Reviewerly/exHarmony huggingface dataset:
Deployment on Machine Learning Algorithms on Cloud

Cloud-based systems can provide access to a large pool of data and computational resources to consumers and developers. Everything can be done on a cloud from running applications to store data off-site. This project harnessed the collaborative nature and mobility of cloud to provide something to everyone. The idea of this project is to use cloud services to provide machine learning services to consumers where one can access trained and tested models or train and test a model of their own. Our aim is to provide these facilities to every data scientist and developer as a fully organized and supervised service. This platform enables users to access and use other deployed machine learning and data science models/tools to meet their needs and expertise. The main focus is on the improvement of delivering machine learning-based services over cloud technology.
Semi-Quantum Inspired Lightweight Mediated Quantum Key Distribution with Limited Resource and Untrusted TP

Semi-quantum inspired lightweight protocol is an important research issue in realization of quantum protocols. However, the previous semi-quantum inspired lightweight mediated quantum key distribution (SQIL-MQKD) protocols need to use the Bell states or measure the Bell states. The generation and measurement of Bell states are more difficult and expensive than those of single photons. To solve this problem, a semi-quantum inspired lightweight mediated quantum key distribution with limited resource protocol is proposed. In this protocol, an untrusted third party (TP) only needs to perform the quantum operations related to single photons and the participants only have to perform two quantum operations: (1) reflecting qubits without disturbance (2) performing unitary operations on single photons. In addition, this protocol is showed to be robust under the collective attack.
A MODEL TO DETECT SOCIAL NETWORK MENTAL DISORDERSUSING AI TECHNIQUES.

Nowadays the users of social network are increasing drastically worldwide. This platform become very useful for sharinginformation, discussing on various issues, Even majority of their active time they are spending on social medias liketweeter, face Book etc. Due to this physical human relations are damaging, and users are addicted to internet and frequentchecking of tweeter, Facebook etc, Net compulsion. Recent surveys telling that there is a relation between mental healthand social network behaviour. Still it is unclear how this mental illness and social networks are related. In this paper weare going propose a model to to find out mental disorders using social network data analysis, in this work we havecollected the data from twitter and manually labelled that data into two classes one is depressive and other is normal thenthen data pre-processing was performed then it is divided into training and testing sets, training data is used to build themodel by making navie bayes classifier to learn from the data. Once model is build it was tested with the testing set andobtained results with high Accuracy around 92.3. So usually doctor need to find metal disorders they will fire somequestions to the patient based on that doctor detects mental illness but in this model we can able to detect mentaldisorders without consulting patient based on their social network behaviour analysis.
Indonesian-English Code-Switching of Sacha Stevenson as a Canadian Bilingual Speaker on &lt;i&gt;YouTube&lt;/i&gt;

Code-switching or language alternation is one of the linguistic strategies that is widely used in bilingual community, including Indonesia. This study attempts to find out the types and reasons of code-switching on YouTube as employed by a Canadian bilingual speaker, Sacha Stevenson. The data used for this study were transcripts of five videos about Indonesian culture taken from Sacha’s YouTube channel. Based on the analysis, there are a total of 313 occurrences of code-switching from Indonesian to English. Poplack’s theory (1980) was applied for the classification of code-switching. The findings showed that the most frequent type is inter-sentential code-switching (42%), followed by intra-sentential code-switching (34%), and the least is tag-switching (24%). This study also explored the reasons for code-switching by applying the theory proposed by Grosjean (1984). It was found that all code-switching occurrences fit into the 11 categorizations of code-switching reasons. This shows a variety of different factors that influence the use of code-switching. The most frequent reason which triggered code-switching is to fill a linguistic need for lexical item, set phrase, discourse marker, or sentence filler (31%). In addition to the 11 reasons proposed by Grojean (1984), another reason for code-switching was found, i.e., to gain popularity.
A Study on Updating Development Phase Components Reliability Using a Bayesian Technique

Purpose: This study proposes the Bayesian model that can update the development phase reliability using the small field failure data.<BR>Methods: A Gaussian distribution with the development phase reliability as one parameter, μ, was used as the prior distribution of reliability. Moreover, the posterior distribution was estimated by Markov Chain Monte Carlo. The standard deviation was assumed to be proportional to the development phase reliability. That optimal proportional value, m, was derived from the simulation.<BR>Results: The effectiveness was verified by applying the proposed method to real cases. The proposed method involves only moderate fluctuations depending on the amount of data considering that only a fraction of data was used. Comparatively, the results of the existing methods showed large fluctuations.<BR>Conclusion: We expand the reuse area of the development phase reliability, where a high cost and effort was invested, Moreover, we propse a procedure to calculate meaningful reliability with a small amount of data. Moreover, an intuitive and familiar Gaussian distribution can double its effectiveness.
Knowledge-Guided Object Discovery with Acquired Deep Impressions

We present a framework called Acquired Deep Impressions (ADI) which continuously learns knowledge of objects as "impressions" for compositional scene understanding. In this framework, the model first acquires knowledge from scene images containing a single object in a supervised manner, and then continues to learn from novel multi-object scene images which may contain objects that have not been seen before without any further supervision, under the guidance of the learned knowledge as humans do. By memorizing impressions of objects into parameters of neural networks and applying the generative replay strategy, the learned knowledge can be reused to generate images with pseudo-annotations and in turn assist the learning of novel scenes. The proposed ADI framework focuses on the acquisition and utilization of knowledge, and is complementary to existing deep generative models proposed for compositional scene representation. We adapt a base model to make it fall within the ADI framework and conduct experiments on two types of datasets. Empirical results suggest that the proposed framework is able to effectively utilize the acquired impressions and improve the scene decomposition performance.
The Macro-Economics of Crypto-Currencies: The Role of Private Moneys in a Post-Corona Monetary Policy

Cryptocurrencies provide an important dimension of innovation to the evolution of the exchange medium we call money. There are now close to 2,000 such currencies, and their potential and volume is growing. The impact of such currencies for money laundering, law enforcement, and banking supervision have been extensively discussed on the transaction level. But this is the “micro” level of analysis. What has been rare is a “macro” level discussion of the impact on the monetary system of a country. Central banks, which are institutions tasked with providing monetary stability, will see their problems rise while the power of their traditional tools to control money supply and interest rates – such as reserve requirements and the discount rates – is declining. But the new digital technologies – such as distributed ledgers – and new approaches provide regulatory bodies also with new and potentially powerful tools. The task for central banks and policy makers is to create new approaches to use, regulate, and incent them in shaping the macro-economic path of their economy. The paper will propose several of these approaches. This is of particular importance in an economic recovery post coronavirus. In the process, central banks will also, predictably, issue their own digital currencies, and a tiny number of those will become global super-currencies. This will create a new type of issues.
SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms

We consider the task of photo-realistic unconditional image generation (generate high quality, diverse samples that carry the same visual content as the image) on mobile platforms using Generative Adversarial Networks (GANs). In this paper, we propose a novel approach to trade-off image generation accuracy of a GAN for the energy consumed (compute) at run-time called Scale-Energy Tradeoff GAN (SETGAN). GANs usually take a long time to train and consume a huge memory hence making it difficult to run on edge devices. The key idea behind SETGAN for an image generation task is for a given input image, we train a GAN on a remote server and use the trained model on edge devices. We use SinGAN, a single image unconditional generative model, that contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. During the training process, we determine the optimal number of scales for a given input image and the energy constraint from the target edge device. Results show that with SETGAN's unique client-server-based architecture, we were able to achieve a 56% gain in energy for a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale training, we obtain around 4x gain in training time on the server.
Designing Student and Lecturer Attendance System Application Using Progressive Web Apps (PWA)

Sriwijaya State Polytechnic currently has a web-based academic information system, namely SISAK.One of the features available in this system is the activity of recording student and lecturer attendance with a lecturer attendance validation system in class.Validation is carried out during lecture activities and the one who carries out the validation is the class leader using the campus intranet network.In its implementation there are still problems such as when many network users will slow down the appearance of the validation feature on SISAK which results in the validation being unable to be carried out at that time.So that students sometimes forget to validate because they are doing the next lecture.In addition, service access rights to validate involve many students, the head of department and Assistant Director I, which can slow down the validation system.This can be a problem in the speed and accuracy of data information.One solution that is considered important to solve this problem is to build an attendance information service for the attendance of students and lecturers through a web-based mobile application using progressive web applicatians (PWA) technology.This system involves students and lecturers in the implementation of validation and information on lecture attendance activities can be seen by class leaders, lecturers and department administrators.
Rancang Bangun Aplikasi Logbook dan Joblist pada Perusahaan Asuransi untuk Monitoring Pekerjaan (Studi Kasus : PT Asuransi Central Asia Cabang Duta Merlin)

ABSTRACTLogbook and Joblist is one of the most important parts of a company as a data archive at PT. Asuransi Central Asia Duta Merlin Branch. Through this logbook, every employee will receive a joblist that must be completed when the company receives a claim. Processes that are still manual lead to inefficient data processing, for example the process of making Claim Commite Approval and procurement of surveys late. Also, poorly monitored employee performance causes data collection time is not timely. The method used to support the development of the system is the waterfall method. With the development of this system design can facilitate the process of making the system because the stages are carried out in stages. Testing this system uses a blackbox and ISO 9126 to test software in terms of software functionality. From the results of testing ISO 9126 on the Logbook and Joblist applications, quality characteristics based on Functionality, Reability, Usability, and Efficiency will be seen.
Z Distance Function for KNN Classification.

This paper proposes a new distance metric function, called Z distance, for KNN classification. The Z distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the Z distance of two data points includes their class center distance and real distance. And its shape looks like Z. In this way, the affinity of two data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the Z distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification.
The Effect of Set Partitioning in Hierarchical Trees with Wavelet Decomposition Levels Algorithm for Image Compression

The transfer and storage of images is done through compression technology using various transformations to find suitable transformations for image analysis and compression, wavelet based images are analyzed with image compression technology, which is necessary for channel image transmission. The purpose of this article is to determine the appropriate wavelengths for compressing images by recording the parameters that are created by compressing and using an appropriate compression method. The compression method Set Partitioning in Hierarchical Trees (SPIHT) is used to obtain a better compression of the image with a high compression ratio using different wavelets and to compare the results that the techniques were implemented in the Matlab program through the results such as basic criteria for the compressed image quality scale.
Leak in OpenSSL

The term “hacker” has been spread around the world and has always been considered as a threat when we use the internet. We often hear hackers deface websites’ contents and break into system to steal private and confidential information, such as account’s username and password, credit card numbers and others. This is definitely an unethical behavior of irresponsible people who mostly aims to gain profit. However the term hacker, on the contrary actually originates from an expert computer technicians who tries to access the system to debug and fix security problems of the system.&#x0D; Nowadays there are dozens of websites out there and some of those websites have low level of security. Hacker can easily break through their system and steal their private confidential data but just because these websites have low level security, that doesn’t mean that it is ethical to break into someone’s system and read their data. It goes the same when someone entering other people’s house because the door was left open by the owner.&#x0D; As web development grows rapidly, security has become an essential part to make the website more secure and reliable. This is when a group of people decided to make a collaborative project on the implementation of SSL (Secure Socket Layer) and TLS (Transport Layer Security) that is available to be used by everyone. This project is called as OpenSSl and has been used by most of the websites in the internet today. What if this OpenSSL, which has been trusted and implemented by 2/3rd of the websites all around the world can be breached? Definitely it will attract dozens of hackers all around the world to do something unimaginably dangerous.
Surrealistic Aspects of Image Generation Techniques Using a Generative Adversarial Network - Based on the Creative Process of Viewporter Generating a City Landscape

Photography and film have been actively used in relation to surrealistic artworks to depict human internal desires. The author of this study uncovered the creative potential of image generation techniques, specifically a generative adversarial network (GAN), for creating surrealistic artworks in addition to the traditional imaging techniques used in photography and film, such as cameras, projection, and film editing. This study utilizes a mixed research method involving a literature review of surrealistic film and media art, an analysis on GAN techniques, and media art project development. Based on Freidrich Kittler’s analysis method for media studies, this research examines the operational methods of GAN for image generation. The study summarizes the three steps of GAN techniques (data learning, algorithmic statistics, and model application processes) as showing fragmentary, fluidity and combinatory aspects, which relates well with surrealistic expressions. Based on the findings here, the author created an interactive media art project, termed <Viewporter>, and compared a surrealistic AI technique with social phenomena pertaining to city development. <Viewporter> depicts a landscape of a non-existent city and shows this view through a device that resembles a telescope. Developing this project, the author aimed to visualize the subconscious desires of a group, which the viewer can internalize through the artwork. This study shows the future direction of surrealistic artworks using AI for image generation, which is to evoke and drive the viewer’s subconscious desires.
Current Status on Limit State Method of Design in Crane Design

Conventionally, in designing the strength of machines and structures, it was common to use the allowable stress method of design that uses an empirical safety factor with a sufficient margin in strength. However, since ISO 2394:1973 was published, standards based on limit state method of design that quantitatively evaluates the reliability of materials and structures against damage by means of reliability engineering methods and ensure an appropriate safety margin have been established. The crane design is also similar. In Japan, JIS and construction code for mobile cranes have been established or revised in recent years, and a system that can use the limit state method of design for crane design has been established. In this study, we investigated the difference between the allowable stress method and the limit state method, and the development trend of standards for crane design, and confirmed that the use of the limit state design method in crane design is a global trend.
On a realization of motion and similarity group equivalence classes of labeled points in $\mathbb R^k$ with applications to computer vision

We study a realization of motion and similarity group equivalence classes of $n\geq 1$ labeled points in $\mathbb R^k,\, k\geq 1$ as a metric space with a computable metric. Our study is motivated by applications in computer vision.
HOW DO WE DO WITH DIGITAL TECHNOLOGY? A STUDY ON THE USE OF TECHNOLOGY AND THE SEARCH FOR DIFFERENT TYPES OF CONTENT

C. de Aldama 1, D. García-Pérez 1, A. Aguirre-Camacho 1, G. González-Cuevas 2 1European University of Madrid (SPAIN) 2Idaho State University (UNITED STATES)
On the issue the formation and development of professional competencies of a student - a future history teacher

The article considers the urgent problem of modern higher pedagogical education related to the formation and development of professional competencies of a future teacher. The purpose of the article is to identify, on the basis of analysis of federal state educational standards of higher education, as well as own pedagogical experience of the professional competencies of a future history teacher, which are necessary to improve the quality of teacher training in the process of educational and extracurricular work at a university. The article presents various interpretations of the concept of professional competencies of a teacher presented by both domestic and foreign researchers. Based on the analysis of professional tasks and activities declared in the Federal State Educational Standard of Higher Professional Education and Higher Professional Education (2011, 2014, 2016, 2018). Five professional competencies of a history teacher have been identified. They are included in the curricula and working curricula for the disciplines of the subject preparation of the bachelor's degree (profile History and Social Studies): Methods of teaching and upbringing in historical education, Use of information and communication technologies in teaching history, Game technologies in teaching and upbringing, "Pedagogical workshops" as additional to the general professional competencies contained in the federal state educational standard of higher education. The formation of professional competencies of future history teachers is carried out as part of the students' academic work, and their development and practical application in the process of extracurricular work. The professional competencies of the teacher are characterized by his level of professional training, his attitude to work, to himself and affect the effectiveness of the implementation of educational and educational work in school.
A Novel Framework for the Analysis of Unknown Transactions in Bitcoin: Theory, Model, and Experimental Results

Bitcoin (BTC) is probably the most transparent payment network in the world, thanks to the full history of transactions available to the public. Though, Bitcoin is not a fully anonymous environment, rather a pseudonymous one, accounting for a number of attempts to beat its pseudonimity using clustering techniques. There is, however, a recurring assumption in all the cited deanonymization techniques: that each transaction output has an address attached to it. That assumption is false. An evidence is that, as of block height 591,872, there are several millions transactions with at least one output for which the Bitcoin Core client cannot infer an address. In this paper, we present a novel approach based on sound graph theory for identifying transaction inputs and outputs. Our solution implements two simple yet innovative features: it does not rely on BTC addresses and explores all the transactions stored in the blockchain. All the other existing solutions fail with respect to one or both of the cited features. In detail, we first introduce the concept of Unknown Transaction and provide a new framework to parse the Bitcoin blockchain by taking them into account. Then, we introduce a theoretical model to detect, study, and classify -- for the first time in the literature -- unknown transaction patterns in the user network. Further, in an extensive experimental campaign, we apply our model to the Bitcoin network to uncover hidden transaction patterns within the Bitcoin user network. Results are striking: we discovered more than 30,000 unknown transaction DAGs, with a few of them exhibiting a complex yet ordered topology and potentially connected to automated payment services. To the best of our knowledge, the proposed framework is the only one that enables a complete study of the unknown transaction patterns, hence enabling further research in the fields -- for which we provide some directions.
Thorne Lay: RAS Gold Medallist 2021

Abstract An influential leader in the seismological community, Lay was awarded this year's geophysics Gold Medal for his outstanding work in seismological analysis