Reviewerly/exHarmony huggingface dataset:
Critical Thinking About Explainable AI (XAI) for Rule-Based Fuzzy Systems

This article is about explainable artificial intelligence (XAI) for rule-based fuzzy systems [that can be expressed generically, as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$y({{\bf x}}) = f({{\bf x}})$</tex-math></inline-formula> ]. It explains why it is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not valid</i> to explain the output of Mamdani or Takagi–Sugeno–Kang rule-based fuzzy systems using IF-THEN rules, and why it <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">is valid</i> to explain the output of such rule-based fuzzy systems as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">association</i> of the compound antecedents of a small subset of the original larger set of rules, using a phrase such as “these linguistic antecedents are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">symptomatic</i> of this output.” Importantly, it provides a novel multi-step approach to obtain such a small subset of rules for three kinds of fuzzy systems, and illustrates it by means of a very comprehensive example. It also explains why the choice for antecedent membership function shapes may be more critical for XAI than before XAI, why linguistic approximation and similarity are essential for XAI, and, it provides a way to estimate the quality of the explanations.
Investigating Gender Bias in BERT

In this work, we analyze the gender bias induced by BERT in downstream tasks. We also propose solutions to reduce gender bias. Contextual language models (CLMs) have pushed the NLP benchmarks to a new height. It has become a new norm to utilize CLM-provided word embeddings in downstream tasks such as text classification. However, unless addressed, CLMs are prone to learn intrinsic gender bias in the dataset. As a result, predictions of downstream NLP models can vary noticeably by varying gender words, such as replacing "he" to "she", or even gender-neutral words. In this paper, we focus our analysis on a popular CLM, i.e., $$\text {BERT}$$ . We analyze the gender bias it induces in five downstream tasks related to emotion and sentiment intensity prediction. For each task, we train a simple regressor utilizing $$\text {BERT}$$ 's word embeddings. We then evaluate the gender bias in regressors using an equity evaluation corpus. Ideally and from the specific design, the models should discard gender informative features from the input. However, the results show a significant dependence of the system's predictions on gender-particular words and phrases. We claim that such biases can be reduced by removing gender-specific features from word embedding. Hence, for each layer in BERT, we identify directions that primarily encode gender information. The space formed by such directions is referred to as the gender subspace in the semantic space of word embeddings. We propose an algorithm that finds fine-grained gender directions, i.e., one primary direction for each BERT layer. This obviates the need of realizing gender subspace in multiple dimensions and prevents other crucial information from being omitted. Experiments show that removing embedding components in gender directions achieves great success in reducing BERT-induced bias in the downstream tasks. The investigation reveals significant gender bias a contextualized language model ( i.e., $$\text {BERT}$$ ) induces in downstream tasks. The proposed solution seems promising in reducing such biases.
Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm

With the rapid development of “We media” technology, netizens can freely express their opinions regarding enterprise products on a network platform. Consequently, online public opinion about enterprises has become a prominent issue. Negative comments posted by some netizens may trigger negative public opinion, which can have a significant impact on an enterprise’s image. From the perspective of helping enterprises deal with negative public opinion, this paper combines user portrait technology and a random forest algorithm to help enterprises identify high-risk users who have posted negative comments and thus may trigger negative public opinion. In this way, enterprises can monitor the public opinion of high-risk users to prevent negative public opinion events. Firstly, we crawled the information of users participating in discussions of product experience, and we constructed a portrait of enterprise public opinion users. Then, the characteristics of the portraits were quantified into indicators such as the user’s activity, the user’s influence, and the user’s emotional tendency, and the indicators were sorted. According to the order of the indicators, the users were divided into high-risk, moderate-risk, and low-risk categories. Next, a supervised high-risk user identification model for this classification was established, based on a random forest algorithm. In turn, the trained random forest identifier can be used to predict whether the authors of newly published public opinion information are high-risk users. Finally, a back propagation neural network algorithm was used to identify users and compared with the results of model recognition in this paper. The results showed that the average recognition accuracy of the back propagation neural network is only 72.33%, while the average recognition accuracy of the model constructed in this paper is as high as 98.49%, which verifies the feasibility and accuracy of the proposed random forest recognition method.
Multimodal sentimental analysis for social media applications: A comprehensive review

Abstract The analysis of sentiments is essential in identifying and classifying opinions regarding a source material that is, a product or service. The analysis of these sentiments finds a variety of applications like product reviews, opinion polls, movie reviews on YouTube, news video analysis, and health care applications including stress and depression analysis. The traditional approach of sentiment analysis which is based on text involves the collection of large textual data and different algorithms to extract the sentiment information from it. But multimodal sentimental analysis provides methods to carry out opinion analysis based on the combination of video, audio, and text which goes a way beyond the conventional text‐based sentimental analysis in understanding human behaviors. The remarkable increase in the use of social media provides a large collection of multimodal data that reflects the user's sentiment on certain aspects. This multimodal sentimental analysis approach helps in classifying the polarity (positive, negative, and neutral) of the individual sentiments. Our work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve human–machine interaction and challenges involved in analyzing them. A detailed survey on sentimental dataset, feature extraction algorithms, data fusion methods, and efficiency of different classification techniques are presented in this work. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Social Considerations
A newly-designed fault diagnostic method for transformers via improved empirical wavelet transform and kernel extreme learning machine

In order to solve the problems of unsatisfactory diagnosis performance and unstable model of conventional fault diagnosis methods for transformers, a new approach based on improved empirical wavelet transform (IEWT) and salp swarm algorithm (SSA) optimized kernel extreme learning machine (KELM) is proposed in this study. Firstly, IEWT is used to adaptively decompose the vibration signal to obtain a set of empirical wavelet functions (EWFs). Secondly, the first n-order components with high correlation coefficient are collected. Thirdly, the mean value, variance, kurtosis, refine composite multiscale entropy (RCMSE), and time-frequency entropy(TFE) of these n-order components are calculated to construct a fusion feature vector. Finally, a two-level diagnostic model based on SSA-KELM is established. The first-level of it is applied to identify normal and abnormal states, and the second-level is selected to identify fault categories in the abnormal states. The proposed method can effectively diagnose the existing fault categories in the training set and accurately identify the unknown categories of faults. Experimental results show that the proposed method can efficiently extract features of different vibration signals and identify the faults, with an average classification accuracy of 96.25%. It is better than other methods, such as wavelet packet energy spectrum analysis-KELM and EWT-fisher.
How artificial intelligence might disrupt diagnostics in hematology in the near future

Artificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?
Optimal planning of a rooftop PV system using GIS-based reinforcement learning

This study aimed to develop a geographic information system (GIS)-based reinforcement learning (RL) model for optimal planning of a rooftop PV system, considering the uncertainty of future scenarios throughout the life cycle of buildings. To that end, GIS was used to establish the spatial data for the rooftop PV installation, and an RL model was developed to maximize the economic profit of the rooftop PV installation in various locations and future scenarios. The developed model was applied to residential buildings in Nonhyeon district, South Korea to evaluate their economic profitability and to compare the model with the existing planning methods. With the use of the developed GIS-based RL model, the rooftop PV system became economically feasible, achieving average economic profit of 539,197 USD over all scenarios for all target buildings which was higher than that of the existing models by 4.4% and 4.3%. Furthermore, the developed model outperformed the existing models especially in volatile scenarios with lower solar radiation. Therefore, the use of the proposed GIS-based RL model can optimize the economic feasibility of rooftop PV systems for buildings, which will benefit building owners and community-level energy business owners. In conclusion, the developed model can promote the adoption of rooftop PV systems, which have 91.8% lower global warming potential than the Korean mixed grid, without additional subsidies to achieve Korea’s national CO2 emission reduction plan.
A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning

Breast cancer is a deadly disease responsible for statistical yearly global death. Identification of cancer tumors is quite tasking, as a result, concerted efforts are thus devoted. Clinicians have used ultrasounds as a diagnostic tool for breast cancer, though, poor image quality is a major limitation when segmenting breast ultrasound. To address this problem, we present a semantic segmentation method for breast ultrasound (BUS) images. The BUS images were resized and then enhanced with the contrast limited adaptive histogram equalization method. Subsequently, the variant enhanced block was used to encode the preprocessed image. Finally, the concatenated convolutions produced the segmentation mask. The proposed method was evaluated with two datasets. The datasets contain 264 and 830 BUS images respectively. Dice measure (DM), Jaccard measure, and Hausdroff distance were used to evaluate the methods. Results indicate that the proposed method achieves high DM with 89.73% for malignant and 89.62% for benign BUSs. Moreover, the results obtained validate the capacity of the proposed method to achieve higher DM in comparison with reported methods. The proposed algorithm provides a deep learning segmentation procedure that can segment tumors in BUS images effectively and efficiently.
Exploring the barriers and organizational values of blockchain adoption in the banking industry

This study intends to extend the literature on IT business value by unveiling the organizational values and hindrances of blockchain adoption in the banking industry. The study incorporated a mixed-method qualitative analysis composed of semi-structured interviews (15 experts), a two-panels Delphi study (26 & 20 experts), and a systematic literature review of 35 papers about blockchain adoption. In total, 65 experts from Europe, Asia, and the Middle East working in the banking industry participated in our study. The Delphi study ended up in a list of 25 organizational values and 20 barriers. The values were classified into four main categories: business, strategy, operation, and knowledge management, while the barriers were classified into three main categories: technological, organizational, and environmental. The study shows that the most critical organizational values of blockchain in the banking industry are related to business operations. It will increase the transparency, traceability, and trustworthiness of business processes. The least essential organizational values of blockchain are related to the business strategy domain: flexible business models, the flexibility of compliance to regulations, and certain ROI. The study shows that the most critical barriers to extracting value from blockchain in the industry are organizational and environmental, lack of understanding by top managers, compliance and regulatory requirements, and marketing noise. Theoretically, this study will extend the literature on blockchain business value, and practically it can provide insights for managers regarding mechanisms to maximize value from the blockchain. We recommended some future studies as well.
Achieving cybersecurity in blockchain-based systems: A survey

With the increase in connectivity, the popularization of cloud services, and the rise of the Internet of Things (IoT), decentralized approaches for trust management are gaining momentum. Since blockchain technologies provide a distributed ledger, they are receiving massive attention from the research community in different application fields. However, this technology does not provide with cybersecurity by itself. Thus, this survey aims to provide with a comprehensive review of techniques and elements that have been proposed to achieve cybersecurity in blockchain-based systems. The analysis is intended to target area researchers, cybersecurity specialists and blockchain developers. For this purpose, we analyze 272 papers from 2013 to 2020 and 128 industrial applications. We summarize the lessons learned and identify several matters to foster further research in this area.
RefinedC: automating the foundational verification of C code with refined ownership types

Given the central role that C continues to play in systems software, and the difficulty of writing safe and correct C code, it remains a grand challenge to develop effective formal methods for verifying C programs. In this paper, we propose a new approach to this problem: a type system we call RefinedC, which combines ownership types (for modular reasoning about shared state and concurrency) with refinement types (for encoding precise invariants on C data types and Hoare-style specifications for C functions).
DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) is a powerful cryptographic primitive to secure outsourced computations against an untrusted third-party provider. With the growing demand for AI and the usefulness of machine learning as a service (MLaaS), the need for secure training and inference of artificial neural networks is rising. However, the computational complexity of existing FHE schemes has been a strong deterrent to this. Prior works suffered from accuracy degradation, lack of scalability, and ciphertext expansion issues. In this paper, we take the first step towards the problem of space-efficiency in evaluating deep neural networks through designing DOReN: a low depth, batched neuron that can simultaneously evaluate multiple quantized ReLU-activated neurons on encrypted data without approximations. Our circuit design reduced the complexity of the accumulator circuit depth from O(logm ·logn) to O(logm + logn) for n bit integers. The experimental results show that the amortized processing time of our homomorphic neuron is approximately 1.26 seconds for 300 inputs and less than 0.13 seconds for 10 inputs at 80 bit security, which is a 20 fold improvement upon Lou and Jiang, NeurIPS 2019.
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection

Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy. Specifically, we first introduce six heterogeneous information paths to build our search space, namely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connect and none. Second, we propose a novel search space of FPNs, in which each FPN candidate is represented by a densely-connected directed acyclic graph (each node is a feature pyramid and each edge is one of the six heterogeneous information paths). Third, we propose an efficient one-shot search method to find the optimal path aggregation architecture; specifically, we first train a super-net and then find the optimal candidate with an evolutionary algorithm. Experimental results demonstrate the efficacy of the proposed OPANAS for object detection: (1) OPANAS is more efficient than state-of-the-art methods (e.g., NAS-FPN and Auto-FPN) at significantly smaller searching cost (e.g., only 4 GPU days on MS-COCO); (2) the optimal architecture found by OPANAS significantly improves main-stream detectors including RetinaNet, Faster R-CNN and Cascade R-CNN, by 2.3∼3.2 % mAP compared to their FPN counterparts; and (3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) is achieved at smaller training costs than comparable recent arts. Code will be released at https://github.com/VDIGPKU/OPANAS.
Financial stability of Asian Nations: Governance quality and financial inclusion

This study investigates the role of governance quality in maintaining financial stability and enhancing financial inclusion in Asian countries using the stakeholder theory. Composite variables were constructed for the governance quality, financial stability, and financial inclusion dimensions using principal component analysis (PCA). The impact of governance quality on financial stability and financial inclusion was analyzed using dynamic system generalized method of moments (GMM) estimations. Additionally, using Baron and Kenny's approach, the mediating role of financial inclusion in the relationship between governance quality and financial stability was examined. The results obtained were robust to various standardization techniques. This study's findings suggest that governance quality negatively affects financial inclusion but has a positive influence on financial stability. Furthermore, financial inclusion positively affects financial stability in Asia. Finally, based on the results, we conclude that financial inclusion mediates the relationship between governance quality and financial stability.
Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

Abstract Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non‐publicly domain datasets based on the two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment (QA) criteria, extracting data, and drawing results. We have evaluated a state‐of‐the‐art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.