Reviewerly/exHarmony huggingface dataset:
When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey's 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score < 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret.
HGSmark: An efficient ECG watermarking scheme using hunger games search and Bayesian regularization BPNN

The extensive use of telemedicine has led to an exponential increase in the exchange of medical data via insecure networks. For example, diagnosis in telemedicine practice often requires electrocardiograms to be shared via Internet. This raises security and authenticity concerns. In this paper, we have proposed HGSmark—an ECG signal watermarking scheme based on multiple embedding strength (MES). We have used the hunger games search (HGS) algorithm to optimize the MES values locally, while maintaining the imperceptibility-robustness trade-off. However, as the optimization using metaheuristic techniques is time-intensive, we have used HGS only once to train a Bayesian regularization-backpropagation neural network (BR-BPNN) on a set of ECG signals. Subsequently, the trained BR-BPNN is employed to quickly compute the optimum MES values. For watermarking, we have employed a wrapping-based fast discrete curvelet transform (FDCT) and singular value decomposition (SVD) to extract the principal component (PC) coefficients of the ECG signal, and embedded a watermark into PC coefficients using the MES values. We have used PC coefficients for watermarking in order to avoid the problem of false-positive. Further, to enhance security, we have encoded the watermark using the logistic chaotic map. We evaluated the performance of HGSmark on MIT-BIH normal sinus rhythm and MIT-BIH arrhythmia databases. The experimental results show that HGSmark achieved average PSNR and PRD values of 57.725 dB and 0.271, respectively and is robust to common interferences. Moreover, the comparison with the state-of-the-art ECG signal watermarking schemes establishes that HGSmark is superior to others in terms of imperceptibility and robustness.
Chaotic enhanced teaching-based differential evolution algorithm applied to discrete truss optimization

In this paper, we develop a discrete chaotic enhanced hybrid teaching-based differential evolution algorithm (CETDE) that can deal with realistic yet challenging constrained discrete truss design problems more efficiently. CETDE adaptively executes multiple mutation operators to quickly search for the solution, but at the same time, premature convergence, a common issue in discrete optimization, is also prevented by embedding the chaotic logistic rule into the mutations. An improved logical strategy that only allows potential mutants to go through structural analysis is introduced to reduce computation. An enhanced chaotic local search strategy and a one-component change technique are also developed for further efficiency. Besides, CETDE simultaneously keeps continuous and discrete population throughout the evolution, which is distinguishable from existing discrete optimizers, to preserve the stochastic nature. Compared to other state-of-the-art performers in the literature, CETDE illustrates both higher efficiency and solution optimality.
A Pattern Language for Blockchain Governance

Blockchain technology has been used to build next-generation applications taking advantage of its decentralised nature. Nevertheless, there are some serious concerns about the trustworthiness of blockchain due to the vulnerabilities in on-chain algorithmic mechanisms, and tedious disputes and debates in off-chain communities. Accordingly, blockchain governance has received great attention for improving the trustworthiness of all decisions that direct a blockchain platform. However, there is a lack of systematic knowledge to guide practitioners to perform blockchain governance. We have performed a systematic literature review to understand the state-of-the-art of blockchain governance. We identify the lifecycle stages of a blockchain platform, and present 14 architectural patterns for blockchain governance in this study. This pattern language can provide guidance for the effective use of patterns for blockchain governance in practice, and support the architecture design of governance-driven blockchain systems.
Hand Gesture Recognition using DenseNet201-Mediapipe Hybrid Modelling

Understanding hand gestures has been crucial to mankind since the dawn of time, due to various purposeful reasons contemporaneous with other things of the same era. The recognition of hand gestures is greatly applied for the understanding of sign languages, which serves as the primary mode of communications for deaf and dumb individuals. Recognition of universal and customised hand gestures also bridges the gap between those beings who are well versed with sign language and individuals who are incapable of understanding it. Apart from this, understanding of hand gestures has vast applications in the fields of automation, virtual reality, provision of a touchless user interface and various such modes of Human Computer Interactions. In this paper we have proposed a hybrid architecture for Hand Gesture Recognition involving Mediapipe for the detection and tracking of the hand along with various neural network architectures for training and classifying the hand gestures. A sub-sample of the publicly available HaGRID (Hand Gesture Recognition) dataset has been considered that involves eighteen hand gestures and a no-gesture category. A gesture classification model has been created by making use of transfer learning techniques on DenseNet201 deep neural network architecture which gave a validation loss of 0.115 and validation accuracy of 97.55%.
Performance Comparison of Various Wireless Sensor Network Dataset using Deep Learning Classifications

Deep Learning is the subset of artificial intelligence and various techniques are available to predict the performance of real time applications. Wireless devices are available to access the devices from multiple places based environment and coverage area. Wireless sensor network (WSN) tools are positioned in multiple places which has sense the information from the substances. The performance is the major factor to facilitate the bandwidth and power. In this paper to analyse the wireless sensor network performance using deep learning techniques. Here to measure the performance indicators such as accuracy, precision and score function of sensor dataset using TensorFlow. The deep learning models such as convolutional neural networks, recurrent neural network and k-means neural network performance factors are classified by using sensory dataset. The accuracy factor is obtained as 96% and compares this with existing models.
What Do Audio Transformers Hear? Probing Their Representations For Language Delivery &amp; Structure

Transformer models across multiple domains such as natural language processing and speech form an unavoidable part of the tech stack of practitioners and researchers alike. Au-dio transformers that exploit representational learning to train on unlabeled speech have recently been used for tasks from speaker verification to discourse-coherence with much success. However, little is known about what these models learn and represent in the high-dimensional latent space. In this paper, we interpret two such recent state-of-the-art models, wav2vec2.0 and Mockingjay, on linguistic and acoustic features. We probe each of their layers to understand what it is learning and at the same time, we draw a distinction between the two models. By comparing their performance across a wide variety of settings including native, non-native, read and spontaneous speeches, we also show how much these models are able to learn transferable features. Our results show that the models are capable of significantly capturing a wide range of characteristics such as audio, fluency, supraseg-mental pronunciation, and even syntactic and semantic text-based characteristics. For each category of characteristics, we identify a learning pattern for each framework and conclude which model and which layer of that model is better for a specific category of feature to choose for feature extraction for downstream tasks.
MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities

Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software codes represented by heterogeneous contract graphs built upon both control-flow graphs and call graphs containing different types of nodes and links. We propose MANDO, a new heterogeneous graph representation to learn such heterogeneous contract graphs’ structures. MANDO extracts customized meta-paths, which compose relational connections between different types of nodes and their neighbors. Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection. Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. More importantly, it is the first learning-based approach capable of identifying vulnerabilities at the fine-grained line-level, and significantly improves the traditional code analysis-based vulnerability detection approaches by 11.35% to 70.81% in terms of F1-score.
Gamified Learning and Assessment Using ARCS with Next-Generation AIoMT Integrated 3D Animation and Virtual Reality Simulation

In higher education institutions (HEI), particularly in biology and medical education, the use of 3D animation, virtual reality, and simulation offers great potential in terms of enhancing learning and engaging students. Higher education researchers are still investigating virtual reality’s possibilities and outcomes in various fields. This study focuses on the effects of 3D gamification using an Artificial Intelligence integrated Internet of Medical Things (AIoMT) implemented with virtual reality application for biology and medical students to learn about the human brain. Nowadays, both theoretical and practical education frequently incorporate virtual reality and augmented reality. Virtual tours of the human body’s systems are offered to biology students so that they may comprehend such systems’ functions. This study focuses on the use of 3D animation, virtual reality, and simulation in medical education, with a specific focus on the effects of a 3D gamification app using the Internet of Medical Things (AIoMT) on medical professionals’ passion for learning. This study uses the ARCS model and SEM analysis to examine the impact of virtual reality on students’ motivation and learning. The results show that virtual reality positively impacts motivation and the understanding of the concept-to-execution process through practice and simulation-based training. To assess how well students are learning, what they are analyzing, and how well they can understand the objects of analysis, a 3D-simulation-based and user-feedback-based design has been developed using the proposed research methodology. According to this article’s findings, a smartphone app that uses virtual reality can help medical professionals better understand the concept-to-execution process through practice. VR simulation-based training, as well as Biology teachers or medical colleges, can offer high-definition 3D VR models rather than organs in jars to understand the human anatomy and its functions more experientially and effectively.
Three-Party Stochastic Evolutionary Game Analysis of Supply Chain Finance Based on Blockchain Technology

In the process of accounts receivable financing under supply chain finance, the phenomenon of accounts receivable forgery and default have caused great pressure on the supervision of financial institutions. We consider the integration of blockchain technology with a supply chain finance platform around the fraudulent default phenomenon in supply chain finance receivables financing and construct a three-party stochastic evolutionary game model among financial institutions, core enterprises, and Micro, Small, and Medium Enterprises (MSMEs). Firstly, we use Ito^’s stochastic differential equation theory to analyze the conditions for the stability of the behavior of game subjects. Secondly, we use numerical simulations to quantitatively analyze the impact of the regulatory strength of financial institutions, the information sharing of the blockchain platform, and the change of incentive parameters on the strategy choice of game subjects. Through the above analysis, we conclude that the information-sharing incentive coefficient promotes financial institutions to choose to connect to the blockchain platform, and the information-sharing risk coefficient and the regulatory intensity have the opposite effect on the blockchain platform construction. Meanwhile, the allocation of incentive shares has a significant influence on the core enterprises. Finally, we give priorities and directions for adjusting the relevant parameters to provide recommendations for financial institutions to regulate the financing process more effectively.
Feature relevance XAI in anomaly detection: Reviewing approaches and challenges

With complexity of artificial intelligence systems increasing continuously in past years, studies to explain these complex systems have grown in popularity. While much work has focused on explaining artificial intelligence systems in popular domains such as classification and regression, explanations in the area of anomaly detection have only recently received increasing attention from researchers. In particular, explaining singular model decisions of a complex anomaly detector by highlighting which inputs were responsible for a decision, commonly referred to as local post-hoc feature relevance, has lately been studied by several authors. In this paper, we systematically structure these works based on their access to training data and the anomaly detection model, and provide a detailed overview of their operation in the anomaly detection domain. We demonstrate their performance and highlight their limitations in multiple experimental showcases, discussing current challenges and opportunities for future work in feature relevance XAI for anomaly detection.
Financial innovation and bank financial performance: Evidence from Nigerian deposit money banks

Since the global financial crisis of 2008, there has been increasing attention on the impact of financial innovation (FI) on the financial services industry. Despite wide consensus on the benefits of innovation to the real economy, the crisis made FI a focus of re-evaluation and re-conceptualization. FI can be accomplished through the deployment of a branchless banking system and technological adoption. The objective of this study is to investigate the impact of mobile, internet, and automated teller machines (ATMs) on banks’ financial performance. Schumpeter’s Theory of Innovation Diffusion and constraint-induced financial innovation theories underpin this study. Utilizing data for the 2012 to 2021 period because of data availability, this study considers the causal effect of innovation on commercial bank performance via Granger causality test. The entire 24 deposit money banks in Nigeria constitute the study’s population. Secondary data were gathered over the study period from NDIC annual reports, the Nigeria Inter-Bank Settlement System (NIBSS), and the Central Bank of Nigeria statistical bulletins (2012-2021). Based on the ARDL model analysis, POS banking service has the greatest impact on deposit money bank performance because of large volume and value of transaction witnessed in the banking sector. Thus, more mobile and e-banking services should be made available. The usage of ATMs, mobile banking, credit and debit cards, online banking, and agency banking have a positive short run and long run substantial effect on deposit money bank performance in Nigeria, except National Electronic Fund Transfer (NEFT) and NIBSS Instant Payments (NIP) according to empirical results.
ISE-Hate: A benchmark corpus for inter-faith, sectarian, and ethnic hatred detection on social media in Urdu

Social media has become the most popular platform for free speech. This freedom of speech has given opportunities to the oppressed to raise their voice against injustices, but on the other hand, this has led to a disturbing trend of spreading hateful content of various kinds. Pakistan has been dealing with the issue of sectarian and ethnic violence for the last three decades and now due to freedom of speech, there is a growing trend of disturbing content about religion, sect, and ethnicity on social media. This necessitates the need for an automated system for the detection of controversial content on social media in Urdu which is the national language of Pakistan. The biggest hurdle that has thwarted the Urdu language processing is the scarcity of language resources, annotated datasets, and pretrained language models. In this study, we have addressed the problem of detecting Interfaith, Sectarian, and Ethnic hatred on social media in Urdu language using machine learning and deep learning techniques. In particular, we have: (1) developed and presented guidelines for annotating Urdu text with appropriate labels for two levels of classification, (2) developed a large dataset of 21,759 tweets using the developed guidelines and made it publicly available, and (3) conducted experiments to compare the performance of eight supervised machine learning and deep learning techniques, for the automated identification of hateful content. In the first step, experiments are performed for the hateful content detection as a binary classification task, and in the second step, the classification of Interfaith, Sectarian and Ethnic hatred detection is performed as a multiclass classification task. Overall, Bidirectional Encoder Representation from Transformers (BERT) proved to be the most effective technique for hateful content identification in Urdu tweets.
Presenting Job Instructions Using an Augmented Reality Device, a Printed Manual, and a Video Display for Assembly and Disassembly Tasks: What Are the Differences?

Components assembly and disassembly are fundamental tasks in manufacturing and the product service industry. Job instructions are required for novice and inexperienced workers to perform such tasks. Conventionally, job instructions may be presented via printed manual and video display. Augmented reality (AR) device has been one of the recent alternatives in conveying such information. This research compared the presentation of job instruction via AR display, video display, and a printed manual in performing computer component assembly and disassembly tasks in terms of efficiency, quality, and usability. A Microsoft® HoloLens 2 device and a laptop computer were adopted to present the job instruction for the AR and video conditions, respectively. A total of 21 healthy adults, including 11 males and 10 females, participated in the study. Our findings were that AR display led to the least efficiency but the best quality of the task being performed. The differences of the overall usability scores among the three job instruction types were insignificant. The participants felt that support from a technical person for the AR device was significantly more than the printed manual. More male participants felt the AR display was easier to use than their female counterparts.
Invertible mask network for face privacy preservation

Face privacy Preservation is research subject has attracted significant interest. Existing face privacy-preserving methods aim at erasing the semantic information of faces; however, they cannot well preserve the reusability of original facial information. To achieve the naturalness of the processed face and the recoverability of the original protected face, this paper proposes a face privacy-preserving method based on Invertible Mask Network (IMN). First, a high-definition mask face is generated by the proposed Mask Generator model. Then, the mask face is put onto a protected face to obtain the masked face, in which the masked face is visually indistinguishable from the mask face. Finally, the mask face can be removed from the masked face and the recovered face can be obtained by authorized users, where the recovered face is visually indistinguishable from the protected face. The experimental results show that the proposed method can effectively protect the privacy of the protected face in subjective perception, the protection success rate is very high by the face recognition tool, and the proposed method can almost perfectly recover the protected face from the masked face.