Reviewerly/exHarmony huggingface dataset:
Sistem Pendukung Keputusan Menentukan Kualitas Ayam Petelur Menggunakan Metode Simple Additive Weighting

Determining the selection of quality laying hens is a decision that must be carried out so that production results will increase and the quality of the eggs produced, there are lots of criteria that must be owned for the complexity and importance of the problem requiring analytical methods rather than intuitive decisions. as a condition for obtaining the scholarship. Each laying hens must have criteria to determine the quality of laying hens. Decision Support System Determining the Quality of Laying Hens with the Simple Additive Weighting (SAW) Method is an element that greatly influences the smooth running of the task. The division in this study will be appointed a case, namely looking for the best alternative based on predetermined criteria using the SAW (Simple Additive) method. Weighting) The research was conducted by finding the weight value for each attribute, then a ranking process was carried out which would determine the optimal alternative, namely the best quality laying hens.
Telkom University News Topic Modeling Using Latent Semantic Analysis (LSA) Method on Online News Portal

In this day and age, the development of online news portals regarding news is quite easy to access, online news portals are information that explains an event that has occurred or is happening with electronic media intermediaries, as well as news about Telkom University which is quite easily accessible through online news portals. A system has been designed that is capable of modeling Telkom University news topics. Modeling news topics is very interesting to be used as research material because the process of understanding each individual on the topics contained in the news is different, therefore topic modeling is needed to find out what topics are news about Telkom University. In this study, a Latent Semantic Analysis (LSA) model has been designed to carry out a topic modeling process that aims to make it easier for readers to understand news topics related to Telkom University, Latent Semantic Analysis (LSA) is a mathematical method in finding hidden topics by analyzing the structure semantics of the text. After doing several research scenarios, the best coherence score was 0.524 with a total of six topics.
Two-epoch TLS deformation analysis of a double curved wooden structure using approximating B-spline surfaces and fully-populated synthetic covariance matrices

This contribution presents a B-spline-based approach of area-wise deformation analysis applied on elements of a double curved wooden tower. The monitored object is the Urbach Tower with a height of 14 m. Terrestrial laser scans from two epochs acquired under real-world conditions are used for approximating two jointly parametrized B-spline surfaces of the tower’s outer shell. The stochastic model of the observations used within the surface approximation is based on elementary error theory and is defined by a synthetic variance-covariance matrix (SVCM). In addition to previous work on this topic, the object’s dimension is extended from a few dm to a few m and the measurement distance ranges from 20 to 60 m. Moreover, environment specific error sources are addressed in the SVCM, revealing the effect of the object’s dimension as well as of additional elementary errors on the estimated B-spline surfaces and the subsequent deformation analysis. Based on constructed points pairs using a grid of surface parameters, rigid body movements of the object under investigation are estimated while at the same time distorted regions of the wooden tower are detected. All results of the deformation analysis are statistically verified using hypothesis tests based on the elementary error model propagated through the processing algorithms of surface estimation and deformation analysis. The results demonstrate that during the modelling and deformation analysis, the measurement noise is reduced and therefore distorted regions are detectable in a statistically correct way.
Analyzing Comments Made to The Duolingo Mobile Application with Topic Modeling

There are approximately 6.4 billion mobile users in today's world.With this widespread use, people now prefer devices that are easier to carry and use, such as mobile devices.At this point, mobile applications play a very important role.Mobile applications are preferred for many purposes, especially for entertainment, education and social activities.In foreign language learning, there are hundreds of mobile applications that contain different activities for many foreign languages.Duolingo is an application that stands out in terms of providing many foreign language support and learning by gamification.Users of this application, which has such a widespread use, give feedback with their ratings and comments.More than one million comments have been left on the Duolingo app so far.This study was carried out in order to analyze these comments and to determine which points the users bring to the fore, and to provide preliminary information for both software developers and those who want to look for similar applications based on these points.The comments were analyzed by the Latent Dirichlet Allocation (LDA) method using the Python programming language and NLTK, Gensim libraries.The topics detected by both the word bag term frequency and the term frequency-inverse document frequency (TF-IDF) method are shown.
Survey on Modern Cryptography

Security is one of the significant difficulties that individuals are confronting when the information exchanged over the internet in today's world. Cryptography is the way to achieve security of network or data by hiding the important information from unauthorized individuals. Encryption is a mechanism of converting plain-text into cipher-text in order to be unreadable by unwanted people. In this review, we explain some important cryptography primitives ( symmetric and asymmetric cryptography algorithms) with examples for each type. Comparative study is making among this cryptography primitives according to many parameters such as structure, speed, block size and others parameters.
Students’ Perception toward English Online Learning

&nbsp; Abstract: This research aimed at knowing the students' perception towards English online learning during Covid-19 pandemic at SMAN 1 Cipanas. This research uses qualitative method with a case study approach. The data were collected through interviews with students. Out of 38 students, researcher only interviewed 13 people of XI MIPA 3 at SMAN 1 Cipanas participated in this research. The result of this research was presented descriptively in order to reveal the students' perception. The results of this study found positive and negative perceptions. Some students stated that learning English while at home was quite effective, because it is the only way out to continue learning online while the Covid-19 pandemic is still in Indonesia, so they can still learn even though it is not directly at school. And some argue that online learning English is not effective, because there are obstacles such as difficult signals, bored, sometimes there are students who do not have a quota and limited space for students to talk to the teacher &nbsp
AI, Equity, and the IP Gap

Artificial intelligence (AI) has helped determine vaccine recipients, prioritize emergency room admissions, and ascertain individual hires, sometimes doing so inequitably. As we emerge from the Pandemic, technological progress and efficiency demands continue to press all areas of the law, including intellectual property (IP) law, toward incorporating more AI into legal practice. This may be good when AI promotes economic and social justice in the IP system. However, AI may amplify inequity as biased developers create biased algorithms with biased inputs or rely on biased proxies. This Article argues that policymakers need to take a thoughtful and concerted approach to graft AI into IP law and practice if social justice principles of access, inclusion, and empowerment flow from their union. It explores what it looks like to obtain AI justice in the IP context and focuses on two areas where IP law impedes equitable AI-related outcomes. The first involves the civil rights concerns that stem from trade secrets blocking access and deflecting accountability in biased algorithms or data. The second concerns the patent and copyright doctrine biases perpetuating historical inequity in AI-augmented processes. The Article also ad- dresses how equity by design should look and provides a roadmap for implementing equity audits to mitigate bias. Finally, it briefly examines how AI would assist with adjudicating equitable IP law doctrines, which also tests the outer limits of what bounded AI processes can do.
Implementation of The Speech Recognition System Using a real time web Server Based

a Speech Recognition is a system used to recognize word commands from human voices and then translated them into data that can be understood by a computer. The advantages of Speech Recognition include speed and ease of use. Merdeka Malang is a private university founded on January 29, 1964. The University of Merdeka Malang still doesn’t have a strategy in recommending new students based on historical data calculations in choosing faculties. and majors. In calculating the history, if you use a manual system, they will be constrained by the time and accuracy of the data, causing the data to be less valid.Therefore, there is a need for a system that can exchange data in real-time so that the data displayed is always up to date without manual data updating. This process is useful for monitoring and accelerating the process of calculating student interest data, which can later be recommended to future new students. Moreover, The speech technique used is using speech recognition which is used for processing speech data on smartphones while sending data using the XAMPP web server which is used to exchange data from smartphones to servers.&#x0D;
Certainty Factor Method Analysis for Identification of Covid-19 Virus Accuracy

Corona virus or often called COVID-19 is a virus caused by SARS CoV 2, where the incident was uploaded in the world of health or we often call WHO. Even the World Health Organization (WHO) has declared that the corona virus outbreak is a Public Health Emergency of International Concern (PHEIC) or an international public health emergency. Not only has an impact on health, but this virus outbreak has also had a major impact in various sectors such as disrupting the country's economy, disrupting the education process and so on. This impact is caused by the very fast spread of the virus. Therefore, the author will analyze the level of accuracy in the covid-19 virus by using the certainty method model which aims to make it easier for local governments to monitor the spread of the COVID-19 virus and can determine future policies so that the spread is not more easily exposed to the community. this method will produce data analysis and diagnoses regarding identifying the covid-19 virus with results in the form of accuracy, namely someone is indicated as COVID-19 POSITIVE.
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog

A typical end-to-end task-oriented dialog system transfers context into dialog state, and upon which generates a response, which usually faces the problem of error propagation from both previously generated inaccurate dialog states and responses, especially in low-resource scenarios. To alleviate these issues, we propose BORT, a back and denoising reconstruction approach for end-to-end task-oriented dialog system. Squarely, to improve the accuracy of dialog states, back reconstruction is used to reconstruct the original input context from the generated dialog states since inaccurate dialog states cannot recover the corresponding input context. To enhance the denoising capability of the model to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response. Extensive experiments conducted on MultiWOZ 2.0 and CamRest676 show the effectiveness of BORT. Furthermore, BORT demonstrates its advanced capabilities in the zero-shot domain and low-resource scenarios.
ADAPT : Awesome Domain Adaptation Python Toolbox

In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods. The library is designed with a user friendly approach to facilitate the access to domain adaptation for a wide public. ADAPT is compatible with scikit-learn and TensorFlow and a full documentation is proposed online https://adapt-python.github.io/adapt/ with a substantial gallery of examples.
Liability Rules for the Digital Age

Abstract With legislative proposals for two directives published in September 2022, the European Commission aims to adapt the existing liability system to the challenges posed by digitalisation. One of the proposals is related and limited to liability for artificial intelligent (AI) systems, but the other contains nothing less than a full revision of the 1985 Product Liability Directive, which lies at the heart of European tort law. Whereas the current Product Liability Directive largely followed the model of US law, the revised version breaks new ground. It does not limit itself to the expansion of the concept of product to include intangible digital goods, such as software and data as well as related services, important enough in itself, but also targets the new intermediaries of e-commerce as liable parties. As such, the proposal for a new product liability directive is a great leap forward and has the potential to grow into a worldwide benchmark in the field. In comparison, the proposal of a directive on AI liability is much harder to assess. It remains questionable whether a second directive is actually needed at this stage of the technological development.
Sequential Imagery Lossless Compression Algorithm for Space Astronomical Observation

空间天文观测任务会获得大量天文图像.对定点天体连续观测得到的序列天文图像具有时间及空间冗余较高的特点.为了减少序列天文图像的存储与传输的数据量，保证序列天文图像的完整性，满足科学目标的任务需求，需要对其进行无损压缩.本文提出了一种利用帧内压缩与改进的帧间压缩相结合的无损压缩算法，将序列天文图像的第一帧进行JPEG-LS帧内无损压缩编码，其余帧进行改进的帧间无损压缩编码，从而有效去除序列天文图像的时间及空间冗余，提高序列天文图像的压缩比.经过试验测试，改进后的帧间压缩效果优于帧内压缩效果，改进后的帧间压缩时间少于帧内压缩时间.结果表明，该算法简单且高效，适用于对序列天文图像的无损压缩.
‘Out of time’: Constructing teacher professionality as a perpetual project on the eTwinning digital platform

This paper seeks to understand what digital schooling platforms do to teacher professionality; that is, the combination of professional knowledge, discretion and responsibility that enables a teacher to be professional. Specifically, we explore how the European Commission’s (EC) teacher professional learning platform eTwinning promotes a projectified (i.e., project-focused) and platformed (i.e., largely occurring on digital platforms) version of teacher professionality. Informed by recent thinking around ‘projectification’; that is, the ability of the project form to shape work practices, as well as the topological nature of timespace within a project, we argue that projectified teacher learning and professionality are now constituted through platform dynamics as a perpetual project-initself. As such, the projectified teacher is left simultaneously in-time (i.e., within the bounds of the project timespace) and out-of-time (i.e., out of possibilities of progress that can exist outside of the project), and thus faces the insuperable task of never-ending self-improvement through and as the project form (teacher-as-project).
Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling

Nested sampling is an efficient method for calculating Bayesian evidence in data analysis and partition functions of potential energies. It is based on an exploration using a dynamical set of sampling points that evolves to higher values of the sampled function. When several maxima are present, this exploration can be a very difficult task. Different codes implement different strategies. Local maxima are generally treated separately, applying cluster recognition of the sampling points based on machine learning methods. We present here the development and implementation of different search and clustering methods on the nested_fit code. Slice sampling and the uniform search method are added in addition to the random walk already implemented. Three new cluster recognition methods are also developed. The efficiency of the different strategies, in terms of accuracy and number of likelihood calls, is compared considering a series of benchmark tests, including model comparison and a harmonic energy potential. Slice sampling proves to be the most stable and accurate search strategy. The different clustering methods present similar results but with very different computing time and scaling. Different choices of the stopping criterion of the algorithm, another critical issue of nested sampling, are also investigated with the harmonic energy potential.
Role of artificial intelligence in the enabling sustainable supply chain management during COVID-19

The purpose of the paper is to investigate the functions of artificial intelligence in supply chain management. In recent years, artificial intelligence framework has accomplished human-like performances in various previously considered computationally impossible tasks. Better access to large amounts of information, improved algorithms, and advanced hardware systems have led to artificial technology development. Artificial technology has supported business organisations to enhance their data collection abilities with the rapid advancement of different tools. If a product depends on supplies from multiple suppliers, disruptions can have subsequent effects. Organisations must redesign supply chains, improve flexibility, and re-evaluate the relationship with suppliers to reduce systematic risks. The methodology used in this study is a critical review of previous literature related to this topic. We searched the articles in the English language by following general research procedures. We manually searched different relevant articles from EBSCO, ProQuest, Emerald Insight, Science direct, Taylor & Francis, Wiley, JSTOR, and IEEE. The findings present the significant functions of artificial intelligence on sustainable supply chain management in the COVID-19 scenario. Future research perspective is also discussed.
HyperNST: Hyper-Networks for Neural Style Transfer

We present HyperNST; a neural style transfer (NST) technique for the artistic stylization of images, based on Hyper-networks and the StyleGAN2 architecture. Our contribution is a novel method for inducing style transfer parameterized by a metric space, pre-trained for style-based visual search (SBVS). We show for the first time that such space may be used to drive NST, enabling the application and interpolation of styles from an SBVS system. The technical contribution is a hyper-network that predicts weight updates to a StyleGAN2 pre-trained over a diverse gamut of artistic content (portraits), tailoring the style parameterization on a per-region basis using a semantic map of the facial regions. We show HyperNST to exceed state of the art in content preservation for our stylized content while retaining good style transfer performance.
Exploring Metaverse: A Virtual Ecosystem from Management Perspective

An interconnected digital ecosystem, seamlessly blending the virtual and physical worlds, is termed as 'metaverse'.The metaverse offers unlimited virtual space to participants such as corporates and individuals, to explore and design their experiences.The present study showcases the potential of the metaverse to enhance the operations in retail, hospitality & tourism, and entertainment industries etc.The study posits that adoption of metaverse in these industries will alter the consumer decision-making journey, and their subsequent behavior.Corporate leaders by mapping this virtual space, where consumers have their digital doppelganger or avatars representing their digital persona, can reach the consumer across geographies.The study highlights that immersive experiences offered by the metaverse will be the essence of higher customer involvement, leading to value co-creation and seamless exchange of value.The final section of the study provides a perspective on the emerging issues and challenges in the form of privacy, security, racism, and digital hatred in the metaverse world.
Fault-tolerant quantum simulation of materials using Bloch orbitals

The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.
Blockchain Implementation in E-Commerce to Improve The Security Online Transactions

The implementation of blockchain technology in E-commerce has become an increasingly important topic in recent years. Blockchain can bring big changes in the E-commerce industry, especially in increasing customer security and trust in online transactions. In this article, it is explained how blockchain technology can be used in E-commerce, such as in increasing the security of online transactions, product tracking, use of smart contracts, and digital identities. Nonetheless, the implementation of blockchain technology also has challenges and obstacles that need to be overcome, such as the considerable cost and time involved in system development and integration, as well as regulatory and legal issues related to the use of cryptocurrencies and digital identities. Therefore, the implementation of blockchain technology in E-commerce requires careful strategy and planning to maximize the benefits that this technology can provide.