Reviewerly/exHarmony huggingface dataset:
The impact of the planting of forest biomass energy plants under the embedded Internet of Things technology on the biodiversity of the local environmental ecology

Among a variety of renewable energy sources, only the use of forest biomass instead of fossil fuel is an excellent choice, because forest biomass is not only rich in reserves, but also has a relatively small impact on the local ecological environment after its consumption. Because the current development technology for forest biomass energy is not yet mature, the use of energy is relatively low. Therefore, it is imperative to construct a relatively standardized calculation model for forest biomass energy. To build a calculation model for forest biomass energy, it is necessary to rely on new technologies such as sensing technology, big data technology, and precise calculation control, especially biomass power generation and the demand for IoT technology from multiple energy sources. Based on the comparative analysis of the various parts of forest biomass energy, and relying on the overall framework of the Internet of Things technology, this article finds the integration point for the application of the Internet of Things technology in forest biomass energy and biodiversity monitoring. Through the use of PSCAD software, the planting of biomass energy plants was simulated, the composition type of forest biomass energy resources was determined, and the estimation model of forest biomass energy resources was established based on the bottom-up method, and its possible impact Carried out analysis and discussion. In the follow-up work, electromagnetic transient simulation software was used to simulate the planting of biomass energy plants, so as to determine the composition structure of forest biomass energy, and construct the calculation model of forest biomass energy using the method of linking up and down, and In-depth research on its possible impact. In view of the possible energy grid connection problems, solutions are proposed, and simulation experiments are carried out. Based on the actual construction data, the electromagnetic transient simulation software is used for simulation. The results show that the method in this paper can effectively provide data simulation for the development and utilization of forest biomass energy.
Are DeFi Tokens a Separate Asset Class from Conventional Cryptocurrencies?

We test for the existence of bubbles in DeFi-focused cryptocurrencies, seeking to identify key driving forces that distinguish DeFi tokens from conventional cryptocurrencies. Conducting Supremum Augmented Dickey-Fuller, and Hacker-Hatemi-J modified Wald tests, as well as Diebold-Yilmaz return and volatility spillover analysis, results evidence the presence of bubbles in key DeFi assets during the third quarter of 2020. Additional causality analysis suggests that, while Bitcoin has a causal nexus with most conventional tokens, amongst DeFi it only causes Link and Mkr. According to Diebold-Yilmaz test results, while spillovers of returns and volatilities between the conventional and DeFi markets are predominantly between Bitcoin and Mkr, contra general views about Bitcoin’s dominance, Ethereum is found to be the leading cryptocurrency influencer of conventional tokens. However, we do not observe Ethereum or Bitcoin causing bubbles in the DeFi market. Rather we find Link and Mkr are primary contributors, in terms of returns and volatilities, to DeFi bubbles. Results of our analysis suggest that the DeFi market should be viewed as a separate asset class from conventional cryptocurrencies. However, this view is slightly nuanced, with strong correlations between certain DeFi tokens and Bitcoin. DeFi tokens that are particularly prominent (Mkr and Link) are closest to acting like conventional tokens. Our findings suggest that groupings of cryptocurrencies manifest as separate asset classes; therefore the operational process of portfolio construction needs to consider inclusion of DeFi cryptocurrencies in order to optimize diversification.
mpi4jax: Zero-copy MPI communication of JAX arrays

The tensor framework JAX (Bradbury et al., 2018) combines expressivity and performance while retaining an accessible pure Python interface.Expressivity is achieved by treating functions as first-class objects, while efficiency is obtained by compiling to machine code justahead-of-time.* Contributed equally, order determined by coin flip.
Face Mask Detection in Real-Time using MobileNetv2

Face mask detection has made considerable progress in the field of computer vision since the start of the Covid-19 epidemic. Many efforts are being made to develop software that can detect whether or not someone is wearing a mask. Many methods and strategies have been used to construct face detection models. A created model for detecting face masks is described in this paper, which uses “deep learning”, “TensorFlow”, “Keras”, and “OpenCV”. The MobilenetV2 architecture is used as a foundation for the classifier to perform real-time mask identification. The present model dedicates 80 percent of the training dataset to training and 20% to testing, and splits the training dataset into 80% training and 20% validation, resulting in a final model with 65 percent of the dataset for training, 15 percent for validation, and 20% for testing. The optimization approach used in this experiment is “stochastic gradient descent” with momentum (“SGD”), with a learning rate of 0.001 and momentum of 0.85. The training and validation accuracy rose until they reached their maximal peak at epoch 12, with 99% training accuracy and 98% validation accuracy. The model's training and validation losses both reduced until they reached their lowest at epoch 12, with a validation loss of 0.050% and a training loss of less than 0.025%. This system allows for real-time detection of someone is missing the appropriate face mask. This model is particularly resource-efficient when it comes to deployment, thus it can be employed for safety. So, this technique can be merged with embedded application systems at public places and public services places as airports, trains stations, workplaces, and schools to ensure subordination to the guidelines for public safety. The current version is compatible with both IP and non-IP cameras. Web and desktop apps can use the live video feed for detection. The program can also be linked to the entrance gates, allowing only those who are wearing masks to enter. It can also be used in shopping malls and universities.
Learning interpretable representations of entanglement in quantum optics experiments using deep generative models

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are the core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experiment setups. In a series of computational experiments, we investigate the learned representation of our quantum optics variational autoencoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that QOVAE learns an interpretable representation of quantum optics experiments and the relationship between the experiment structure and entanglement. We show QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. QOVAE and the insights from our investigations can be immediately applied to other physical systems. A variational autoencoder is trained on a dataset of quantum optics experiment configurations and learns an interpretable representation of the relationship between experiment setup and quantum entanglement. The approach can be used to explore new experiment designs with specific, highly entangled states.
Quantitative evaluation of visual guidance effects for 360-degree directions

Abstract A head-mounted display cannot cover an angle of visual field as wide as that of natural view (out-of-view problem). To enhance the visual cognition of an immersive environment, previous studies have developed various guidance designs that visualize the location or direction of items presented in the users’ surroundings. However, two issues regarding the guidance effects remain unresolved: How are the guidance effects different with each guided direction? How much is the cognitive load required by the guidance? To investigate the two issues, we performed a visual search task in an immersive environment and measured the search time of a target and time spent to recognize a guidance design. In this task, participants searched for a target presented on a head-mounted display and reported the target color while using a guidance design. The guidance designs (a moving window, 3D arrow, radiation, spherical gradation, and 3D radar) and target directions were manipulated. The search times showed an interaction effect between guidance designs and guided directions, e.g., the 3D arrow and radar shorten the search time for targets presented at the back of users. The recognition times showed that the participants required short times to recognize the details of the moving window and radiation but long times for the 3D arrow, spherical gradation, and 3D radar. These results suggest that the moving window and radiation are effective with respect to cognitive load, but the 3D arrow and radar are effective for guiding users’ attention to necessary items presented at the out-of-view.
The ABC of linear regression analysis: What every author and editor should know

Regression analysis is a widely used statistical technique to build a model from a set of data on two or more variables. Linear regression is based on linear correlation, and assumes that change in one variable is accompanied by a proportional change in another variable. Simple linear regression, or bivariate regression, is used for predicting the value of one variable from another variable (predictor); however, multiple linear regression, which enables us to analyse more than one predictor or variable, is more commonly used. This paper explains both simple and multiple linear regressions illustrated with an example of analysis and also discusses some common errors in presenting the results of regression, including inappropriate titles, causal language, inappropriate conclusions, and misinterpretation.
Blockchain-enabled real-time SLA monitoring for cloud-hosted services

Abstract Cloud computing is an important technology for businesses and individual users to obtain computing resources over the Internet on-demand and flexibly. Although cloud computing has been adopted across diverse applications, the owners of time-and-performance critical applications require cloud service providers’ guarantees about their services, such as availability and response times. Service Level Agreements (SLAs) are a mechanism to communicate and enforce such guarantees typically represented as service level objectives (SLOs), and financial penalties are imposed on SLO violations. Due to delays and inaccuracies caused by manual processing, an automatic method to periodically verify SLA terms in a transparent and trustworthy manner is fundamental to effective SLA monitoring, leading to the acceptance and credibility of such service to the customers of cloud services. This paper presents a blockchain-based distributed infrastructure that leverages fundamental blockchain properties to achieve immutable and trustworthy SLA monitoring within cloud services. The paper carries out an in-depth empirical investigation for the scalability of the proposed system in order to address the challenge of transparently enforcing real-time monitoring of cloud-hosted services leveraging blockchain technology. This will enable all the stakeholders to enforce accurate execution of SLA without any imprecisions and delays by maintaining an immutable ledger publicly across blockchain network. The experimentation takes into consideration several attributes of blockchain which are critical in achieving optimum performance. The paper also investigates key characteristics of these factors and their impact to the behaviour of the system for further scaling it up under various cases for increased service utilization.
Debiased Explainable Pairwise Ranking from Implicit Feedback

Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback. Specifically, we address two limitations of BPR: (1) BPR is a black box model that does not explain its outputs, thus limiting the user’s trust in the recommendations, and the analyst’s ability to scrutinize a model’s outputs; and (2) BPR is vulnerable to exposure bias due to the data being Missing Not At Random (MNAR). This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system. In this work, we first propose a novel explainable loss function and a corresponding Matrix Factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations. Then, we theoretically quantify additional exposure bias resulting from the explainability, and use it as a basis to propose an unbiased estimator for the ideal EBPR loss. The result is a ranking model that aptly captures both debiased and explainable user preferences. Finally, we perform an empirical study on three real-world datasets that demonstrate the advantages of our proposed models.
Interacting with digitised historical newspapers: understanding the use of digital surrogates as primary sources

Purpose The paper examines academic historians' information interactions with material from digital historical-newspaper collections as the research process unfolds. Design/methodology/approach The study employed qualitative analysis from in-depth interviews with Finnish history scholars who use digitised historical newspapers as primary sources for their research. A model for task-based information interaction guided the collection and analysis of data. Findings The study revealed numerous information interactions within activities related to task-planning, the search process, selecting and working with the items and synthesis and reporting. The information interactions differ with the activities involved, which call for system support mechanisms specific to each activity type. Various activities feature information search, which is an essential research method for those using digital collections in the compilation and analysis of data. Furthermore, application of quantitative methods and multidisciplinary collaboration may be shaping culture in history research toward convergence with the research culture of the natural sciences. Originality/value For sustainable digital humanities infrastructure and digital collections, it is of great importance that system designers understand how the collections are accessed, why and their use in the real-world context. The study enriches understanding of the collections' utilisation and advances a theoretical framework for explicating task-based information interaction.
A novel DNA-based key scrambling technique for image encryption

Abstract Nowadays, data are the most valuable content in the world. In the age of big data, we are generating quintillions of data daily in the form of text, image, video, etc. Among them, images are highly used in daily communications. Various types of images, e.g., medical images, military images, etc. are highly confidential. But, due to data vulnerabilities, transmitting such images in a secured way is a great challenge. For this reason, researchers proposed different image cryptography algorithms. Recently, biological deoxyribonucleic acid (DNA)-based concepts are getting popular for ensuring image security as well as encryption as they show good performance. However, these DNA-based methods have some limitations, e.g., these are not dynamic and their performance results are far from ideal values. Further, these encryption methods usually involve two steps, confusion and diffusion. Confusion increases huge time complexity and needs to send one or more additional map tables with a cipher to decrypt the message. In this research, we propose a novel and efficient DNA-based key scrambling technique for image encryption that addresses the above limitations. We evaluate our proposed method using 15 different datasets and achieved superior performance scores of entropy, keyspace, cipher pixel correlations, variance of histogram, time complexity and PSNR. The experimental results show that our method can be used for image encryption with a high level of confidentiality.
Improving Unsupervised Question Answering via Summarization-Informed Question Generation

Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair.Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer.A disadvantage of the heuristic approach is that the generated questions are heavily tied to their declarative counterparts.A disadvantage of the supervised approach is that they are heavily tied to the domain/language of the QA dataset used as training data.In order to overcome these shortcomings, we propose an unsupervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system.We make use of freely available news summary data, transforming declarative summary sentences into appropriate questions using heuristics informed by dependency parsing, named entity recognition and semantic role labeling.The resulting questions are then combined with the original news articles to train an end-to-end neural QG model.We extrinsically evaluate our approach using unsupervised QA: our QG model is used to generate synthetic QA pairs for training a QA model.Experimental results show that, trained with only 20k English Wikipedia-based synthetic QA pairs, the QA model substantially outperforms previous unsupervised models on three in-domain datasets (SQuAD1.1,Natural Questions, TriviaQA) and three out-of-domain datasets (NewsQA, BioASQ, DuoRC), demonstrating the transferability of the approach.
PENGEMBANGAN E-LKPD MATERI BILANGAN PECAHAN BERBASIS PROBLEM BASED LEARNING PADA KELAS IV SEKOLAH DASAR

Student worksheet usually in printed form, while Electronic student worksheet in digital form, so it could be accessed anywhere using the internet network. This study aims to develop and determine suitability of Electronic worksheets on fractional number material in grade 4th. Type of research is R&amp;D using the 4-D Thiagarajan and Sammel model which consists of four step, 1) Define, 2) Design, 3) Develop, 4) Disseminate. The subjects of this research were 4th grade students of SDN Menteng Atas 01 Pagi. Data instruments in the form of observations, interviews, expert review and student response questionnaires. The expert review process is carried out on material, media, and language experts. The average result of the three experts is 86.20% and can be interpreted as "very feasible". The COVID-19 virus pandemic has made the field trial process only using the zoom application, namely with one to one evaluation and small group. The results of each field trial are 83.66% and 88.54%, which means "very feasible". Based on the test result of GEBEKA Electronic student worksheet, it is appropriate to use it as for learning fractional number material in grade 4th Elementary School.
Blockchain-Envisioned Secure Authentication Approach in AIoT: Applications, Challenges, and Future Research

The Artificial Intelligence of Things (AIoT) is the amalgamation of Artificial Intelligence (AI) methods and the Internet of Things (IoT) infrastructure, which are deployed there to improve the overall performance of the system. AIoT can be deployed to achieve more efficient IoT operations; thereby can improve human-machine interactions and provide better data analysis. AI methods can be used to transform IoT data into useful information for the better decision-making processes, and it further increases the overall usability of the system. AIoT frameworks are very useful and applicable in a variety of applications, like security and surveillance system, smart home, intelligent transportation system, smart farming, secure and safe healthcare monitoring, industrial automation and control, eCommerce, logistics operations and control, and many more. However, AIoT frameworks may have issues related to data security and privacy as they are vulnerable to various types of information security-related attacks. These issues further cause the serious consequences, like the unauthorized data leakage and data update. Blockchain is a specific type of database. It is a digital ledger of transactions, which is duplicated and distributed across the entire network of computer systems. It stores data in the form of some blocks, which are then chained together. Blockchain is tamper proof and provides more security as compared to the traditional security mechanisms. Hence, blockchain can be integrated in various AIoT applications to provide more security. A generalized blockchain-envisioned secure authentication framework for AIoT has been proposed. The adversary model of blockchain-envisioned secure authentication framework for AIoT is also highlighted that covers most of the potential threats of a kind of communication environment. Various applications of the proposed framework are also discussed. Furthermore, different issues and challenges of the proposed framework are highlighted. In the end, we also provide some future research directions relevant to the proposed framework.
Heralding on the detection of zero photons

Although heralding signals in quantum optics experiments are typically based on the detection of exactly one photon, it has recently been theoretically shown that heralding based on the detection of zero photons can be useful in a number of quantum information applications. Here we describe an experimental technique for "heralding on zero photons" using conventional single-photon detectors and time-tagging methods. We find that detector efficiency and dark count rates play a counterintuitive role in the ability to accurately detect zero photons, and demonstrate these effects in an experiment that involves heralding on zero photons in the well-known Hong-Ou-Mandel interferometer.