Reviewerly/exHarmony huggingface dataset:
Versatile modular neural locomotion control with fast learning

Legged robots have significant potential to operate in unstructured environments. The design of locomotion control is, however, still challenging. Currently, controllers must be manually designed for specific robots and tasks, or automatically designed via machine learning methods that require long training times and yield large opaque controllers. Drawing inspiration from animal locomotion, we propose a simple yet versatile modular neural control structure with fast learning. The key advantages of our approach are that behaviour-specific control modules can be added incrementally to obtain increasingly complex emergent locomotion behaviours, and that neural connections can be quickly and automatically learned. In a series of experiments, we show how eight modules can be quickly learned and added to a base control module to obtain emergent adaptive behaviours allowing a hexapod robot to navigate in complex environments. We also show that modules can be added and removed during operation without affecting the functionality of the remaining controller. Finally, the controller is successfully demonstrated on a physical robot. Taken together, our study reveals a significant step towards fast automatic design of versatile neural locomotion control. Controllers for robotic locomotion patterns deal with complex interactions and need to be carefully designed or extensively trained. Thor and Manoonpong present a modular approach for neural pattern generators that allows incremental and fast learning.
A Study of Deep Reinforcement Learning Based Recommender Systems

With the advancement in new technologies and a massive amount of data every day, various tools and models are being introduced to tackle such data. Such a branch of machine learning is reinforcement learning that works on the concept of reward and penalty. To make this model more efficient, a deep learning approach can be applied. To generate effectual recommendations, many machine learning algorithms are applied. We have consolidated a few papers that used deep reinforcement learning in the domain of recommender systems and presented the study in this paper.
Total SciComm: A Strategy for Communicating Open Science

This paper seeks to introduce a strategy of science communication: Total SciComm or all-out science communication. We proposed that to maximize the outreach and impact, scientists should use different media to communicate different aspects of science, from core ideas to methods. The paper uses an example of a debate surrounding a now-retracted article in the Nature journal, in which open data, preprints, social media, and blogs are being used for a meaningful scientific conversation. The case embodied the central idea of Total SciComm: the scientific community employs every medium to communicate scientific ideas and engages all scientists in the process.
DAH: Discrete Asymmetric Hashing for Efficient Cross-Media Retrieval

Given the merits in high computational efficiency and low storage cost, hashing techniques have been widely studied in cross-media retrieval. Existing methods usually adopt the equal length encoding scheme to represent the multimedia data. However, the strictly equal length scheme maybe not optimal because the dimension of different modalities is often various. Besides, there exists other challenges in designing a cross-media retrieval system, e.g., how to address the discrete constraints, how to avoid using the n*n similarity matrix, and how to effectively exploit the discriminative label information. To conquer the above challenges, we propose a novel method, i.e., discrete asymmetric hashing (DAH). Specifically, DAH exploits a flexible model, which can seamlessly deal with equal or unequal length encoding scenarios. Moreover, DAH constructs a supervised semantic embedding framework by jointly minimizing the distance-distance difference and label reconstructing error, significantly reducing the computational complexity. An asymmetric strategy is employed to establish the connection between hash codes and the latent subspace. Furthermore, the hash codes can be learned discretely by the designed optimization algorithm. In the training stage2, a semantic intersection scheme is proposed to learn more powerful hash functions. Experiments show that our DAH is effective in equal and unequal scenarios.
The Current State of Blockchain Applications in Supply Chain Management

In recent years, blockchain technology is expanding to new areas beyond finance, proving its use as an underlying technology for several application areas in supply chain management. There, the technology can be used to improve collaboration and transparency between supply chain partners. In this paper a systematic literature mapping is presented, which investigates the state of the art regarding blockchain-based applications in supply chain management. Identified applications are then analyzed regarding their industry sector, implemented blockchain framework and addressed challenges.
Data an Overview of Cybersecurity Threats on Credit Card Companies and Credit Card Risk Mitigation

As the increase of the demand on digital financial transactions, the higher the risks are associated with it. This paper examines the largest financial security incidents and then explains in detail some of the major incidents happened as reported by many sources. Then, the paper identified the risk mitigation for credit card companies' solutions by examining a research and some steps written by experts. Credit card security incidents are increasing, and its methods are rapidly evolving. Therefore, it is important for all credit card companies to consider these solutions and in order to mitigate risks and losses.
Repressive Legalism: How Postsecondary Administrators’ Responses to On-Campus Hate Speech Undermine a Focus on Inclusion

Guided by legal, sociolegal, and higher education concepts, we use an embedded case study of university administrators at a public institution to examine how they negotiate and institutionalize principles of freedom of expression and inclusion in responses to the proliferation of on-campus hate speech following the 2016 U.S. presidential election. Our findings reveal that an institution's legal context and administrators’ interpretations of law and law-related pressures shape their understanding of hate speech–related incidents, and the permissible responses, in ways that make it nearly impossible to consider and implement inclusion-focused practices. We advance the concept of “repressive legalism” to explain these dynamics and discuss implications for policies and practices that support both open, robust expression and inclusion for students of color.
A new Bayesian approach toward improved regression of low-count U Pb geochronology data generated by LA-ICPMS

UPb dating of hydrogenic and fossil material can be a significant challenge because samples often contain low U levels and variable amounts of non-radiogenic Pb. Line scans using LA-ICPMS provide the widest range of ratios available for defining the radiogenic to common Pb mixing line, hence the best age estimate, but extracting this information requires processing many low-count data. Conventional least-squares regression of ratios based on such data can give inconsistent results. A new approach is introduced where data are regressed as count rates in a 3D space rather than ratios on a 2D plot. This is a maximum likelihood approach in that mixing planes covering a range of ages and common 207Pb/206Pb ratios (PbC) are compared to the data set and used to determine a relative probability density surface whose maximum should fix the best-fit age and common 207Pb/206Pb ratio. 95% confidence errors are determined from T and PbC values that cut off 2.5% of the area to the left and right of 2D plots generated by integration of the 3D plot over PbC and age, respectively. Results from a rugose coral fossil show that this approach gives age results similar to regressions of concordia data and more accurate than Tera-Wasserburg regressions on data from natural samples. Tests on randomly generated synthetic data do not show significant discrepancy between Isoplot ages but they do between values of PbC. The 3D approach is the only one that gives accurate results for both age and PbC.
Asset market equilibria in cryptocurrency markets: Evidence from a study of privacy and non-privacy coins

This paper explores whether asset market equilibria in cryptocurrency markets do exist. In doing so, it distinguishes between privacy and non-privacy coins. Most recently, privacy coins have attracted increasing attention in the public debate as non-privacy cryptocurrencies, such as Bitcoin, do not satisfy some users’ demands for anonymity. Analyzing ten cryptocurrencies with the highest market capitalization in each submarket in the 2016–2018 periods, we find that privacy coins exhibit a distinct market equilibrium. Contributing to the current debate on the market efficiency of cryptocurrency markets, our findings provide evidence of market inefficiency. Moreover, the asset market equilibrium of privacy coins appears to originate from non-privacy coins with highest market capitalizations. We argue that the reason for this finding could be that non-privacy coins may be the first choice for criminals who might prefer cryptocurrencies exhibiting both a high level of anonymity and liquidity.
A Smart Comparative Analysis for Secure Electronic Websites

Online banking is an ideal method for conducting financial transactions such as e-commerce, e-banking, and e-payments.The growing popularity of online payment services and payroll systems, however, has opened new pathways for hackers to steal consumers' information and money, a risk which poses significant danger to the users of e-commerce and e-banking websites.This study uses the selection method of the entire e-commerce and e-banking website dataset (Chi-Squared, Gini index, and main learning algorithm).The results of the analysis suggest the identification and comparison of machine learning and deep learning algorithm performance on binary category labels (legal, fraudulent) between similar datasets, and understanding which function plays a vital role in predicting safe e-banking and e-commerce website datasets.The e-commerce and e-banking website dataset was compiled from the UCI machine learning library.We obtained 11,056 entries based on 30 unique website attributes.We used the machine learning algorithms support vector machine (SVM), k-nearest neighbors, random forest (RF), decision tree (DT), and the multilayer perceptron (MLP) deep learning algorithm to analyze the datasets of e-commerce and e-banking websites and found the best algorithms based on accuracy, precision, recall, and F1-measure.MLP had the highest precision at 97%.With this procedure we can now accurately test websites to assist in the early prediction of secure e-banking e-commerce transactions.
FAST-Hex—A Morphing Hexarotor: Design, Mechanical Implementation, Control and Experimental Validation

We present FAST-Hex, a micro aerial hexarotor platform that allows to seamlessly transit from an under-actuated to a fully-actuated configuration with only one additional control input, a motor that synchronously tilts all propellers. The FAST-Hex adapts its configuration between the more efficient but under-actuated, collinear multi-rotors and the less efficient, but full-pose-tracking, which is attained by non-collinear multi-rotors. On the basis of prior work on minimal input configurable micro aerial vehicle we mainly stress three aspects: mechanical design, motion control and experimental validation. Specifically, we present the lightweight mechanical structure of the FAST-Hex that allows it to only use one additional input to achieve configurability and full actuation in a vast state space. The motion controller receives as input any reference pose in $\mathbb{R}^3\times \mathrm{SO}(3)$ (3D position + 3D orientation). Full pose tracking is achieved if the reference pose is feasible with respect to actuator constraints. In case of unfeasibility a new feasible desired trajectory is generated online giving priority to the position tracking over the orientation tracking. Finally we present a large set of experimental results shading light on all aspects of the control and pose tracking of FAST-Hex.
Truthful Decentralized Blockchain Oracles

Summary Blockchain systems rely on oracles to bridge external information to the decentralized applications residing in the systems. Astraea protocols are decentralized oracle designs utilizing majority‐voting mechanism to determine the oracle outcomes and/or rewards to voters. However, the voters are indifferent between voting through a single or multiple identities, as the potential rewards by the decentralized oracles grow linearly with the voters stakes. Additionally, the majority‐voting mechanism may facilitate herd behaviors among the voters, as the voters are rewarded only if they are in agreement with the majority outcomes. In this paper, a novel oracle protocol is introduced by proposing a peer prediction‐based scoring scheme along with non‐linear staking rules, aiming at extracting subjective data truthfully. Specifically, an incentive compatible scoring scheme is designed so that voters uniquely maximize their expected score by honest reporting. The voters are rewarded when their report achieves a relatively high score compared to the rest of the voters, as opposed to the existing schemes, where a reward is only given when they agree to the majority. Furthermore, a non‐linear stake scaling rule is proposed to discourage Sybil attacks. Detailed simulation results are presented to show the operation of the proposed oracle protocol and its improvement compared to indicative mechanisms proposed in the existing literature.
English Grammar Error Detection Using Recurrent Neural Networks

Automatic marking of English compositions is a rapidly developing field in recent years. It has gradually replaced teachers’ manual reading and become an important tool to relieve the teaching burden. The existing literature shows that the error of verb consistency and the error of verb tense are the two types of grammatical errors with the highest error rate in English composition. Hence, the detection results of verb errors can reflect the practicability and effectiveness of an automatic reading system. This paper proposes an English verb’s grammar error detection algorithm based on the cyclic neural network. Since LSTM can effectively retain the valid information in the context during training, this paper decided to use LSTM to model the labeled training corpus. At the same time, how to convert the text information in English compositions into numerical values for subsequent calculation is also an important step in automatic reading. Most mainstream tools use the word bag model, i.e., each word is encoded according to the order of each word in the dictionary. Although this encoding method is simple and easy to use, it not only causes the vector to lose the sequence information of the text but also is prone to dimensional disaster. Therefore, word embedding model is adopted in this paper to encode the text, and the text information is sequentially mapped to a low-dimensional vector space. In this way, the position information of the text is not lost, and the dimensional disaster is avoided. The proposed work collects some corpus samples and compares the proposed algorithm with Jouku and Bingguo. The verification results show the superiority of the proposed algorithm in verb error detection.
Deep Fuzzy Rule-Based Classification System With Improved Wang–Mendel Method

Wang–Mendel (WM) fuzzy system is an effective and interpretable model for solving tabular data classification problem. However, original WM fuzzy system is weak in handling dataset with high dimensionality or large volume. Meanwhile, its capability of characterizing data is narrow, which results from lacking hierarchical transformation of features like deep learning-based models. In this article, we propose a deep fuzzy rule-based classification system (DFRBCS) based on improved WM method, in which fuzzy technique and deep learning strategy are combined to make a desirable tradeoff between model’s interpretability and prediction accuracy. We first redefine the consequent part of fuzzy rule in WM fuzzy system with class probability vector, which endows the improved WM fuzzy system with capacity for serving as building block of deep model. The model structure of DFRBCS is designed in layer-by-layer manner, where raw features can be transformed hierarchically. For every layer in DFRBCS, it contains many improved WM fuzzy systems whose input spaces are generated by shuffling and sliding window operation on concatenated outputs of fuzzy systems in previous layer. Comparative experiments are conducted on 45 real-world datasets with various sizes and dimensionality between our method, five baseline models, and the other deep fuzzy classifiers (D-TSK-FC, HID-TSK-FC, FCCI-TSK, DSA-FC, and MEEFIS). The experimental results show that DFRBCS is competitive in classification performance and promising in model’s interpretability.
HerBChain, a blockchain-based informative platform for quality assurance and quality control of herbal products

The use of medicinal plants is popular worldwide. Correct herbal authentication is of paramount importance to the safety and best interest of consumers. On the market, there is no comprehensive blockchain-based system to track the processes from plantation to manufacturing and to the sale. With the advancement of information technology, an open and transparent blockchain-based platform, HerBChain, was created to enhance the quality control of herbal products. The implementation of blockchain technology is to minimize the manipulation of recorded information. HerBChain is an information platform for recording the six important processes of herbal product manufacturing and marketing, which include plantation base, TCM processing factory, TCM manufacturer, testing laboratory, distributor and retailer. By duly recording the parameters and data essential for product quality in manufacturing and supply chain, the traceability and reliability of the products can be ensured.
The application of gradient evolution algorithm to an intuitionistic fuzzy neural network for forecasting medical cost of acute hepatitis treatment in Taiwan

Forecasting is important in the decision-making process. Therefore, among many forecasting algorithms that have been developed, fuzzy neural network (FNN) has been widely applied. The FNN structure is commonly trained using a back-propagation algorithm. However, this algorithm is sensitive to the initial weights and high computation, especially for complex problems. Thus, this study intended to overcome these drawbacks by applying the gradient evolution (GE) algorithm to train the intuitionistic FNN (IFNN). The proposed algorithm, GEIFNN, was verified using ten benchmark datasets. The results were compared with some other metaheuristic-based IFNN algorithms, such as genetic algorithm, particle swarm optimization algorithm, and differential evolution algorithm. The computational results show that the proposed GEIFNN relatively outperformed other tested algorithms, especially in training error. Furthermore, the proposed algorithm was applied to forecast medical costs for the treatment of acute hepatitis in Taiwan. The result also shows that GEIFNN can obtain the smallest training error.
Investigating the Key Persuasive Features for Fitness App Design and Extending the Persuasive System Design Model: A Qualitative Approach

Physical inactivity has been recognized as one of the leading risk factors that account for many non-communicable diseases, with the World Health Organization labeling it as the fourth leading risk factor for global mortality. This has led researchers and developers to create fitness apps to support and motivate people to engage in physical activity more regularly. However, there is limited research on how collectivist and individualist users from different social and cultural backgrounds differ in terms of the persuasive features they care about in fitness apps. Having knowledge of the cultural differences will help designers and developers create better fitness apps tailored to the two main types of culture. Hence, we conducted an empirical study to uncover how both cultures differ and the possibility of extending the Persuasive System Design (PSD) model. We found that Primary Task Support (Self-Monitoring and Goal-Setting) is requested more by the individualist group than the collectivist group. On the other hand, Dialog Support (Reminder and Suggestion) is requested more by the collectivist group than the individualist group. Finally, we found that the PSD model can be extended with Goal-Setting and Verbal Persuasion for fitness app design.
Markov features based DTCWS algorithm for online image forgery detection using ensemble classifier in the pandemic

The unexpected blast of the COVID has been found everywhere in the world, and everyone has been shocked unusually. In this critical situation, the number of online activities increases, whether related to online education, research, business meetings, virtual conferences, or virtual court. Under this pandemic situation, digital images are the only source of information that can be generally shared and visualized in virtual conferences and social media, and it's challenging to share the document for forgery detection. Today, it's straight forward to forge these images using image-editing software, and it's essential to detect image forgery for such images. In this paper, an efficient novel Discrete-Time Cosine Wavelet and Spatial (DTCWS) Markov feature-based algorithm has been designed for the detection of such forgery, especially for this pandemic situation. For this work, high-dimensional Markov features have been extracted in the DTCWS domain, and the dimensionality of these Markov features has been reduced with Principal Component Analysis (PCA). Furthermore, the co-occurrence matrix has increased the correlation among coefficients. For classification, an optimized ensemble classifier is used for evaluating the results instead of using a support vector machine classifier. Due to the time constraint in online activities, the proposed algorithm shows the best accuracy of 99.9% without taking too much time and fewer complexes compared to the current work.
New Insights into the Pulang Porphyry Copper Deposit in Southwest China: Indication of Alteration Minerals Detected Using ASTER and WorldView-3 Data

The Pulang porphyry copper deposit (PCD), one of the main potential areas for copper resource exploration in China, exhibits typical porphyry alteration zoning. However, further investigation of the indicative significance of alteration minerals, additional insight into metallogenic characteristics, and prospecting guidelines continue to be challenging. In this study, ASTER and WorldView-3 data were used to map hydrothermal alteration minerals by employing band ratios, principal component analysis, and spectrum-area techniques; and subsequently, the indication significance of alteration minerals was studied in-depth. The following new insights into the metallogenic structure and spatial distribution of alteration zoning in Pulang PCD were obtained and verified. (1) A new NE trending normal fault, passing through the northeast of Pulang PCD, was discovered. (2) Two mineralization alteration centers, exhibiting alteration zoning characteristics of potassic-silicified, phyllic, and propylitic zones from the inside to the outside, were observed on both sides of the fault. (3) At the junction of the redivided potassic-silicification and phyllic zones, favorable prospecting potential areas were delineated. This study shows that the spectral/multi-sensor satellite data are valuable and cost-effective tools for the preliminary stages of porphyry copper exploration in inaccessible and remote areas around the world.