Reviewerly/exHarmony huggingface dataset:
Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems

For a highly nonlinear system with additive process and observation noises, the non-augmented sigma-point nonlinear filter induces a loss of odd-order moment information (skewness), thereby resulting in a degradation of state estimation accuracy. To address this problem, we present a novel filtering algorithm, namely augmented cubature information filter (ACIF). The adopted augmentation strategy can facilitate the estimator to capture and propagate higher odd-order moment information of random variables. In addition, the covariance resulting from linearization errors is compensated based on the proposed filtering framework. Further, the ACIF is extended to the decentralized multi-sensor system. The employed modified state augmentation strategy can eliminate the inconsistency of covariance estimation induced by the state-mean samples. And the propagation of cubature points is simplified to reduce the computational cost associated with state augmentation. To validate the proposed algorithm, a comparative study is performed via Monte Carlo simulation in the scenario of tracking a maneuvering target. The results show that the simplified ACIF can improve the filtering efficiency without dramatically increasing the computational burden.
Machine Learning-Based Approach for Early Diagnosis of Breast Cancer Using Biomarkers and Gene Expression Profiles

Breast cancer is a heterogeneous disease and the most frequent malignancy in women globally each year. Emerging applications of gene expression profiles are important to determine the clinical outcomes for breast cancer. Biomarker dataset from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Oncomine databases are used to analyze the potential therapeutic target for the expression level of hub gene based on their microarray technology. Several computational intelligent techniques, such as—Naïve Bayes, Support vector machines (SVM), K-nearest neighbor, and decision trees, are mostly used to classify different types of breast cancers. These also have been appreciated in the field for identifying new gene biomarkers for breast cancer. Out of these major algorithms, SVM has been proved to have higher accuracy and specificity while detecting breast cancers during initial diagnosis. The benchmark of these algorithms can be attributed to the development of new computational biology strategies, characterization performance for gene expression profile data, and the ability to predict biomedical outcomes. This chapter focuses on an array of successful computational intelligent systems that are deployed for identifying potential biomarkers, strongly related to breast cancer survivability. Furthermore, we will also address the recent advancement in techniques, their challenges, possible opportunities, and clinical implementation in their assessment.
IoT Based Fall Detection System for Elderly Healthcare

Falls are a leading cause of immobility, morbidity, and mortality in older adults. Falls incur high cost to health services with millions of bed days. Half of the older adults over 65 years old, fall in a span of 5 years with 62% sustaining injuries and 28% protracting extensive injuries. Automatic fall detection system for elderly healthcare through Internet of Things (IoT) human-centered design can provide timely detection and communication of fall events for immediate medical aid in case of injury or unconsciousness. Fall detection systems have been reported to provide reduction in death rates of up to 80% due to timely medical support. In this chapter, we discuss elderly-centric IoT based fall detection system for smart homes and care centers with emphasis on edge, fog and cloud IoT layers. Sensing edge devices with wearable/environmental sensors, vision-based systems, and radio frequency sensing systems, such as WiFi-based sensing and RADAR are presented for an IoT-centered fall detection system. IoT gateways and communication protocols for the fog layer are discussed in the context of a fall detection system. Cloud processing of edge device data for fall activity detection and classification from activities of daily life is explained. Machine and deep learning algorithms for detection of fall events from 1 and 2D signals (image/video) are presented and various deployment scenarios are discussed in the context of edge or cloud IoT layers. This chapter is concluded with results and performance comparison of several IoT centered fall detection systems in terms of various sensing systems and state-of-the-art machine and deep learning models for effective detection of falls for elderly healthcare. Furthermore, future work and prospective improvements in IoT centered design for fall detection in elderly healthcare is discussed.
Improve Unsupervised Pretraining for Few-label Transfer

Unsupervised pretraining has achieved great success and many recent works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance than supervised pretraining on downstream target datasets. But in this paper, we find this conclusion may not hold when the target dataset has very few labeled samples for finetuning, i.e., few-label transfer. We analyze the possible reason from the clustering perspective: 1) The clustering quality of target samples is of great importance to few-label transfer; 2) Though contrastive learning is essential to learn how to cluster, its clustering quality is still inferior to supervised pretraining due to lack of label supervision. Based on the analysis, we interestingly discover that only involving some unlabeled target domain into the unsupervised pretraining can improve the clustering quality, subsequently reducing the transfer performance gap with supervised pretraining. This finding also motivates us to propose a new progressive few-label transfer algorithm for real applications, which aims to maximize the transfer performance under a limited annotation budget. To support our analysis and proposed method, we conduct extensive experiments on nine different target datasets. Experimental results show our proposed method can significantly boost the few-label transfer performance of unsupervised pretraining.
Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems

With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm's key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA's performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.
Phase recovery technology of a dual-frame phase-shifting interferogram based on first-order norm vector normalization

The pursuit of high-precision and high-efficiency phase recovery methods has been a research focus of interferometric technology. We propose a dual-frame phase-shifting interferogram phase recovery technique based on normalization of the first-order norm. A set of sine and cosine components is constructed by the addition and subtraction of dual-frame interferograms. Then the first-order norm normalization method is employed to achieve vector orthogonality. The phase distribution is then obtained through the arctangent operation. State-of-the-art dual-frame phase recovery techniques are evaluated, and it shows that the first-order norm normalization method outperforms the second-order norm normalization method. Especially in terms of computational efficiency, the method using the first-order norm is at least 50% more efficient than other methods.
SGD-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si41.svg"><mml:mrow><mml:mi>r</mml:mi><mml:mi>α</mml:mi></mml:mrow></mml:math>: A real-time <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si42.svg"><mml:mrow><mml:mi>α</mml:mi></mml:mrow></mml:math>-suffix averaging method for SGD with biased gradient estimates

Stochastic gradient descent (SGD) is a simple and efficient method for solving large-scale stochastic optimization problems in machine learning. It has been shown that the convergence rate of SGD can be improved by α-suffix averaging technique, abbreviated as SGD-α. Classic analysis usually requires the assumption of unbiased gradient estimates, which is not suitable for many practical applications of SGD-α such as non-independently and identically distributed (non-i.i.d.) scenarios. Another limitation is that SGD-α needs to store all iterates in memory and thus cannot be implemented on-the-fly. To address the issues, we employ rounding technique to propose a real-time version of SGD-α (named SGD-rα), which can iteratively calculate the α-suffix averaging and has the same convergence rate as that of SGD-α. In particular, SGD-rα with biased gradient estimates can obtain sublinear convergence rate for strongly convex objectives. Numerical experiments on the benchmark datasets have shown the characteristics of SGD-rα and corroborated the theoretical results. The implementation of SGD-rα is available at:https://github.com/xudp100/SGD-ra.
RefFaceNet: Reference-based Face Image Generation from Line Art Drawings

The flexible artistic creation of painting using deep neural networks has attracted lots of attention recently. Existing image-to-image translation approaches show powerful capabilities in producing photos between various domains. However, little attention has been paid to reference - based and line-based tasks simultaneously. In this paper, we introduce a novel network, RefFaceNet, to synthesize face portraits by utilizing reference face photos and line art drawings that only consist of the outlines of principal facial components. Our model can provide those people who have no experience in painting with the freedom to create. We utilize two separate encoders to focus on learning better feature representations for the line domain and the reference domain. An Attention-based Face Transfer Module composed of several sub-modules is built to capture the spatial correspondence between the two types of features. To construct a more robust encoding ability for our generator, we first learn a direct mapping from the line drawings to their ground truth color images and then take some distortions on the reference examples. With the assistance of optimal transport, we further propose to keep Spatial Distortion Consistency between the reference pictures in different geometric shapes by aligning features in high-dimensional space. A series of experiments have been conducted, and the results demonstrate the superiority of our method in generating more pleasing images compared with state-of-the-art models.
B2H: Enabling delay-tolerant blockchain network in healthcare for Society 5.0

This paper introduces an architecture – B2H – to enable a delay-tolerant public blockchain network (PBCN) in healthcare systems for Society 5.0. Healthcare systems in Society 5.0 envision an abstract paradigm that provides beyond health monitoring services to all the citizens of the society. These services include healthcare medical supply chain, clinical trial, and hospital management. To provide these services, the existing healthcare systems upload their important information, such as users’ health parameters, medicinal details, and clinical trial information into the cloud using PBCN (Ioppolo et al., 2020). Additionally, PBCN provides end-to-end security in uploading of such healthcare information in healthcare systems by decentralizing and distributing the information validation among multiple end-user devices. The existing PBCN-based healthcare systems enable its users to validate information before the transaction of healthcare information. Enabling such a facility in the healthcare systems of Society 5.0 increases the validation latency of PBCN in healthcare systems, considering the presence of a huge number of citizens in Society 5.0. On the other hand, the existing PBCN-based healthcare systems are unable to detect malicious end-users, which may degrade the efficiency of the healthcare systems of Society 5.0. We introduce B2H to address the issues of increasing validation latency and detecting malicious end-users in the healthcare systems of Society 5.0. The architecture of B2H utilizes the features of PBCN. B2H introduce a layer of validation service providers (VSPs) in a PBCN that collects information from the end-user, distributes it among multiple validation devices. VSP in B2H reduces the validation time by distributing the information among a specific set of validation devices (VDs). B2H also allows the end-user to optimally select a VSP and a VSP to select a set of validation devices optimally. Through extensive experiments, we observe that B2H performs better than the existing schemes by reducing 94%–95% average network delay, 88%–94% average energy consumption, and 94%–96% average cost.
Applying ontology learning and multi-objective ant colony optimization method for focused crawling to meteorological disasters domain knowledge

The focused crawler based on semantic analysis is a research hotspot in the field of information retrieval. The domain ontology is generally applied to construct the topic model of the focused crawler. In order to overcome the limitations of builders' knowledge reserve and subjective consciousness in the process of constructing artificially ontology, a semi-automatic construction method of domain ontology based on ontology learning technology combining the latent Dirichlet allocation and the Apriori algorithm is proposed in this article. When evaluating the relevance between a hyperlink and a specific topic, the joint evaluation method considering both the web text and the link structure is usually used. However, the traditional weighted sum method is difficult to reasonably determine the optimal weights of these evaluating indicators. To solve this problem, a multi-objective optimization model for link evaluation and a subsequent multi-objective ant colony optimization algorithm (MOACO) are proposed. In the MOACO, a method of the nearest farthest candidate solution (NFCS) is combined with the fast non-dominated sorting to select a set of Pareto-optimal hyperlinks and guide the crawlers’ search directions. The experimental results of the focused crawling on the domain knowledge of typhoon disasters and rainstorm disasters prove that the ability of the proposed focused crawlers to retrieve topic-relevant webpages.
Detection of Cyber Attacks in Encrypted Control Systems

Recently a resilient homomorphic encryption (RHE) scheme has been proposed in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , which allows not only to carry out the evaluation process of an output feedback controller in an encrypted environment but also can neutralize the effect of additive attacks injected into the ciphertexts. However, the resilience to additive attacks has its limits. In this letter, at first it will be shown that the resilience range of the RHE scheme to additive attacks is indeed much larger than shown in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . Then, a detection approach is proposed to give a twofold protection to control systems encrypted by the RHE scheme. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">warning</i> signal is triggered as soon as an additive attack is injected into the ciphertexts transmitted over the network, while an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alarm</i> signal is triggered when the attack is outside of the resilience range. This is achieved by exploiting the symmetric property of the inner product. Therefore, the RHE scheme can be combined with the proposed detection approach to ensure the integrity of the signals obtained after decryption in case of additive attacks. A simulation example of the well-established quadruple-tank benchmark process is used to demonstrate the proposed detection approach for encrypted control systems.
The Design and Application of College English-Aided Teaching System Based on Web

With the rapid advancement of information technology, the Internet and the web have become the central technology tools for information sharing in education and teaching. Modern education combines teaching with information technology and gives full play to the role of information technology in education and teaching. To improve the resource sharing and scheduling level a web-based college English-assisted teaching system is presented in this study. Using the JavaScript object notation syntax (JSON) as the data communication format between the intelligent terminal and the server, the system is implemented using a web server and mobile Android terminal, and a web-based data integration module is designed. Statistical data mining and fuzzy feature detection are employed to implement the decision-making scheduling and self-adaptive optimization of English teaching resources in a web aided English teaching system. The fusion particle swarm optimization algorithm is used to integrate English teaching resources and implement adaptive scheduling. To verify the application performance of the designed system, a simulation experiment is designed. The experimental results show that the proposed English-assisted instruction system has good convergence and high accuracy of learning information, optimal allocation, and scheduling.
TraceSim: An Alignment Method for Computing Stack Trace Similarity

Software systems can automatically submit crash reports to a repository for investigation when program failures occur. A significant portion of these crash reports are duplicate, i.e., they are caused by the same software issue. Therefore, if the volume of submitted reports is very large, automatic grouping of duplicate crash reports can significantly ease and speed up analysis of software failures. This task is known as crash report deduplication. Given a huge volume of incoming reports, increasing quality of deduplication is an important task. The majority of studies address it via information retrieval or sequence matching methods based on the similarity of stack traces from two crash reports. While information retrieval methods disregard the position of a frame in a stack trace, the existing works based on sequence matching algorithms do not fully consider subroutine global frequency and unmatched frames. Besides, due to data distribution differences among software projects, parameters that are learned using machine learning algorithms are necessary to provide more flexibility to the methods. In this paper, we propose TraceSim – an approach for crash report deduplication which combines TF-IDF, optimum global alignment, and machine learning (ML) in a novel way. Moreover, we propose a new evaluation methodology for this task that is more comprehensive and robust than previously used evaluation approaches. TraceSim significantly outperforms seven baselines and state-of-the-art methods in the majority of the scenarios. It is the only approach that achieves competitive results on all datasets regarding all considered metrics. Moreover, we conduct an extensive ablation study that demonstrates the importance of each TraceSim's element to its final performance and robustness. Finally, we provide the source code for all considered methods and evaluation methodology as well as the created datasets.
Resume Parsing Framework for E-recruitment

Modern approaches to improve networking and communication have given ways to the advancement of recruitment process through the development of e-recruitment recommender systems. The increasing expansion of internet- based recruiting has resulted in a large number of resumes being stored in recruitment systems. Most resumes are prepared in a variety of styles to attract the attention of recruiters, including different font sizes, font colors, and table formats. However, data mining operations such as resume information extraction, automatic profile matching, and applicant ranking are immensely affected by the variety of formats. Rule-based methods, supervised methods and semantics-based methods have been introduced to extract facts from resume accurately, however, these methods heavily depend on large amounts of annotated data that is usually difficult to collect Furthermore, these techniques are time-intensive and bear knowledge incompleteness that strongly affect the accuracy of resume parser. In this paper, we present a resume parsing framework that handles the limitations faced in the previous techniques. At first, the raw text is extracted from resumes and blocks are separated using text block classification. Furthermore, the entities are extracted using named entity recognition and enriched using ontology. The proposed resume parser accurately extracts information from resumes that directly contributes towards the selection of best candidate.
Under Pressure. A User-Centered Threat Model for Cryptocurrency Owners

Cryptocurrencies have gained popularity in recent years. However, for many users, keeping ownership of their cryptocurrency is a complex task. News reports frequently bear witness to scams, hacked exchanges, and fortunes beyond retrieval. However, we lack a systematic understanding of user-centered cryptocurrency threats, as causes leading to loss are scattered across publications. To address this gap, we conducted a focus group (n=6) and an expert elicitation study (n=25) following a three-round Delphi process with a heterogeneous group of blockchain and security experts from academia and industry. We contribute the first systematic overview of threats cryptocurrency users are exposed to and propose six overarching categories. Our work is complemented by a discussion on how the human-computer-interaction community can address these threats and how practitioners can use the model to understand situations in which users might find themselves under the pressure of an attack to ultimately engineer more secure systems.