Reviewerly/exHarmony huggingface dataset:
A logic-based event controller for means-end reasoning in simulation environments

Simulation games are designed to cultivate expertise and rehearse particular skill sets. To yield longitudinal effects, sequences of events must be crafted to yield intended learning outcomes, sometimes by focusing on particularly difficult situations and replaying variants. The present paper develops a logic-based approach for encoding the interrelation between action, events, and objects in a manner that allows the resulting scenario description to immediately be executed in a game development environment. This has the dual effect of decoupling the description of a scenario from the simulation platform itself, as well as supporting iterative and flexible development of learning content. To this end, we provide three interrelated components: First, we develop a scenario description language based on Answer Set Programming. The language is designed to allow an automated reasoner to deduce a schedule of the future events that are caused by an action taken in a given simulation environment. Second, we define a protocol for exchanging actions and computed futures between, respectively, the simulation environment and the external automated reasoner. Finally, as a proof of concept, we develop an Application Programming Interface (API) for the Unity Real-Time Development Platform that implements the protocol and offers a software framework for connecting the computed future events to concrete game objects. This allows the game to evolve coherently from the specification. We argue that the resulting system inherits capabilities for artificial commonsense reasoning from its declarative basis which are useful for reasoning about an evolving emergency incident or training scenario.
Assessing global and local interest in eNaira CBDC and cryptocurrency information: implications for financial stability

Purpose This paper investigates the global and local interest in Internet information about cryptocurrency and the Nigeria central bank digital currency, which is also known as eNaira. Design/methodology/approach Granger causality test and GMM coefficient matrix methodologies were used. Findings There is sustained increase in global and local interest in Internet information about eNaira in the first six weeks after eNaira adoption. Local interest in Internet information about cryptocurrency in Nigeria exceeded global interest in Internet information about cryptocurrency. The south-east region had the highest interest in cryptocurrency information followed by the south-south, the north-central, the north-east, the north-west and the south-west regions. In contrast, the north-east region had the highest interest in Internet information about eNaira, followed by the north-west, the north–central, the south-west, the south-south and the south-east regions. Nigeria recorded the highest global interest in Internet information about cryptocurrency and eNaira, while Japan and Brazil recorded the lowest interest during the period. The correlation results show a significant and positive correlation between interest in cryptocurrency information and interest in eNaira information. The Granger causality results show that global interest in cryptocurrency information causes both global and local interest in eNaira information. Also, local interest in cryptocurrency information causes global interest in eNaira information. The GMM regression coefficient matrix shows a significant positive relationship between interest in cryptocurrency information and eNaira information. Originality/value There are few studies on CBDC in country-specific contexts. This study adds to the literature by examining the Nigerian context.
Perception of New Information Technology Rules Towards OTT Platforms

&#x0D; &#x0D; &#x0D; The last decade has witnessed a remarkable surge in the use of online applications, which have become a part &amp; parcel of everyone's life since the appearance of advanced cells. Today, internet use has expanded by leaps and bounds amongst the urban and suburban populations, and it has integrated itself as a fundamental way of life. The plethora of people using web-based media and especially since the last phase of over-the-top (OTT), the fear of information security has emerged drastically. The remarkable evolution in the use of the internet has brought along a multitude of security threats but the laws for the management of web activities are sparse and inadequate due to its constantly evolving nature. Explicitly in India, until the end, the 2000 information innovation demonstration was the main asset for managing Internet use. Although IT rules were added in 2011, they are now obsolete. The ongoing rivalry and series of conflicts between the Indian government and online media intermediaries such as Twitter, WhatsApp and social media prompted the development of the expanded IT rules, which are additional ground rules to be included in the Information Technology Act 2000. This article discusses the rationale for the presentation of expanded IT rules and what makes it a matter of concern for information security Vs unnecessary restrictions.&#x0D; &#x0D; &#x0D;
Personalized Explainable Recommendation based on BERT

With the proliferation of text information on the Internet, the rapid evolution of artificial intelligence, and the wide application of machine learning and deep learning, text emotion analysis has been widely concerned by the academic community. Personalized recommendation system has emerged as the circumstances demanded, and has quickly gained interest in both industry and academic circles. It has gradually become an extremely important part of people's life and work in many fields, such as e-commerce, short video content, take-out service, online advertising push and so on. First, the Chinese comment text of an item is analyzed in this paper. Since the absolute standard of binary text sentiment analysis cannot meet the need of the recommendation system to recommend the item to users for interpretation, a ternary text sentiment analysis method based on BERT model is used to combine with the idiosyncrasies of text data is proposed to solve the problems of poor Chinese text representation, low exactitude, and inability to precisely comprehend the semantic information expressed in the text, which are caused by polysemy of Chinese version. The proposed method can generate interpretable recommendations for items that users are interested in. The relevant properties of the text are captured by the Transformer encoder in the BERT model, meanwhile the attention framework is used to weight the information recovered from the pattern to highlight the hinge information in the comment on the text. Second, the SoftMax function is used for categorizing the text aspect data of users' reviews of items, as well as finally the recommendation system recommends interested items to users and produces emotional and coloration reasons for recommendation that are accorded with users' justifications. The method is applied to real datasets, and the results show that text breakdown effect has been achieved, which greatly improves the interpretability of recommendation system, which is more in line with users' ideas.
Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks’ machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.
Survival of Illegal Fentanyl Sales in the Twittersphere and Tumblr Sphere: A Cross-Sectional Forensics Approach

Social media offer valuable knowledge about opioid narcotics' risks but are also commonly used to sell them.To determine how drug dealers avoid social media surveillance, the cross-sectional study used fentanyl-related data from Twittersphere and Tumblrsphere.The three-stagee process with data collection, preprocessing, and forensic tracking of fentanyl sales and promotion was performed.Twitter data were consistent between January and June 2021, while Tumblr data were more up-to-date.From January to June 2021, 12189 tweets and, from January 2020 to July 2021, 1240, Tumblr posts related to fentanyl were gathered.A higher proportion of Tumblr data (192, 15.56%) was classified as fentanyl sales and promotion, compared with Twitter data (1, 0.01%).Twitter has been successful in limiting the sale of opioids.However, more attention should be paid to non-English languages.Researchers, governments, and social media companies must collaborate to positively maintain social networking platforms to prevent the illicit sale of drugs through them.
Whether Use/Mention Is a Valid Distinction in Addressing the Problem of N-Word and The Possibility of Hidden Implicature

This paper focuses on the issue of the n-word, investigating whether there is a justified, proper scenario under which the taboo word can be uttered. Given the word’s negative impact on people’s psychological health, there is a loud call to prohibit it from appearing in TV shows, podcasts, radios, newspapers, and all forms of media. The word should simply never be uttered by anyone who is non-black, under all circumstances and contexts. Based on this situation, thinkers such as Kevin Cokley and John McWhorter try to analyse the problem and give their own theories in explaining and addressing the issue. Their arguments are presented in this paper. McWhorter’s use/mention distinction will be the centre of discussion. The distinction is specified and analysed in the argumentation section to examine whether it is a valid standard in addressing the issue. Opinions of other thinkers and objections to the use/mention distinction are also critically evaluated. The analysis and evaluation of the opinions refute the possibility that there is an implicature in the mention of n-word, which further illustrate the validity of the use/mention distinction. The paper ends by concluding that the use/mention distinction is a valid standard in addressing the issue of the n-word and it is unreasonable to think there are implicatures in the mention of n-word.
Predicting the Output of Solar Photovoltaic Panels in the Absence of Weather Data Using Only the Power Output of the Neighbouring Sites

There is an increasing need for capable models in the forecast of the output of solar photovoltaic panels. These models are vital for optimizing the performance and maintenance of PV systems. There is also a shortage of studies on forecasts of the output power of solar photovoltaics sites in the absence of meteorological data. Unlike common methods, this study explores numerous machine learning algorithms for forecasting the output of solar photovoltaic panels in the absence of weather data such as temperature, humidity and wind speed, which are often used when forecasting the output of solar PV panels. The considered models include Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) and Transformer. These models were used with the data collected from 50 different solar photo voltaic sites in South Korea, which consist of readings of the output of each of the sites collected at regular intervals. This study focuses on obtaining multistep forecasts for the multi-in multi-out, multi-in uni-out and uni-in uni-out settings. Detailed experimentation was carried out in each of these settings. Finally, for each of these settings and different lookback and forecast lengths, the best models were also identified.
Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods

There are known limitations in mobile omnidirectional camera systems with an equirectangular projection in the wild, such as momentum-caused object distortion within images, partial occlusion and the effects of environmental settings. The localization, instance segmentation and classification of traffic signs from image data is of significant importance to applications such as Traffic Sign Detection and Recognition (TSDR) and Advanced Driver Assistance Systems (ADAS). Works show the efficacy of using state-of-the-art deep pixel-wise methods for this task yet rely on the input of classical landscape image data, automatic camera focus and collection in ideal weather settings, which does not accurately represent the application of technologies in the wild. We present a new processing pipeline for extracting objects within omnidirectional images in the wild, with included demonstration in a Traffic Sign Detection and Recognition (TDSR) system. We compare Mask RCNN, Cascade RCNN, and Hybrid Task Cascade (HTC) methods, while testing RsNeXt 101, Swin-S and HRNetV2p backbones, with transfer learning for localization and instance segmentation. The results from our multinomial classification experiment show that using our proposed pipeline, given that a traffic sign is detected, there is above a 95% chance that it is classified correctly between 12 classes despite the limitations mentioned. Our results on the projected images should provide a path to use omnidirectional images with image processing to enable the full surrounding awareness from one image source.
On Bitcoin: A Study in Applied Metaphysics

Abstract This essay is dedicated to the memory of Katherine Hawley.1 Bitcoin was invented to serve as a digital currency that demands no trust in financial institutions, such as commercial and central banks. This paper discusses metaphysical aspects of bitcoin, in particular the view that bitcoin is socially constructed, non-concrete, and genuinely exists. If bitcoin is socially constructed, then one may worry that this reintroduces trust in the communities responsible for the social construction. Although we may have to rely on certain communities, I argue that social construction doesn’t imply a demand for trust because the relevant communities don’t take on any relevant commitments. Bitcoin is indeed trust-free.
Image matching algorithm based on improved AKAZE and Gaussian mixture model

Simultaneous localization and mapping (SLAM) technology has gained popularity as a result of technological advancements, and image matching—the labor-intensive and crucial component of SLAM technology—has emerged as a key area of research. Hence, the purpose of this study is to develop an image matching method based on improved accelerated-KAZE (AKAZE) and Gaussian mixed model (GMM) segmentation to guarantee the successful completion of subsequent vision challenges. (i) For feature extraction and feature description, first, the AKAZE algorithm was used to extract the key points of the gray-scale image, and second, the binary robust independent elementary features (BRIEF) algorithm was used to obtain the descriptors. Introduction of gray-scale prime method and convolution kernel function to enhance the robustness of features in the BRIEF calculation process. (ii) For feature matching, the gray-scale image is pseudocolored, and then all the pixels in its R channel are segmented by GMM, retaining the features in the same segmented region after brute-force matching. The experimental results demonstrate that, in comparison to oriented FAST and rotated BRIEF and AKAZE, the proposed algorithm in this work provides higher matching accuracy for images of different data sets. That is, the matching approach can be used more effectively for subsequent vision tasks.
Weighted Schatten p-norm and Laplacian scale mixture-based low-rank and sparse decomposition for foreground–background separation

Low-rank and sparse decomposition (LRSD) plays a vital role in foreground–background separation. The existing LRSD methods have the drawback: imprecise surrogate functions of rank and sparsity. We propose the weighted Schatten p-norm (WSN) and Laplacian scale mixture (LSM) method based on LRSD for foreground–background separation, which introduces the WSN and LSM to improve this drawback. To demonstrate the performance of the proposed method, it is applied to foreground–background separation and gets the highest average F-measure score.
Active feature elicitation: An unified framework

We consider the problem of active feature elicitation in which, given some examples with all the features (say, the full Electronic Health Record), and many examples with some of the features (say, demographics), the goal is to identify the set of examples on which more information (say, lab tests) need to be collected. The observation is that some set of features may be more expensive, personal or cumbersome to collect. We propose a classifier-independent, similarity metric-independent, general active learning approach which identifies examples that are dissimilar to the ones with the full set of data and acquire the complete set of features for these examples. Motivated by four real clinical tasks, our extensive evaluation demonstrates the effectiveness of this approach. To demonstrate the generalization capabilities of the proposed approach, we consider different divergence metrics and classifiers and present consistent results across the domains.
Scientific document processing: challenges for modern learning methods

Neural network models enjoy success on language tasks related to Web documents, including news and Wikipedia articles. However, the characteristics of scientific publications pose specific challenges that have yet to be satisfactorily addressed: the discourse structure of scientific documents crucial in scholarly document processing (SDP) tasks, the interconnected nature of scientific documents, and their multimodal nature. We survey modern neural network learning methods that tackle these challenges: those that can model discourse structure and their interconnectivity and use their multimodal nature. We also highlight efforts to collect large-scale datasets and tools developed to enable effective deep learning deployment for SDP. We conclude with a discussion on upcoming trends and recommend future directions for pursuing neural natural language processing approaches for SDP.
Subordinated Gaussian processes for solar irradiance

Abstract Traditionally the power grid has been a one‐way street with power flowing from large transmission‐connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by‐the‐second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non‐stationary and non‐Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non‐Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.
Revolutionizing the Modern Education through Digital Transformation: Challenges and Solutions

In today's world, digitalization is an instrument but apart from that, a way to survive that unlocks new opportunities such as knowledge without any time constraint with continuous education. The digital revolution necessitates integrating data and ways to improve a company's basic business operations to efficiently meet customer expectations. The entire academic fraternity is the target consumer in the educational sector. Digitalization in education is directly linked to a new way of doing things in a digital environment around the world, as well as the development of millennials who were born and taught in that milieu. This present study delves into the unique characteristics of digital education, as well as the current level of its implementation, expected outcomes, and related problems, hence a transformation study has demonstrated the fundamentals of e-education with its implementation in modern society. The study has critically evaluated the benefits and challenges with reference to current transformation and the effectiveness of this kind of teaching-learning process. The study is based on secondary research that has covered information regarding the benefits of employing electronic educational tools in teaching. The study concludes with some prominent solutions to make this transformation more visible and user friendly. The implication of the study suggests its benefit for all the stakeholders involved in the e-learning process transition right at the state, private or institutional bodies level for the policy frameworks, model designs, and dissemination through such digital models.&amp;nbsp;&lt;br&gt;