Reviewerly/exHarmony huggingface dataset:
Lawfare in the Disinformation Age: Chinese Interference in Taiwan’s 2020 Elections

Despite an aggressive disinformation campaign from the People’s Republic of China (“PRC”), Taiwan effectively countered false information coming from the PRC and maintained the integrity of its January 2020 elections. Political warfare between the PRC and Taiwan (the Republic of China) stretches back decades. However, the digital age has catalyzed the range and effectiveness of “disinformation”—the dissemination of false information with the intention to deceive public opinion. During Taiwan’s 2020 elections, PRC disinformation centered on the political unrest in Hong Kong and sought to sway Taiwanese public opinion toward candidates that were sympathetic to Beijing. Taiwan mounted a robust self-defense against this disinformation through a whole-of-society approach: factchecking by civil society organizations, government-sponsored education for media literacy, and a technology sector that actively curbed inauthentic behavior online. Taiwan’s legal responses to PRC disinformation also demonstrate how specific legislation can counter the spread of rumors domestically and discourage interference from abroad. These lawfare mechanisms included Taiwan’s libel laws, “fake news” regulations, and the Anti-Infiltration Act.Taiwan’s 2020 elections offer a glimpse into the frontlines of disinformation and the novel social media strategies that authoritarian regimes like the PRC use to influence public opinion in democracies. The story of Taiwan’s elections also shows the potential resiliency of democracy in the face of authoritarian disinformation and the possible legal recourse to counter foreign interference. Amid rising election interference and a U.S.-PRC propaganda war surrounding the COVID-19 pandemic, the world urgently needs these lessons.
Rancang Bangun Robot Smart Farming Berbasis Computer Numerical Control (Sensor dan Monitoring)

Urban farming is an activity of farming in urban areas by using the yard area or corners of the house efficiently. The obstacle is the urban community who spend a lot of time working outside. Therefore, tools are needed to do watering, planting seeds, and monitoring plants based on the internet of things. The main objective of this research is to create a sophisticated agricultural system with IOT applications, it can easily maintain the farm, this robot which will later be named Robot Smart Farming (SF-BOT). SF-BOT has options for planting seeds, measuring soil moisture and watering. To implement this feature, SF-BOT works from the Soil Moisture Sensor reading with the help of Arduino and Raspberry pi. In addition SF-BOT can also be to plant seeds and show monitoring and can make streaming video in realtime.SF-BOT is controlled via  webserver interface to select a menu of vegetables to be planted and select the point of planting. In addition, it can also display sensor monitoring and video streaming. The results of the DHT sensor accuration of 98,69 % and magnetic sensor accuration of 100%.  The measurement results can be displayed and can be monitored from the webserver.Keywords: SF-BOT, Soil Moisture, Arduino, Raspberry pi, Internet of  Things
ROLE OF KNOWLEDGE MANAGEMENT ON SERVICE DELIVERY IN PUBLIC HOSPITALS IN NAIROBI CITY COUNTY, KENYA

The purpose of the study was to establish the influence of knowledge management practices on service delivery in the public hospitals in Nairobi City County, Kenya. The study was anchored to adaptive saturation theory, Organizational knowledge conversion theory and Knowledge based theory. The study employed descriptive research design. The target population was 145 head of departments of the public hospitals in Nairobi City County, Kenya. The data collection instruments were questionnaire and study used census approach to collect primary data. Quantitative data collected was analysed by the use of descriptive statistics using SPSS (version 24) as the tool of choice and presented through diverse measures that include percentages, means, standard deviations and frequencies. M ultiple regression analysis was used to establish the relation among the variables. The correlation coefficient was 0.852. This indicated a very strong postive relationship between the independent variable and depedent variable. The study recommended that there is need to enhance knowledge acquisition to improve service delivery in the public hospitals in Kenya. The public hospitals should frequently support conferences for employees to acquire knowledge. The hospital should employee freely dialogues in order to acquire knowledge. The study recommended that there is need to improve knowledge creation on service delivery in the public hospitals in Kenya. The study established that knowledge enables creation of new products and services to meet customer need. The hospitals need to be encouraged to innovate for better services. The study further recommended that public hospitals ought to embrace knowledge sharing as a key driver to innovation and productivity. The public hospitals ought to organize meetings to enable sharing of knowledge and borrowing of better ideas to the enterprises. T here is need to have adequate knowledge storage in the public hospitals in Kenya. There is need for the hospitals to have all important documents filed well for future references. CITATION: Kijugu, M. M., &amp; Moronge, M. (2021). Role of knowledge management on service delivery in public hospitals in Nairobi City County, Kenya. T he Strategic Journal of Business &amp; Change Management, 8 (4), 357 – 371. Key Words: Knowledge Creation, Knowledge Acquisition, Knowledge Sharing, Knowledge Storage
Understanding the Features of Deep Learning

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.
On the Impact of Graph Embedding on Device Placement

Modern neural network (NN) models require more data and parameters in or- der to perform ever more complex tasks. When an NN model becomes too massive to fit on a single machine, it may need to be ...
Depth360: Monocular Depth Estimation using Learnable Axisymmetric Camera Model for Spherical Camera Image

Self-supervised monocular depth estimation has been widely investigated to estimate depth images and relative poses from RGB images. This framework is attractive for researchers because the depth and pose networks can be trained from just time sequence images without the need for the ground truth depth and poses. In this work, we estimate the depth around a robot (360 degree view) using time sequence spherical camera images, from a camera whose parameters are unknown. We propose a learnable axisymmetric camera model which accepts distorted spherical camera images with two fisheye camera images. In addition, we trained our models with a photo-realistic simulator to generate ground truth depth images to provide supervision. Moreover, we introduced loss functions to provide floor constraints to reduce artifacts that can result from reflective floor surfaces. We demonstrate the efficacy of our method using the spherical camera images from the GO Stanford dataset and pinhole camera images from the KITTI dataset to compare our method's performance with that of baseline method in learning the camera parameters.
Blockchain-Based Security and Privacy for Smart Contracts

The speedy growth of cryptocurrencies in recent years and Blockchain technology has revived smart contracts (SC). Decentralization, verifiability, and enforceability are SC characteristics that allow contract terms to be enforced between untrustworthy parties without the interference of a central server or centralized authority. Traditional sectors, such as finance, the Internet of Things (IoT), and management, are expected to be transformed by smart contracts. Smart contracts could be used in a wide range of scenarios in the digital economy and smart industries such as banking, marketing, sanitation, and the IoT. Not only that, but the SC may also provide improvements in the mainstream development process. For the security of information in distributed networks, Blockchain systems such as Ethereum and Hyperledger are trending. The problems related to privacy and security of information need enhancement through analysis, and we discuss the same in this chapter. Blockchain creates secure smart contracts and has some unique features, including: (a) The Smart Contract software code is recorded and tested on the Blockchain, thereby rendering the Contract resistant to interference. (b) The smart contract execution is imposed anonymously, trustless single nodes with no centralized power and cooperation of third-party administrations. (c) The Smart Contract acts as an intelligent agent with its cryptocurrency (or possibly other digital assets) to transfer when certain conditions are met. Smart contracts are computer protocols that use Blockchain technology to digitally verify, facilitate, and enforce agreements between two or more parties. This chapter aims to provide a comprehensive and systematic overview of Blockchain-based Smart Contracts and their emerging research fields.
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks

This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs. Then, we combine the results of these runs to obtain the final result. We prove that DropGNNs can distinguish various graph neighborhoods that cannot be separated by message passing GNNs. We derive theoretical bounds for the number of runs required to ensure a reliable distribution of dropouts, and we prove several properties regarding the expressive capabilities and limits of DropGNNs. We experimentally validate our theoretical findings on expressiveness. Furthermore, we show that DropGNNs perform competitively on established GNN benchmarks.
Mean Nyström Embeddings for Adaptive Compressive Learning.

Compressive learning is an approach to efficient large scale learning based on sketching an entire dataset to a single mean embedding (the sketch), i.e. a vector of generalized moments. The learning task is then approximately solved as an inverse problem using an adapted parametric model. Previous works in this context have focused on sketches obtained by averaging random features, that while universal can be poorly adapted to the problem at hand. In this paper, we propose and study the idea of performing sketching based on data-dependent Nystr\om approximation. From a theoretical perspective we prove that the excess risk can be controlled under a geometric assumption relating the parametric model used to learn from the sketch and the covariance operator associated to the task at hand. Empirically, we show for k-means clustering and Gaussian modeling that for a fixed sketch size, Nystr\om sketches indeed outperform those built with random features.
Assessment Of Perceptions Of Villagers About Various Yogic Procedures At Rasulabad

Background: The word ‘Yoga’ itself suggests its divinity and spirituality. It can also be considered as a path to attain utter peace of mind that helps to enhance person's intellectual, results in prosperity. It is a Gift of our Ancestors which can uplift the entire human race to ultimate Salvation. It is obtained from ‘Yuj’ word means ‘to unite or integrate’ . The term in the modern realm frequently indicates a new method  which is considered as Hatha Yoga, Yoga correlated with exercise, involving positions mainly and is known as Asanas. The Yogic exercise has an extensive history of combined regime as a science. The persons repeatedly do Yoga exclusively find that it bits nearly each aspect of their inner with outer survives. It’s all about coordinating the body with the mind and breathes by means of many breathing exercises, Asanas and meditation. Aim: To assess perceptions of villagers about various Yogic procedures at Rasulabad Objectives: To enumerate the perception of villagers of Rasulabad and to assess the various factors responsible for motivation of Yoga among the Villagers.
AI창작물의 특허보호 방안

Today's AI invention refers to cases where data preprocessing, such as data structures for learning which process collected data, learning model definitions required for learning, physical implementations, physical implementations performed by applying Deep Learning learning programs, and derived learning completion models (AI-based SW) are implemented in computer-like hardware in algorithmic form, and these AI inventions satisfy the establishment of invention and are included in a target to be patented. However, input data are input into the learning completion model (AI-based SW), and the AI creation in that the AI creates by the input data is not patentable because it is not the invention but the invention generated by the AI, which is non-natural, under the current patent law. Therefore, there is a problem that the AI technology is not sufficiently patent-protected under the current patent law. In particular, there is various problems: (i) whether the AI creation created by the AI can be regarded as an invention created by humans (natural people), (ii) countries around the world recognize only humans as inventors, whether the AI can be recognized as an inventor, (iii) whether there is a way to recognize the AI as an inventor, (iv) whether there is a way to solve the problem of authority and duty as a rightful person, responsibility for exercise of rights, etc. that may arise if the AI is recognized as an inventor. In this article, we considered the revised plan of the Patent Act to include AI-generated creations as targets to be patented. As a result, the definition of invention under Article 2 of the Patent Act 1 will be specific to inventions, such as Article 101 of the US Patent Act, and If there is a substantial human contribution for AI to create, not an artificial act of humans, the revised plan is needed to introduce AI-generated AI creations and definitions of AI inventions on the premise of that human contribution. For those who are eligible for patents, it is needed to introduce a regulation in Article 33 of the Patent Act stating that the inventor for AI creations is to be the on the premise of human contribution.
MENINGKATKAN HASIL BELAJAR SISWA MELALUI PENGGUNAAN METODE PEMBELAJARAN HOLISTIK MATERI SENI PATUNG KELAS IX-4 SMP NEGERI 29 MEDAN TAHUN PELAJARAN 2019/2020

This shows that students at the time of answering questions are still low. After giving the action during the post-test cycle I, the results obtained from 32 students were 14 students who completed or 43.59%, with a class average of 58.43, there was an increase in learning completeness in the initial test and post-test I of 27.97 %, and in the first cycle the maximum mastery of individual and classical learning outcomes has not been obtained, then proceed to the second cycle and obtained learning mastery, namely from 32 students there are 29 people who have completed learning or 90.62%, and an average of 79, 06, in the second cycle, students have completed to obtain good learning outcomes, so this research ends here.
Jurassic is (Almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue

One challenge with open-domain dialogue systems is the need to produce truthful, high-quality responses on any topic. We aim to improve the quality and coverage of Athena, an Alexa Prize dialogue system. We experiment with few-shot prompt-based learning, comparing GPT-Neo to Jurassic-1 for the movies, music, TV, sports, and video game domains, both within and cross-domain, with different prompt set sizes (2, 3, 10), formats, and meaning representations consisting of either set of WikiData KG triples, or dialogue acts. Our evaluation uses bleurt and human metrics and shows that with 10-shot prompting, Athena-Jurassic’s performance is significantly better for coherence and semantic accuracy. Experiments with a 2-shot cross-domain prompt result in a considerable performance drop for Athena-GPT-Neo, whose semantic accuracy falls to 0.41, and whose untrue hallucination rate increases to 12%. Experiments with dialogue acts for video games show that with 10-shot prompting, both models learn to control dialogue acts, but Athena-Jurassic has significantly higher coherence and only 4% untrue hallucinations. Our results suggest that Athena-Jurassic produces high enough quality outputs to be useful in live systems with real users. To our knowledge, these are the first results demonstrating that few-shot semantic prompt-based learning can create NLGs that generalize to new domains and produce high-quality, semantically controlled, conversational responses directly from meaning representations.
A Novel Approach for Detecting Cryptocurrency Miners Using Traffic Forensics

The idea of this approach is the invention of the cryptocurrency in the past years. This invention opens new opportunities for the financial world for buying, investing, and mining cryptocurrency. Digital currency does provide anonymity for customers and uses it because it uses blockchain technologies and provides confidentiality, integrity, non-repudiation, and authentication. Cryptographic is the fundamental establishment for all cryptocurrencies. As this invention, huge cryptocurrencies on the internet like Bitcoin are the first currency that was invented and others like Ethereum, Tether, and XRP. Miners are the most significant part of the cryptocurrency world. They solve a complicated math puzzle and get profit. Criminals use cryptocurrency miners by installing it in victim computers or an organization network. Even they could inject a sample javascript on any website they control to mine cryptocurrency when a victim visits their hacked site. In this paper, we will introduce how a forensic could detect and defends against a mining cryptocurrency by analyzing the network traffics manually and using public tools.
An Optimized Dynamic Migration Method of Virtual Machine

Virtualization technology, as the technical support for cloud computing, frees users from the limitations of hardware resources. Dynamic migration technology enables virtual machines to be moved across hosts, and services running on the virtual machines are basically uninterrupted during the migration process, which greatly facilitates the maintenance work in the data center. However, dynamic migration needs to transmit a large amount of repeated data. In order to improve the migration efficiency, this paper conducts the following research on the original pre-copy algorithm. First, we introduce a high dirty page determination algorithm based on the locality principle to classify memory pages and propose an improved memory migration pre-copy algorithm on this basis. Second, in order to solve the problem that the pre-copy algorithm cannot converge under a high load, an improved VM downtime determination algorithm is proposed. Third, the paper also introduces the LZ4 compression algorithm to further optimize the memory migration process. Finally, this paper implements the above optimization scheme on a KVM platform and tests its performance. It is compared with the native pre-copy algorithm and Three-Phase Optimization algorithm (TPO). The result shows that the optimized scheme significantly reduces the total migration time and downtime.
The search of Type I codes.

A self-dual binary linear code is called Type I code if it has singly-even codewords, i.e.~it has codewords with weight divisible by $2.$ The purpose of this paper is to investigate interesting properties of Type I codes of different lengths. Further, we build up a computer-based code-searching program based on our knowledge about Type I codes. Some computation results achieved by this program are given.
Identifying phishing as a form of cybercrime in Nigeria

Phishing is one of the oldest and most flexible types of social engineering attacks and could be used in many ways, and for different purposes, to lure unwary users to sites and trick them into entering personal information. This paper is written with the purpose of educating the public about phishing as a form of cybercrime. It adopts the use of doctrinal research methodology in analyzing phishing as a form of cybercrime, discussing its historical development, techniques and its criminalization under the Cybercrime Act in Nigeria. The paper further highlights the various ways of identifying messages that are phishing in nature. Aside from the general conclusion, the paper enumerates some of the things which a person can do when confronted with an attempted phishing scam. The paper recommends that the general public should be more suspicious of all electronic communications and websites especially those communications which were not initiated by them.
La conscience P, la conscience A et la liberté [la fonction de la conscience et l'avenir de l'humanité] (P-Consciousness, A-Consciousness and Freedom [the Function of Consciousness and the Future of Humanity])

The English version of this paper can be found at: http://ssrn.com/abstract=3525051.French Abstract: Pour apprehender comment se presente la liberte humaine, j'ai propose de distinguer l'espace des options qui peuvent etre choisies, de l'instance decisionnelle qui choisit. Dans la premiere partie de l'article, a partir de la distinction entre la conscience d'acces et la conscience phenomenale, nous allons regarder comment la conscience participe a l'instance decisionnelle.Dans la deuxieme partie, apres avoir defendu l'idee que la fonction de la conscience etait la survie, nous allons integrer une autre caracteristique de la conscience humaine, l'attention conjointe. Cela va nous permettre de definir les concepts de conscience collective et d'inconscience collective, avec l'espoir d'amenuiser la seconde en ameliorant la premiere.English abstract: In order to understand how human freedom presents itself, I have proposed to distinguish the space of options that can be chosen, from the decision-making instance that chooses. In the first part of the article, starting with the distinction between access consciousness and phenomenal consciousness, we will look at how consciousness participates in the decision-making instance.In the second part, after having argued that the function of consciousness is survival, we will integrate another characteristic of human consciousness, joint attention. This will allow us to define the concepts of collective consciousness and collective unconsciousness, with the hope of reducing the latter by improving the former.