounds listened to by the built-in microphone of a legitimate user’s device. Li et al. reported that by emitting sonar sounds from an attacker smartphone’s built-in speaker and analyzing the reflected waves from a legitimate user’s finger at the time of tap input, keystrokes can be estimated with 90% accuracy. However, the method proposed by Ilia et al. requires prior penetration of the target smartphone and the attack scenario lacks plausibility; if the attacker’s smartphone can be penetrated, the keylogger can directly acquire the keystrokes of a legitimate user. In addition, the method proposed by Li et al. is a side-channel attack in which the attacker actively interferes with the terminals of legitimate users and can be described as an active attack scenario. Herein, we analyze the extent to which a user’s keystrokes are leaked to the attacker in a passive attack scenario, where the attacker wiretaps the sounds of the legitimate user’s keystrokes using an external microphone. First, we limited the keystrokes to the personal identification number input. Subsequently, melfrequency cepstrum coefficients of tapping sound data were represented as image data. Consequently, we found that the input is discriminated with high accuracy using a convolutional neural network to estimate the key input.