Reviewerly/exHarmony huggingface dataset:
Tracking the Covid zones through geo-fencing technique

Purpose Because of the outbreak of Covid 19, the entire world is thinking of new strategies, preventive measures to safeguard the human life from the widespread of the pandemic. The areas where people are affected are marked as containment zones and people are not allowed to exit out of those areas. Similarly, new people are not allowed to enter inside those areas. Hence, the purpose of this paper is to propose a methodology to track the Covid zones, to enhance and tighten the security measures. A geo-fence is created for the containment zone. The person who enters or exits out of that particular zone will be monitored and alert message will be sent to that person’s mobile. Design/methodology/approach After tracking the location of a suspicious individual, the geo-fenced layer is mapped in the area and then the virtual perimeter is used for further trapping process. This geo-fenced layer can be viewed by the citizens as soon as it is updated by the Covid monitoring team. The geo-fencing is a concept of building a virtual perimeter area. This virtual perimeter monitoring system helps in monitoring the containment zones effectively. It reduces operational costs by using an automated system based on wireless infrastructure. It also alerts the authorities immediately to catch the violators. Thus, it helps to speed up the process of inspecting the containment zones and monitoring the individuals who violate the rules given by government. Findings The proposed methodologies will be an effective way to track the Covid’s communal spread. But the workflow of the system demands the required data sets and permission in legal manner to set up the environment that maintains the constitutional law and order in practice. The application developed was a prototype to display how it works if the required data sets are provided by the government. There are several tracking models that are released across the world such as Aarogya setu (India), Trace together (Singapore) and Hagmen (Israel). All these models are based on Bluetooth proximity identification; though Bluetooth proximity identification is helpful for high range in a short distance, the privacy concern is debatable one. Using modern technology, it is so easy to crack the individual gadgets and with Bluetooth enabling it makes things even worse. Thus, it is important to maintain the tracking a safer and secure one, and another issue with those Bluetooth-based applications is that tracking can be done only if the user enabled the Bluetooth option, if not the entire functioning would become a mess. The proposed methodology of tracking without Bluetooth will ensure data security also. Originality/value This was developed as a project by our third-year students of the Department of Information Technology of our college.
What Authors Think about Hypertext Authoring

Despite significant research into authoring tools for interactive narratives and a number of established authoring platforms, there is still a lack of understanding around the authoring process itself, and the challenges that authors face when writing hypertext and other forms of interactive narratives. This has led to a monolithic view of authoring, which has hindered tool design, resulting in tools that can lack focus, or ignore important parts of the creative process. In order to understand how authors practise writing, we conducted semi-structured interviews with 20 interactive narrative authors. Using a qualitative analysis, we coded their comments to identify both processes and challenges, and then mapped these against each other in order to understand where issues occurred during the authoring process. In our previous work we were able to gather together a set of authoring steps that were relevant to interactive narratives through a review of the academic literature. Those steps were: Training/Support, Planning, Visualising/Structuring, Writing, Editing, and Compiling/Testing. In this work we discovered two additional authoring steps, Ideation and Publishing that had not been previously identified in our reviews of the academic literature - as these are practical concerns of authors that are invisible to researchers. For challenges we identified 18 codes under 5 themes, falling into 3 phases of development: Pre-production, where issues fall under User/Tool Misalignment and Documentation; Production, adding issues under Complexity and Programming Environment; and Post-production, replacing previous issues with longer term issues related to the narrative's Lifecycle. Our work shows that the authoring problem goes beyond the technical difficulties of using a system, rather it is rooted in the common misalignment between the authors' expectations and the tools capabilities, the fundamental tension between expressivity and complexity, and the invisibility of the edges of the process to researchers and tool builders. Our work suggests that a less monolithic view of authoring would allow designers to create more focused tools and address issues specifically at the places in which they occur.
Pengembangan E-Learning dengan Metode Self Assessment Untuk Meningkatkan Hasil Belajar Matematika Mahasiswa Universitas Mahendradatta

This study is aimed to develop e-learning media with self-assessment methods to improve mathematics learning outcomes of college students. The model used in the development of e-learning was the ADDIE model. The sample of this study was 39 college students of the Industrial Engineering Program, Faculty of Engineering, Mahendradatta University. The data analyzed in this study were student’s mathematics learning outcomes obtained from the posttest scores after using e-learning with self assessment method. To determine the effectiveness of the implementation of e-learning, experimenation with one group pretest and posttest design was conducted. Data of learning outcomes obtained were analyzed by using the Paired T-Test. Based on the results of expert validation and product trials, e-learning was feasible to be implemented. Based on the results of the Paired T-Test, a significant value of less than 0.05 was obtained, so based on the hypothesis that there was a difference between the learning outcomes before and after e-learning was implemented. This was also supported by an increase in the average value of student mathematics learning outcomes, from the initial pretest score of 75,92 to 82,10. Student response value was 69,0 which indicated that the response to e-learning was positive. The increase in learning outcomes was due to e-learning learning with self-assessment methods which provided opportunities for students to increase their confidence in their mathematical skills.
Hayek and the cryptocurrency revolution

The emergence of cryptocurrencies has been one of the most notable monetary phenomenon of the last decade. Many academics and analysts have found a clear precedent to this event in Friedrich Hayek's latest monetary work, Denationalization of money. The aim of this article is to analyze what we can learn about cryptocurrencies by re-reading this book. As will be proven, Hayek would surely have rejected the idea that Bitcoin and cryptocurrencies with similar characteristics could be accepted as money in the market. Furthermore, this paper will prove that a very close connection between Stablecoins and private money exists, following the Austrian economist’s predictions in a context of monetary competition.
A Point-of-Use Quality Assurance Tool for Digital Pathology Remote Working

Pathology services are facing pressures due to the COVID-19 pandemic. Digital pathology has the capability to meet some of these unprecedented challenges by allowing remote diagnoses to be made at home, during periods of social distancing or self-isolation. However, while digital pathology allows diagnoses to be made on standard computer screens, unregulated home environments may not be conducive for optimal viewing conditions. There is also a paucity of experimental evidence available to support the minimum display requirements for digital pathology. This study presents a Point-of-Use Quality Assurance (POUQA) tool for remote assessment of viewing conditions for reporting digital pathology slides. The tool is a psychophysical test combining previous work from successfully implemented quality assurance tools in both pathology and radiology to provide a minimally intrusive display screen validation task, before viewing digital slides. The test is specific to pathology assessment in that it requires visual discrimination between colors derived from hematoxylin and eosin staining, with a perceptual difference of ±1 delta E (dE). This tool evaluates the transfer of a 1 dE signal through the digital image display chain, including the observers' contrast and color responses within the test color range. The web-based system has been rapidly developed and deployed as a response to the COVID-19 pandemic and may be used by anyone in the world to help optimize flexible working conditions at: http://www. virtualpathology.leeds.ac.uk/res earch/systems/pouqa/.
Towards a Blockchain Special Purpose Vehicle for Financing Independent Renewable Electricity Projects in Sub-Saharan Africa

Sub-Saharan Africa requires affordable, reliable, and sustainable electricity to boost its economic, social, and human development. The main challenge posed to the region's electricity sector is the large investment gap needed to finance new power projects. The employment of new and innovative financing options is required to bridge this investment gap. Independent power projects have become one of the fastest-growing sources of new finance in the region. However, their development is constrained by the limited availability of debt finance for project implementation. The limited capital and bureaucratic burden of traditional financial institutions coupled with the high risks in the region ensures that the debt finance required by independent power projects is raised only after an arduous voyage and at high interest rates. We address these challenges by proposing a novel decentralized finance instrument, a blockchain special purpose vehicle that streamlines the processes in the financial layer of a traditional special purpose vehicle-finance mobilization, revenue collection, and revenue disbursal. Specifically, the proposed decentralized finance instrument facilitates the mobilization of finance for the special purpose vehicle from a location-independent crowd, revenue collection from the electricity off taker in a risk-mitigated manner, and disbursal of eventual project revenues to investors.
User Preference and Categories for Error Responses in Conversational User Interfaces

Error messages are frequent in interactions with Conversational User Interfaces (CUI). Smart speakers respond to about every third user request with an error message. Errors can heavily affect user experience (UX) in interaction with CUI. However, there is limited research on how error responses should be formulated. In this paper, we present a system to study how people classify different categories (acknowledgement of user sentiment, acknowledgement of error and apology) of error messages, and evaluate peoples' preference of error responses with clear categories. The results indicate that if an error response has only one element (i.e. neutral acknowledgement of error, apology or sentiment), responses that acknowledge errors neutrally are preferred by participants. Moreover, we find that when interviewed, participants like error messages to include an apology, an explanation of what went wrong, and a suggestion how to fix the problem in addition to a neutral acknowledgement of an error. Our study has two main contributions: (1) our results inform about the design of error messages and (2) we present a framework for error response categorization and validation.
Connecting GANs, MFGs, and OT

Generative adversarial networks (GANs) have enjoyed tremendous success in image generation and processing, and have recently attracted growing interests in financial modelings. This paper analyzes GANs from the perspectives of mean-field games (MFGs) and optimal transport. More specifically, from the game theoretical perspective, GANs are interpreted as MFGs under Pareto Optimality criterion or mean-field controls; from the optimal transport perspective, GANs are to minimize the optimal transport cost indexed by the generator from the known latent distribution to the unknown true distribution of data. The MFGs perspective of GANs leads to a GAN-based computational method (MFGANs) to solve MFGs: one neural network for the backward Hamilton-Jacobi-Bellman equation and one neural network for the forward Fokker-Planck equation, with the two neural networks trained in an adversarial way. Numerical experiments demonstrate superior performance of this proposed algorithm, especially in the higher dimensional case, when compared with existing neural network approaches.
Revisiting Fundamentals of Experience Replay

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic and extensive analysis of experience replay in Q-learning methods, focusing on two fundamental properties: the replay capacity and the ratio of learning updates to experience collected (replay ratio). Our additive and ablative studies upend conventional wisdom around experience replay -- greater capacity is found to substantially increase the performance of certain algorithms, while leaving others unaffected. Counterintuitively we show that theoretically ungrounded, uncorrected n-step returns are uniquely beneficial while other techniques confer limited benefit for sifting through larger memory. Separately, by directly controlling the replay ratio we contextualize previous observations in the literature and empirically measure its importance across a variety of deep RL algorithms. Finally, we conclude by testing a set of hypotheses on the nature of these performance benefits.
Generalization and Invariances in the Presence of Unobserved Confounding.

The ability to extrapolate, or generalize, from observed to new related environments is central to any form of reliable machine learning, yet most methods fail when moving beyond $i.i.d$ data. In some cases, the reason lies in a misappreciation of the causal structure that governs the observed data. But, in others, it is unobserved data, such as hidden confounders, that drive changes in observed distributions and distort observed correlations. In this paper, we argue that generalization must be defined with respect to a broader class of distribution shifts, irrespective of their origin (arising from changes in observed, unobserved or target variables). We propose a new learning principle from which we may expect an explicit notion of generalization to certain new environments, even in the presence of hidden confounding. This principle leads us to formulate a general objective that may be paired with any gradient-based learning algorithm; algorithms that have a causal interpretation in some cases and enjoy notions of predictive stability in others. We demonstrate the empirical performance of our approach on healthcare data from different modalities, including image and speech data.
Blockchain: A Panacea for Trust Challenges In Public Services? A Socio-technical Perspective

Trust in corporations, governments and public services has been steadily declining over the last few decades.Lack of transparency and auditability has been a key driver for this decline.Blockchain technology has been commended as a solution that can help with disintermediation and filling the consistently increasing trust challenges faced by the corporate and public sectors.Public services are seeking solutions that can help establish trust and increase transparency with its citizens and businesses are undertaking extensive business analysis to determine the need and effectiveness of blockchain-like platforms as the basis for transforming their existing platforms.Due to the decisive nature, most of the analysis results thus indicate that if a trusted third party is an option, then blockchain should not be used.Here we highlight the challenges and opportunities of establishing trust and how blockchain technology can help public services bridge the trust gap with its citizens.We argue that all information technology systems rely on a suite of technologies, thus blockchain should be added to the current technology stack rather than taking an 'all or nothing' approach.We also argue that analysing the effectiveness of futuristic technology like blockchain with industrial age methodology and mindset may limit the realisation of its impact on society and economy.Therefore, we propose to take a heuristic approach, where different properties of blockchain technology need to be mapped against different aspects of current business process with a futuristic view in mind.Taking Companies House -a government organisation that holds over 4 million UK-based companies' records -as an example, we demonstrate how certain business processes in Companies House can benefit from adapting a blockchain-based solution.
Persepsi Mahasiswa Atas Penggunaan Aplikasi Perkuliahan Daring Saat Wabah Covid-19

The purpose of this study is to explore the activities and perceptions of students in conducting online lectures through applications during the COVID-19 outbreak . This research uses a quantitative approach with a survey method with the research respondents are 781 IBI Kesatuan students who are active in the Even Semester Academic Year 2019/2020. The selection of research subjects is done through purposive sampling techniques . D ata study were analyzed using analysis kuantit Atif descriptive percentage technique. The results showed that students gave positive responses to the implementation of these online lectures and were willing to always follow them. The family fully supports students to attend online lectures while at home . The limitation of internet access quota is an obstacle for students in attending online lectures. The use of GCR and WAG applications received positive responses from students because of their flexible, quota-friendly internet access and easy to use them.&#x0D; &#x0D; Keywords: online lectures, covid-19, student perceptions, applications
Revisiting Evaluation of Knowledge Base Completion Models

Representing knowledge graphs (KGs) by learning embeddings for entities and relations has led to accurate models for existing KG completion benchmarks. However, due to the open-world assumption of existing KGs, evaluation of KG completion uses ranking metrics and triple classification with negative samples, and is thus unable to directly assess models on the goals of the task: completion. In this paper, we first study the shortcomings of these evaluation metrics. Specifically, we demonstrate that these metrics (1) are unreliable for estimating how calibrated the models are, (2) make strong assumptions that are often violated, and 3) do not sufficiently, and consistently, differentiate embedding methods from each other, or from simpler approaches. To address these issues, we gather a semi-complete KG referred as YAGO3-TC, using a random subgraph from the test and validation data of YAGO3-10, which enables us to compute accurate triple classification accuracy on this data. Conducting thorough experiments on existing models, we provide new insights and directions for the KG completion research. Along with the dataset and the open source implementation of the models, we also provide a leaderboard for knowledge graph completion that consists of a hidden, and growing, test set, available at https://pouyapez.github.io/yago3-tc/.
Privacy-aware data cleaning-as-a-service

Data cleaning is a pervasive problem for organizations as they try to reap value from their data. Recent advances in networking and cloud computing technology have fueled a new computing paradigm called Database-as-a-Service, where data management tasks are outsourced to large service providers. In this paper, we consider a Data Cleaning-as-a-Service model that allows a client to interact with a data cleaning provider who hosts curated, and sensitive data. We present PACAS: a Privacy-Aware data Cleaning-As-a-Service model that facilitates interaction between the parties with client query requests for data, and a service provider using a data pricing scheme that computes prices according to data sensitivity. We propose new extensions to the model to define generalized data repairs that obfuscate sensitive data to allow data sharing between the client and service provider. We present a new semantic distance measure to quantify the utility of such repairs, and we re-define the notion of consistency in the presence of generalized values. The PACAS model uses (X, Y, L)-anonymity that extends existing data publishing techniques to consider the semantics in the data while protecting sensitive values. Our evaluation over real data show that PACAS safeguards semantically related sensitive values, and provides lower repair errors compared to existing privacy-aware cleaning techniques.
High-Speed Masking for Polynomial Comparison in Lattice-based KEMs

With the NIST post-quantum standardization competition entering the second round, the interest in practical implementation results of the remaining NIST candidates is steadily growing. Especially implementations on embedded devices are often not protected against side-channel attacks, such as differential power analysis. In this regard, the application of countermeasures against side-channel attacks to candidates of the NIST standardization process is still an understudied topic. Our work aims to contribute to the NIST competition by enabling a more realistic judgment of the overhead cost introduced by side-channel countermeasures that are applied to lattice-based KEMs that achieve CCA-security based on the Fujisaki-Okamoto transform. We present a novel higher-order masking scheme that enables an efficient comparison of polynomials as previous techniques based on arithmetic-to-Boolean conversions renders this (generally inexpensive) component extremely expensive in the masked case. Our approach has linear complexity in the number of shares compared to quadratic complexity of previous contributions and it applies to lattice based schemes with prime modulus. It comes with a proof in the probing model and an efficient implementation on an ARM Cortex-M4F microcontroller which was defined as a preferred evaluation platform for embedded implementations by NIST. Our algorithm can be executed in only 1.5-2.2 milliseconds on the target platform (depending on the masking order) and is therefore well suited even for lightweight applications. While in previous work, practical side-channel experiments were conducted using only 5,000 - 100,000 power traces, we confirm the absence of first-order leakage in this work by collecting 1 million power traces and applying the t-test methodology.
Effective Opinion Words Extraction for Food Reviews Classification

Opinion mining (known as sentiment analysis or emotion Artificial Intelligence) holds important roles for ecommerce and benefits to numerous business and organizations.It studies the use of natural language processing, text analysis, computational linguistics, and biometrics to provide us business valuable insights into how people feel about our product brand or service.In this study, we investigate reviews from Amazon Fine Food Reviews dataset including about 500,000 reviews and propose a method to transform reviews into features including Opinion Words which then can be used for reviews classification tasks by machine learning algorithms.From the obtained results, we evaluate useful Opinion Words which can be informative to identify whether the review is positive or negative.