Reviewerly/exHarmony huggingface dataset:
Streams of Approximations, Equivalence of Recursive Effectful Programs

Behavioural equivalence for functional languages with algebraic effects and general recursion is often difficult to formalize. When modelling the behaviour of higher-order programs, one often needs to employ multiple coinductive structures simultaneously. In this paper, we aim to simplify the issue with a single external stream monad dealing with recursion, and deal with the structures for modelling higher-order types in a purely inductive (finite) manner. We take a page from classical domain theory, modelling recursive programs as limits of increasing sequences of “finite” denotational elements. We carry around a single relation which contains all relevant information of behaviour: self-related elements are considered correct according to the denotational semantics, and two elements that are related both ways are considered equivalent. We implement equivalence of effectful programs using a relation lifting, creating a notion of behavioural equivalence for approximations. This is lifted to behavioural equivalence for recursive programs modelled by sequences of approximations, using a relator implementing a notion of weak similarity. We apply this to a fragment of call-by-push-value lambda calculus with algebraic effects, and establish a denotational equivalence which is sound with respect to an operational semantics, and sound with respect to contextual equivalence.
Rating of Doctor Using Tokenization Mechanism Using Secure Ethereum Blockchain Enabled Platform

AbstractToday whenever there is a critical requirement of an expert healthcare advice or a requirement of a specialist doctor not much means are available to verify the creditability of the doctor. If we are able to maintain the doctors creditability on a system than it becomes an asset of record which will be available in a secure and tamper proof manner. On each successful handling of a case healthcare agencies may upload the data of the success and there can be a mechanism designed to update the doctor’s creditability by assigning a token. Tokens may be categorized depending on the success rate for which a parameter in medical terms may be fixed. For this Ethereum blockchain may be used. The Smart Contract would execute every time to update the tokens of the doctors registered. As more and more Health care agencies become a part of the system it would provide a standard platform to make a selection of a doctor based on the severity of the diseases.KeywordsBlockchainEthereumSolidityTamper-proof record
A Efektivitas Penggunaan Aplikasi Fiqhline Dalam Meningkatkan Motivasi Belajar Materi Tata Cara Shalat Jenazah di MAN 1 Ponorogo

The cause of the students' lack of understanding of the material presented is that the teacher explains the material only using the lecture method, e-learning is difficult to access and in delivering the material only uses PPT (Power Point) media which makes students less understanding and less enthusiastic in participating. learning activities because they seem monotonous and saturated in the explanation of the material. The results of the development of the FIQHLINE application after being tested on class X Religion 1 MAN 1 students above, it can be concluded that the average pre-test and post-test is 8.706. Furthermore, based on the results of the t-test analysis obtained T count of 13.356 with a significance of 0.000. The T table value with 5% significance is 2.032 with a significant value less than 0.05 (p = 0.000). From the explanation above, it can be interpreted that there is a significant increase in student learning outcomes for class X Religion 1 MAN 1 Ponorogo .. So it can be interpreted that the FIQHLINE application learning media can improve student learning outcomes so that it is effective for growing student learning motivation.
Large-scale Translation to Enable Response Selection in Low Resource Languages: A COVID-19 Chatbot Experiment

Natural Language Processing for Low Resource Languages is challenging. The lack of large-scale datasets affects the performance of data-hungry algorithms. To overcome this, we employ data augmentation to enlarge the training data for the task of response selection in multi-turn retrieval-based chatbots. We automatically translated a large-scale English dataset to Brazilian Portuguese (PT_BR) and used it to train a deep neural network. For a COVID-19 chatbot system, our results show that the combination of training with the translated dataset followed by a fine-tuning with the context-specific dataset provides the best results in terms of recall for all studied models. In addition, we make available the translated large-scale PT_BR dataset.
Strategy Game Using Adaptive Agents and Reinforcement Learning

This paper presents the functional specifications of a training platform for use in the training of cadets and serving officers. The game aims to teach trainees that their decisions need to be based both on strategic goals as well as on constraints imposed by international law concerning rules of engagement, ethical codes of conduct or environmental issues. Existing techniques bearing similarities to the required solution are presented. The importance of the problem is hence demonstrated and the possibilities for enhancing the quality of the learning experience for both students and instructors are explored. The rules of a computer game suitable for the cadets training are presented. The game is shown to be sufficient for use in the teaching of strategy ethics. The study leads to high level specifications for the design of the proposed training game. The functional design of the proposed game is then explained and its rules are listed. The design of adaptive software agents that are capable of assuming the role of non-player characters is presented. Further research work necessary for the development of optimization algorithms necessary for the design is described.
MBET: Resilience Improvement Method for DNNs

Deep neural network (DNN) accelerators become a large study field. Low voltage DNN accelerators are designed to achieve high throughput and reduce energy consumption. Using low voltage leads to many bit errors in DNN weights. One method to increase fault tolerance against random bit errors is random bit error training. In this paper, we improve this method with multiple bit error rate training (MBET). MBET aims to improve the fault tolerance of the DNN model with using more than one bit error rates. During the training, we inject bit errors with different rates and combine the corresponding loss values. The experimental results on 4 state-of-the-art models show that this method improves fault tolerance of the model against random bit errors while it does not decrease the test accuracy of the model.
Should We Translate? Evaluating Toxicity in Online Comments when Translating from Portuguese to English

Social media and online discussion platforms suffer from the prevalence of uncivil behavior, such as harassment and abuse, seeking to curb toxic comments. There are several approaches to classifying toxic comments automatically. Some of them have more resources and are more advanced in English, thus, stimulating the task of translating the text from a specific language to English. While researchers have shown evidence that this practice is indicated for certain tasks, such as sentiment analysis, little is known in the context of toxicity identification. In this research, we assess the performance of a freely available model for toxic language detection in online comments called Perspective API, widely adopted by some famous news media sites to identify different toxicity classes in online comments. For that, we obtained comments in Portuguese from two Brazilian news media websites during a politically polarized situation as a use case. Then, this dataset was translated to English and compared to four baseline datasets, two composed of highly toxic comments, one in Portuguese and other in English, and two composed of neutral comments, also one in Portuguese and other in English – all of them in its original language, not translated. Finally, human-annotated comments from the news comments dataset were analyzed to assess the scores provided by the Perspective API for the original and the translated versions. Results indicate that keeping the texts in their original language is preferable, even in comparing different languages. Nevertheless, if the translated version is strictly necessary, ways of dealing with the situation were suggested to preserve as much information as possible from the original version.
Sentiment Analysis on Twitter Repercussion of Police Operations

Violence and a sense of insecurity are among the main problems in urban centres. In Brazil, an average rate of 20 deaths per month is estimated for every 100,000 inhabitants due to violence. Virtual social networks are increasingly used as a means for users to express their opinions or indignation about this problem. In this article, we analyze the sentiment of users in comments shared on Twitter about police operations with great repercussions in news portals in Brazil. In this sense, we explore lexicon and machine learning models to understand the emotion in which users discuss public safety on social networks and their opinion about the work of government agencies to reduce violence in cities. Our experiments show how challenging this inference is given peculiar characteristics of the context, such as mostly negative and sarcastic expressions. Nevertheless, our best classifiers achieved accuracy and specificity (macro F1) greater than 60% for identifying sentiments polarity, indicating a promising methodology for automatically inferring public opinion about police operations.
Pengaruh Pelatihan Quantum Learning Untuk Mengatasi Learning Loss Siswa Pasca Pandemi Covid 19

The covid-19 pandemic has left problems in various sectors, especially the education. Currently schools have started to open, meaning that life is back to normal where students return to learning in class with health protocols. During the transition from online learning to face-to-face learning, it becomes a difficult time for students because for almost 2 years students who have not studied online optimally tend to experience what is called learning loss, namely a condition of decline in the learning process or falling behind in learning. Students must catch up with the setbacks in the learning process due to the limitations of online learning. The way that can be done to deal with learning loss is to teach students effective learning strategies. One of the learning strategies such as quantum learning is effective in improving student learning outcomes. The activity offered is quantum learning training for students who experience learning loss due to the covid 19 pandemic. The training activity was carried out on December 1, 2021 in the hall of SMA Negeri 2 Pariaman. A pretest and posttest were given to 20 students. The results of the pretest and posttest showed Asymp. Sig. (2-tailed) of 0.000 which is less than the critical research limit of 0.05 (p &lt; 0.05) which means that there is an effect of quantum learning training to overcome student learning loss after the covid 19 pandemic. This training was smooth and able to provide knowledge to students in overcoming learning loss and have motivation to learn.
The New Requirements Of Classroom Teaching For Teacher Under The New Situation In China

In the new times, classroom leaning is not the only way yet to obtain knowledge, which raises some new requirements for classroom teaching of teacher. To rejuvenate the classroom instruction, the only way is to proactively face the challenge and catch up with the new development. In this article, three main styles of classroom teaching approaches applied usually today in China are reviewed and the upsides and downsides compared to the traditional classroom teaching method are pointed out firstly. Then, the pivotal elements of the classroom teaching reforms, including modern teaching-aid means, learner-centered teaching and the new measures for teaching evaluation, are discussed. Finally, the conclusion is drawn: in the new era, the new measure of classroom teaching-the improvement of the student learning effectiveness-requires the student to proactively take part in the classroom learning, which requires classroom teacher to create the student-centered classroom teaching atmosphere, advance personal quality and put into use the modern teaching-aid means to improve the level of the classroom teaching.
”Do they pass the woman test?”

Residential solar panels are increasingly popular, yet women are largely invisible as customers and users. This creates barriers for reaching gender equality and climate goals where increased renewable energy is key. We present results from a norm-critical study drawing on 10 interviews with solar industry representatives and focus groups with 28 women, either owning solar panels or in the process of buying. The study aims to critically analyze current gender norms related to technology, market, and use, as well as to identify difficulties for women's solar panel engagement. The study shows how women at different touchpoints in the process of buying and having solar panels both navigate and negotiate an ongoing gendering of this technology, despite the industry attempts to present solar panels as gender neutral. While the study focuses on residential solar panels, the contribution is relevant for wider HCI, e.g. work related to smart home technologies.
Sistem Informasi Retribusi Terminal Berbasis Website

Retribution information system is very important and indispensable in a terminal. At the Sabilambo Kolaka Terminal, especially in recapitulating retribution income data, they still use recording in the ledger, this makes it difficult for terminal officers to make a report so that the process for reporting terminal retribution income data to the Transportation Office in its implementation is still not optimal and over time there are losses and losses. data collection in the form of sheets of paper. Based on these problems, the researchers conducted a study with the title "Case Study Terminal Retribution Information System: Sabilambo Terminal". The information system is made using HTML, CSS, Javascript and the PHP programming language and MySQL as the database and data management process. The results of this study can be concluded that the system created works well and meets the requirements of the Sabilambo terminal. This can be seen from the survey results obtained from the questionnaire (93.53), indicating a value. A (very good).
“That such things are”: The Non-Teleological Poetics of Thoreau’s Journal

In his last decade Henry David Thoreau devoted himself to the writing of his Journal, a project that entailed not just the composition of thousands of pages but also a rigorous practice of close daily observation of nature. This essay tries to explain the logic behind Thoreau’s surprising turn away from published writing, as well as to explore the text that resulted from it. It finds in the Journal a unique combination of poetic, philosophical, scientific, and spiritual impulses and practices, rather than the dedication to scientific aims which recent scholars claim characterized Thoreau’s late career. Instead of pursuing private goals or making public contributions to a particular field or discipline, Thoreau in the Journal immerses himself in purposes other than his own, namely, the myriad purposes of animals, plants, and natural phenomena. The result is a sui generis text without a plan, a narrative, or even a narrator.
Test and Validation of a Multi-Block Solution for Improved Tracking in Outdoor Scenarios: A Case Study in the Pinocchio Park

Augmented reality techniques have recently found many applications in the field of cultural heritage. When used in outdoor scenarios, however, this technology can face several issues, mainly due to unstable light conditions, jeopardizing the users’ experience. Various solutions to this problem have been proposed in the literature; however, none of them are fully effective. This paper introduces a solution based on a multi-block image target segmentation and a dedicated add-on for the Unity3D game engine. After tests in the lab, the solution was validated in a real scenario at Pinocchio Park (Collodi, Italy), using two different Augmented Reality (AR) libraries and comparing it to a standard methodology. Quantitative results show that the proposed approach provides superior performance and usability. Although the proposed solution is still open to improvement, it combines effectiveness and ease of implementation without any drawbacks.
Health Risk Assessment of Some Selected Heavy Metals in Agricultural Soils from Katsina State, North-Western Nigeria

This work contributes to the monitoring of heavy metal pollution of Agricultural soils, Katsina State Nigeria, using Atomic Absorption Spectrophotometry. The heavy metal contamination of the soil samples were analyzed based on the Geoaccumulation index (Igeo), enrichment factor (EF), contamination factor (CF), pollution load index (PLI) and potential ecological risk index (PERI). The health risks of the evaluated heavy metals were estimated using the Hazard Quotient (HQ) and Hazard Index (HI)) to evaluate the possible non-carcinogenic effect and the Incremental Lifetime Cancer Risk (ILCR) for the cancer risk to the population. The results of the study had revealed that in the soil samples all the evaluated heavy metals lie within acceptable limits as set by the regulatory agencies. The evaluated soil samples pollution indices had revealed that the I-geo values for the soil samples were within the range of unpolluted to moderate pollution. Also, the heavy metal enrichment factor (EF) value for the soil samples have indicated that only the heavy metal Fe showed significant enrichment, with the soil samples being moderately contaminated with Fe. The pollution load index (PLI) also indicated unpolluted to moderate pollution. With the potential ecological risk index (PERI) values presenting low ecological risks. The calculated non-cancer risk indices in both the children and adults population for all the heavy metals were less than 1. With the exception of the ILCR for the heavy metal Pb in children from Daura zone that was in limits that environmental and regulatory agencies considered as unacceptable risk, risk values for all the heavy metals falls within the range of the threshold of the safe limit and limits regarded as safe by the regulatory agencies (10-7 to 10-4). The results of pollution indices have indicated that the Agricultural soil samples have low contamination and low health risks by the heavy metals evaluated.
Multi-robot Cooperation and Path Planning Using Modified Cuckoo Search

The paper proposes an innovative approach to solve the cooperation and path planning problem of multiple mobile robots in clutter environment. The main emphasis of the work lies in designing a multi-objective fitness function for stick-carrying robot pairs to compute a collision-free optimal path. The present context of the paper address the multi-robot cooperation and path planning of two pairs of stick-carrying robots that move from a predefined initial location to a pre-assumed goal position by carrying a stick at either end. The basic cuckoo search (CS) algorithm is modified concerning the step size and the scaling parameter at each step of movement of the robot pairs. The modified cuckoo search (MCS) algorithm is implemented with the robot pair to mimic the egg laying behavior of the cuckoo for producing the next generation solution. The proposed algorithm is validated using computer simulation and has been compared with other existing approaches such as ICFA, CS, SDA, and ABCO. Due to its simplicity and efficacy in terms of path optimality, the proposed algorithm produces an optimal solution both in the static and dynamic environment irrespective of the number of obstacles.
Richness Fallacy

The paper points at the grieving problems implied by the richness axiom in the Kleinberg's axiomatic system and suggests resolutions. The richness induces learnability problem in general and leads to conflicts with consistency axiom. As a resolution, learnability constraints and usage of centric consistency or restriction of the domain of considered clusterings to super-ball-clusterings is proposed.
Adapting Loss Functions to Learning Progress Improves Accuracy of Classification in Neural Networks

Power error loss (PEL) has recently been suggested as a more efficient generalization of binary or categorical cross entropy (BCE/CCE). However, as PEL requires to adapt the exponent q of a power function to training data and learning progress, it has been argued that the observed improvements may be due to implicitly optimizing learning rate. Here we invalidate this argument by optimizing learning rate in each training step. We find that PEL clearly remains superior over BCE/CCE if q is properly decreased during learning. This proves that the dominant mechanism of PEL is better adapting to output error distributions, rather than implicitly manipulating learning rate.
GRACE (-FO) data discovery, access and download from NASA Earthdata Cloud

&amp;lt;p&amp;gt;PO.DAAC has pioneered data migration to the NASA Earthdata Cloud (Amazon Web Service &amp;amp;#8211; AWS) beginning in January 2021. Today, the entire active data collections archived at PO.DAAC have been successfully migrated. The migration effort includes not just the datasets, but also the access tools/services and documentation, together forming the new PO.DAAC-in-Cloud (POCLOUD) data ecosystem, which brings challenges but also opportunities to the users with many new capabilities. In the new cloud paradigm, the traditional PO.DAAC data search portal will continue to serve as the main data discovery platform for its datasets. In addition, NASA has designed an Earthdata search portal for discovering the earth science datasets and granules across all 12 NASA data centers. To access the POCLOUD data, most of the on-premise tools and services are (will be) made available except PO.DAAC Drive, which will be retired after the cloud data migration. Currently, the data direct download (https), AWS S3 buckets access, OPeNDAP, the Level 2 subsetting tool HiTIDE, and the visualization tool SOTO have been added to POCLOUD datasets. A collection of Harmony APIs, which are a common family of services for discovering , accessing and transforming the Earth observation data in the cloud from different NASA data centers, are also made available to most POCLOUD data, including services for subsetting, Zarr reformatting, and regridding. Users are encouraged to find helpful tutorials, demos and recipes from PO.DAAC Cloud Data page, such as, the powerful and efficient podaac-data-subscriber script, tutorials on OPeNDAP, Amazon S3 bucket direct access, and many more cloud data applications.&amp;lt;/p&amp;gt;