Reviewerly/exHarmony huggingface dataset:
Extracting Classification Boolean Rules with Probabilities from Neural Networks

&lt;p&gt;For classification problems, neural networks are well known for high accuracy in comparison to traditional statistical methods such as logistic regression and discriminant analysis. It is even better than other algorithms such as decision trees and Bayesian networks. However, the knowledge learned by the neural networks is stored in the hierarchical functional mapping of the structures of neural networks and the weight and bias parameters. It is not easy for people to understand its black-box decision process. In this research, we extract probabilistic Boolean classification rules from neural networks. The ruleset model can be tuned to a specified sensitivity according to different thresholds. In addition, we can compute a weighted important factor for each attribute that composes the Boolean rules. The weighted important factor is a numeric number between 0 and 1. If the weighted important factor is 0, it means the corresponding attribute is a noise signal. Hence, the weighted important features can be filtered out with a given threshold.&lt;/p&gt; &lt;p&gt;From the linearly and nonlinearly separable simulation datasets, we find that the accuracy of PBCR1 and PBCR2 are better than neural networks even with a 1/10 training ratio. From UCI machine learning datasets, we find that the AUC of PBCR1 and PBCR2 will be a little lower than the AUC of neural networks. However, on the accuracy metric, from red wine and white wine datasets, PBCR1 and PBCR2 are almost the same with neural networks. The accuracies of PBCR1 and PBCR2 are superior to DT by a statistically significant margin. For the F1 score, PBCR1 and PBCR2 are statistically significantly better than DT on red wine, white wine, and PID datasets.&lt;/p&gt;
IMPLEMENTASI PEMBELAJARAN PADA PEMBELAJARAN BAHASA INDONESIA DENGAN TEMA KEGEMARAN KELAS 3 SDN PORIS PELAWAD 5 KOTA TANGERANG

This article is a theoretical study of the implementation of learning in Indonesian language learning with a favorite theme in class III students at SDN Poris Pelawad 5. This study aims to determine the application of the interaction process of students with educators and learning resources in a learning environment that includes teachers and students who interact with each other. exchanging information in class III SDN Poris Pelawad 5. Implementation is everything that is implemented and implemented according to a program that is designed to be fully implemented in accordance with established regulations. With implementation it aims to achieve a mature plan, both individually and as a team and check and document procedures in implementing plans or policies. Therefore, and we must also know what learning strategies are applied, prospective educators / educators must learn. The goal is so that prospective educators/educators know the basic scientific components that need to be considered and considered in order to develop effective and efficient learning strategies. basics, and types of learning strategies. The results of the study show that (1) What learning strategies are used in Indonesian subjects with a favorite theme in class III at SDN Poris Pelawad 5, (2) How does the teacher communicate with his students, (3) Conditions that must be considered in carrying out learning effective, (4) Identify different learning and communication styles.
Trace metals in the topsoil of Kandahar region, Afghanistan and their associated health and ecological risk

In urban regions, surface dust and topsoil can be used as indicators of metal pollution. Numerous studies have been conducted in different locations throughout the world to assess metal pollution and associated environmental and human health risks, but none have been conducted in Afghanistan's Kandahar region. The purpose of this study is to examine the environmental and health risks of metal concentrations (As, Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) in topsoil samples of sampling site. The average metal concentrations in the topsoil slightly exceeded their corresponding background values in Upper Continental Crust (UCC). Top soil samples showed low contamination for Cd, Co, Cr, Cu, Ni, Pb, V, Zn and considerable to very high contamination for As. Pollution load indices (PLI) demonstrated that all sampling sites are at a high load of metal pollution (PLI &gt;1) indicating deterioration of soil quality. &amp;#160;According to Geo-accumulation Index (Igeo), topsoil samples fall under uncontaminated category for Co, Cr, Cu, Ni, Pb, V, Zn and moderately to heavily contaminated for Cd and heavily to extremely contaminated for As. Ecological risk values suggested that study area had low risk for Co, Cr, Cu, Ni, Pb, V, Zn while considerable to high risk for Cd and high to very high risk for As. According to the health risk assessment, ingestion is the major pathway for non-carcinogenic risk and children are at more risk as compared to adults. Fall outs of high ammunition usage, wear and tear of heavy duty vehicles and fossil fuel consumption seems to be responsible for the high levels of metals in topsoil of Kandahar region. More such studies need to be conducted with more spatial coverage to identify contaminated areas and subsequent remediation to safeguard human health and ecosystem.
Tuning-free and self-supervised image enhancement against ill exposure

Complex lighting conditions and the limited dynamic range of imaging devices result in captured images with ill exposure and information loss. Existing image enhancement methods based on histogram equalization, Retinex-inspired decomposition model, and deep learning suffer from manual tuning or poor generalization. In this work, we report an image enhancement method against ill exposure with self-supervised learning, enabling tuning-free correction. First, a dual illumination estimation network is constructed to estimate the illumination for under- and over-exposed areas. Thus, we get the corresponding intermediate corrected images. Second, given the intermediate corrected images with different best-exposed areas, Mertens’ multi-exposure fusion strategy is utilized to fuse the intermediate corrected images to acquire a well-exposed image. The correction-fusion manner allows adaptive dealing with various types of ill-exposed images. Finally, the self-supervised learning strategy is studied which learns global histogram adjustment for better generalization. Compared to training on paired datasets, we only need ill-exposed images. This is crucial in cases where paired data is inaccessible or less than perfect. Experiments show that our method can reveal more details with better visual perception than other state-of-the-art methods. Furthermore, the weighted average scores of image naturalness matrics NIQE and BRISQUE, and contrast matrics CEIQ and NSS on five real-world image datasets are boosted by 7%, 15%, 4%, and 2%, respectively, over the recent exposure correction method.
mHealth Systems Need a Privacy-by-Design Approach: Commentary on “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review” (Preprint)

<sec> <title>UNSTRUCTURED</title> Brauneck and colleagues have combined technical and legal perspectives in their timely and valuable paper “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review.” Researchers who design mobile health (mHealth) systems must adopt the same privacy-by-design approach that privacy regulations (eg, General Data Protection Regulation) do. In order to do this successfully, we will have to overcome implementation challenges in privacy-enhancing technologies such as differential privacy. We will also have to pay close attention to emerging technologies such as private synthetic data generation. </sec>
A Self-Error-Correction-Based Reversible Watermarking Scheme for Vector Maps

The existing digital watermarking schemes for vector maps focus mainly on the process of watermark embedding, while few works have been conducted on the topic of the self-optimization of watermark data in the process of watermark detection. There is thus still much room for accuracy improvement in watermark detection. In this paper, a model of mixed watermark data construction is built first. It constructs the error-correction codes and checking code of the original copyright watermark data and combines them to generate the final watermark data. Additionally, a lossless compression algorithm is designed for watermark data to constrain the total watermark length. Based on the constructed model, a self-error-correction-based reversible watermarking scheme is put forward for vector maps. In this scheme, map vertices are divided into non-intersecting groups first according to stability, and mixed watermark data are then embedded with respective vertex groups. Simulation results demonstrate that the watermark capacity of this scheme is 1.0000, the coordinate error caused by the watermark embedding process can be limited to no more than 0.00001 when the strength of watermark embedding is set to five, and several watermark bits can be effectively detected and corrected after watermark extraction. Experimental results and analysis show that it can strike a good balance among reversibility, invisibility, capacity and robustness. It can provide a novel solution to improve the watermark detection accuracy of digital watermarking schemes for vector maps.
Testing the Effectiveness of the Diagnostic Probing Paradigm on Italian Treebanks

The outstanding performance recently reached by neural language models (NLMs) across many natural language processing (NLP) tasks has steered the debate towards understanding whether NLMs implicitly learn linguistic competence. Probes, i.e., supervised models trained using NLM representations to predict linguistic properties, are frequently adopted to investigate this issue. However, it is still questioned if probing classification tasks really enable such investigation or if they simply hint at surface patterns in the data. This work contributes to this debate by presenting an approach to assessing the effectiveness of a suite of probing tasks aimed at testing the linguistic knowledge implicitly encoded by one of the most prominent NLMs, BERT. To this aim, we compared the performance of probes when predicting gold and automatically altered values of a set of linguistic features. Our experiments were performed on Italian and were evaluated across BERT’s layers and for sentences with different lengths. As a general result, we observed higher performance in the prediction of gold values, thus suggesting that the probing model is sensitive to the distortion of feature values. However, our experiments also showed that the length of a sentence is a highly influential factor that is able to confound the probing model’s predictions.
Impact of Covid-19 on Higher Education: Challenges and Responses as Perceived by Students: The Puntland-Somalia Context

The main objective of the study is to investigate the impact of Covid-19 on higher education: challenges and responses in Puntland-Somalia. This study was conducted through analytical survey design by using both quantitative and qualitative research methods with two parts: A which consists of a questionnaire for collecting data from selected student respondents. The study utilised a convenience sampling method to select respondents. The study utilised frequencies and percentages; tables were used in the presentation of data. In addition to that, data collection was analysed using themes and explanations while comparing the findings with the literature review part of the study. Findings reveal that universities worldwide are moving more and more towards online learning. Findings also reveal that apart from resources, staff readiness, student accessibility, and motivation play an important function in ICT-integrated learning. The inquiry has exposed the consequences of a new change in the scope of the teaching and learning environments of different universities in Puntland-Somalia. This is related to the perception of students to adapt to online education systems introduced by these Universities, as they both have access to the internet and technical resources. This sort of mindset and the current situation has greatly accelerated the digitalisation of the means of education. Likewise, related institutions as such are also showing their readiness to adapt to this teaching-learning methodology and contribute to the human resource development objectives of a country. This study suggests a number of recommendations; first, it is to organise training courses in online education methods for lecturers; the university’s management should provide constant monitoring of the satisfaction of students and lecturers of the online education organisation for the accumulation of statistical data in the dynamics
The Machine-Learning-Empowered Gesture Recognition Glove

Recently, gesture recognition technology has attracted increasing attention because it provides another means of information exchange in some special occasions, especially for auditory impaired individuals. At present, the fusion of sensor signals and artificial intelligence algorithms is the mainstream trend of gesture recognition technology. Therefore, this article designs a machine-learning-empowered gesture recognition glove. We fabricate a flexible strain sensor with a sandwich structure, which has high sensitivity and good cycle stability. After the sensors are configured in the knitted gloves, the smart gloves can respond to different gestures. Additionally, according to the representation characteristics and recognition targets of sampled signal data, we explore a segmented processing method of dynamic gesture recognition based on Logit Adaboost algorithm. After classification training, the recognition accuracy of smart gloves can reach 97%.
Blockchain and Smart Contracts in a Decentralized Health Infrastructure

The blockchain typically described as a decentralized system in which transactional or ancient statistics are recorded, stored, and maintained throughout a peer-to-peer community of personal computers referred to as nodes. Counterfeit drugs are one consequence of such limitations within existing supply chains, which not only has serious adverse impact on human health but also causes severe economic loss to the healthcare industry. Blockchain technology has gained tremendous attention, with an escalating hobby in a plethora of several applications like safe and relaxed healthcare records management. Similarly, blockchain is reforming the traditional healthcare practices to an extra reliable means, in phrases of powerful progno- sis and treatment through safe and cosy facts sharing using SHA Hash Generation Algorithm. Within the future, blockchain will be an era that can probably assist in personalized, authentic, and at ease healthcare by means of merging the entire actual-time scientific information of a patient’s fitness and offering it in an up to date cosy healthcare setup. In this paper, we evaluation each the present and modern day trends inside the subject of healthcare with the aid of imposing blockchain as a model. We also talk the packages of blockchain, at the side of the demanding situations confronted and destiny views. The proposed system executed blockchain implementation in distributed computing surroundings and it gives the automated restoration of invalid chain by using Consensus and Mining Algorithm. In this system, we present a Custom blockchain- based approach leveraging smart contracts and decentralized off-chain storage for efficient product traceability in the healthcare supply chain. The smart contract guarantees data provenance, eliminates the need for intermediaries and provides a secure, immutable history of transactions to all stakeholders. We present the system architecture and detailed algorithms that govern the working principles of our proposed solution. We perform testing and validation, and present cost and security analysis of the system to evaluate its effectiveness to enhance trace- ability within pharmaceutical supply chains. Keywords— Blockchain Technology, Decentralization / Decentralized System, Distributed Com- puting, Peer-to- Peer Network, Healthcare, Supply chains, etc.
Neural Topic Model with Distance Awareness

Abstract Neural topic models (NTM) have shown their success in topic modelling with awide range of applications in text analysis. NTMs based on generative modelstend to have good refactoring capabilities in achieving good document represen-tations, rather than providing a topic representation that has an understandableassociation with documents and words. To bridge this gap, we have proposeda neural topic model that allows simultaneously learning topics from text dataas well as capturing the structural manifold in the original data space of doc-uments. Comprehensive experiments have shown that the proposed model canimprove learned topics in terms of intrinsic metrics (purity and coherence)and extrinsic downstream tasks (clustering, classification). Beyond that, themodel can provide interpretable low dimensional presentations of documents
Distributed Swarm Collision Avoidance Based on Angular Calculations

Collision avoidance is one of the most important topics in the robotics field. The goal is to move the robots from initial locations to target locations such that they follow shortest non-colliding paths in the shortest time and with the least amount of energy. In this paper, a distributed and real-time algorithm for dense and complex 2D and 3D environments is proposed. This algorithm uses angular calculations to select the optimal direction for the movement of each robot and it has been shown that these separate calculations lead to a form of cooperative behavior among agents. We evaluated the proposed approach on various simulation and experimental scenarios and compared the results with FMP and ORCA, two important algorithms in this field. The results show that the proposed approach is at least 25% faster than ORCA and at least 7% faster than FMP and also more reliable than both methods. The proposed method is shown to enable fully autonomous navigation of a swarm of crazyflies.
Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Abstract A major challenge is when datasets are released to utilize in the outside scope of data-collecting organizations, it is how to balance data utilities and data privacies. To achieve this aim in data collection (datasets), there are several privacy preservation models that have been proposed such as k-Anonymity and l-Diversity. Unfortunately, these privacy preservation models can be sufficient to address privacy violation issues in datasets that do not have high-dimensional attributes. For this reason, a privacy preservation model, LKC-Privacy, can address privacy violation issues in high-dimensional datasets to be proposed. With this privacy preservation model, datasets cannot have any concern of privacy violation issues when all L-size distinct quasi-identifier values are distorted (suppressed or generalized) to be at least K indistinguishable tuples. Moreover, every protected sensitive value relates to each group of indistinguishable quasi-identifier values, it must have the confidence of re-identifications to be at most C. Although LKC-Privacy is more efficient and effective than k-Anonymity and l-Diversity, it is generally efficient and effective to address privacy violation issues in location-based datasets. Moreover, we see that datasets satisfy LKC-Privacy constraints, they still have privacy violation issues from using data comparison attacks and they further have data utility issues that must be addressed. Therefore, a new privacy preservation model can address privacy violation issues in high-dimensional datasets such that its satiable released datasets do not have any concern of privacy violation issues from using data comparison attacks and are highly efficient and effective in data maintenance to be proposed in this work. Furthermore, we show that the proposed model is more efficient and effective by using extensive experiments.
Indian Plate stratigraphic units: On-line open-access database (Lexicon), including options to display sediment and volcanic facies onto plate reconstructions

Our international team has worked with regional experts to compile details on nearly every geologic formation of the Indian Plate from Proterozoic through Quaternary. This suite of nearly 1000 sedimentary and volcanic formations includes India, Pakistan, Nepal, Bhutan, Sri Lanka, Bangladesh and Myanmar, plus all offshore sedimentary basins that have been explored for hydrocarbons. The data for each formation includes its lithologic succession, fossil content, age&amp;#173; span details, regional extent and images of stratigraphic columns/outcrops/etc. (when available). The geologic age spans (as percent-up in stages) are auto-converted to numerical ages using a look-up table (currently Geologic Time Scale 2020), and the regional extents include GeoJSON coding. APIs provide open access to this information for other applications.The user can explore this information using a map interface, stratigraphic-column interfaces (generated with TimeScale Creator software), and a variety of search filters (including and/or logic for words within lithologic descriptions). An additional option on the displayed output is to display either a single formation GeoJSON or an age-suite of formation GeoJSONs as facies&amp;#173; pattern-filled polygons onto a user-selected plate reconstruction model for that appropriate age via a one-click activation of pyGPlates-pyGMT graphics.This cloud-based database and website (currently at indplex.geolex.org/index.php) will be hosted and maintained by the Geologic Society of India in coordination with colleagues in Pakistan and other nations. Our online Indian Plate database currently links to adjacent regional lexicons of China and Indochina for regional paleogeographic displays, and is intended to share age-facies&amp;#173; location information with stratigraphic databases of Australia, MacroStrat, One-Stratigraphy, etc., within the lUGS Deep-Time Digital Earth system, to generate paleogeographic visualizations of any desired geologic age onto any plate-motion model.
Wavelet Estimation of a Density From Observations of Almost Periodically Correlated Process Under Positive Quadrant Dependence

In this paper, we construct a new wavelet estimator of density for the component of a finite mixture under positive quadrant dependence. Our sample is extracted from almost periodically correlated processes. To evaluate our estimator we will determine a convergence speed from an upper bound for the mean integrated squared error (MISE). Our result is compared to the independent case which provides an optimal convergence rate.