Reviewerly/exHarmony huggingface dataset:
Reliability Dynamic Analysis by Fault Trees and Binary Decision Diagrams

New wind turbines are becoming more complex and reliability analysis of them rising in complexity. The systems are composed of many components. Fault tree is used as an useful tool to analyze these interrelations and provide a scheme of the wind turbine, to get a quick overview of the behavior of the system under certain conditions of the components. However, it is complicated and in some cases not possible, to identify the conditions that would generate a wind turbine failure. A quantitative and qualitative reliability analysis of the wind turbine is proposed in this study. Binary decision diagrams are employed as a suitable and operational method to facilitate this analysis and to get an analytical expression by the Boolean functions. The size of the binary decision diagram, i.e., the computational cost for solving the problem, has an important dependence on the order of the components or events considered. Different heuristic ranking methods are used to find an optimal order or one closed, and to validate the results: AND, level, top-down-left-right, deep-first search and breadth-first-search. Birnbaum and criticality importance measures are proposed to evaluate the relevance of each component. This analysis leads to classify the events according to their importance with respect to the probability of the top event. This analysis provides the basis for making medium and long-term maintenance strategies.
Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining

We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models are attractive owing to their ability to convert prosody. While seq2seq models based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been successfully applied to VC, the use of the Transformer network, which has shown promising results in various speech processing tasks, has not yet been investigated. Nonetheless, their data-hungry property and the mispronunciation of converted speech make seq2seq models far from practical. To this end, we propose a simple yet effective pretraining technique to transfer knowledge from learned TTS models, which benefit from large-scale, easily accessible TTS corpora. VC models initialized with such pretrained model parameters are able to generate effective hidden representations for high-fidelity, highly intelligible converted speech. Experimental results show that such a pretraining scheme can facilitate data-efficient training and outperform an RNN-based seq2seq VC model in terms of intelligibility, naturalness, and similarity.
Towards Controllable Biases in Language Generation

We present a general approach towards controllable societal biases in natural language generation (NLG).Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups.We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics.The former scenario enables us to detect the types of biases present in the model.Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics.The second scenario is useful for mitigating biases in downstream applications such as dialogue generation.In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
Fully Automated Breast Density Segmentation and Classification Using Deep Learning

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms' fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study's findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.
Machine learning in medical imaging

Medical imaging is an indispensable component of modern healthcare, playing a critical role in diagnosis, staging, and the assessment of treatment response for most major medical conditions. Advancements in image acquisition technologies have resulted in imaging modalities that capture more detailed and diverse visual information, leading to challenges in image analysis and interpretation. Within this context, machine learning (ML) algorithms offer the opportunity to enhance clinical analytics and decision-making through the creation of computer tools that can be trained to automatically analyze the visual details of medical image data. This chapter examines the current field of ML methods and their utility in clinical applications in decision support systems, with particular emphasis on advances from developments in deep learning. These methods are discussed with respect to the challenges in ML for medical imaging including limited annotated data, imbalanced class distributions, and labels that may be subjective or uncertain.
A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low-discrepancy sequence. To solve the optimization problems in large-dimensional search spaces, the proposed solution is termed as WE-PSO. The suggested solution has been verified on fifteen well-known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE-PSO is compared with the standard PSO and two other initialization approaches Sobol-based PSO (SO-PSO) and Halton-based PSO (H-PSO). The findings indicate that WE-PSO is better than the standard multimodal problem-solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H-PSO, and SO-PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.
Dynamics analysis, hardware implementation and engineering applications of novel multi-style attractors in a neural network under electromagnetic radiation

This article studies the interesting dynamics of a small neural network with three neurons under electromagnetic radiation. The electromagnetic radiation strength can change the number of the equilibrium points in the neural network, which leads to the diversification of the attractor’s trajectory. Thus, the novel multi-style attractors like one-to-two-spiral attractors and one-to-four-scroll attractors can be generated from the neural network stimulated by electromagnetic radiation. Besides, the plentiful dynamical behaviors are observed in the neural network, such as transitional coexisting attractors, hypogenetic attractors, periodic patterns, firing patterns, transient chaos and intermittent chaos. In terms of hardware implementation, we utilize FPGA to digitally implement the constructed neural network model. The experimental verification results are highly consistent with the numerical simulation results. In the aspects of engineering application, we apply it to pseudo-random number generator and image encryption respectively, test the random performance of different chaotic attractors by NIST test suite, describe the image encryption scheme based on neural network and estimate its security performances. The ultimate outcomes demonstrate that the neural network model with chaotic behavior has superior randomness and wonderful security, which is very suitable for engineering applications based on chaos.
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding

Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical way. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simplified architecture that requires minor code changes upon the original vision transformer. The benefits of the proposed judiciously-selected design are threefold: (1) NesT converges faster and requires much less training data to achieve good generalization on both ImageNet and small datasets like CIFAR; (2) when extending our key ideas to image generation, NesT leads to a strong decoder that is 8 times faster than previous transformer-based generators; and (3) we show that decoupling the feature learning and abstraction processes via this nested hierarchy in our design enables constructing a novel method (named GradCAT) for visually interpreting the learned model. Source code is available https://github.com/google-research/nested-transformer.
Avoiding obstacles in cooperative load transportation

This work deals with load transportation by quadrotors, when the load is attached to the vehicles through flexible cables. More specifically, two quadrotors are used to carry a single load, which is attached to both vehicles, through such kind of cables. The idea of using two quadrotors working cooperatively to carry the load is adopted to suppress any load oscillation in the direction of movement, what would happen if just one UAV were used. As a consequence of using two UAVs (or more than two) it can happen collisions between the vehicles when carrying the load, caused by the forces the load exert on the two vehicles, whose tendency is to bring the vehicles closer one to the other when they accelerate forward. The paper proposes a strategy to avoid such collisions and any collision with obstacles eventually present in the working space as well. Simulated results are shown and discussed, using two AR.Drone®2.0 quadrotor to carry the load, which validate the proposed strategy.
Should accountants care about blockchain?

Abstract The use of blockchain technology has increased tremendously over the last decade. Blockchain continues to evolve and new features and capabilities are developed continuously. Although the use of the technology started in cryptocurrency (specifically, Bitcoin), it has expanded to other areas that can benefit from a shared, secure, ledger. This article investigates the potential impact of blockchain technology on the accounting profession. This article analyzes data security and privacy considerations, technology, adoption, and implementation considerations, and some considerations that relate specifically to accounting and auditing. We find that the unique needs of an accounting information system may not be a good match for blockchain as it currently exists. While we explain that blockchain may deliver many benefits, particularly in the areas of data reliability and the financial statement audit, we identify several factors, which raise significant questions about whether blockchain will ever be significantly integrated in the accounting function. Chief among those concerns is the scalability of the technology at an acceptable cost. While significant investment has and will be made for further development of blockchain business applications, it is our assessment that proponents of blockchain integration in accounting have not yet made the economic case for it. We also have significant concerns about whether blockchain technology can adequately address risks associated with data security and privacy. Addressing all these issues will be a minimum requirement for gaining widespread acceptance by firms and their accountants.
Connecting heterogeneous quantum networks by hybrid entanglement swapping

Recent advances in quantum technologies are rapidly stimulating the building of quantum networks. With the parallel development of multiple physical platforms and different types of encodings, a challenge for present and future networks is to uphold a heterogeneous structure for full functionality and therefore support modular systems that are not necessarily compatible with one another. Central to this endeavor is the capability to distribute and interconnect optical entangled states relying on different discrete and continuous quantum variables. Here we report an entanglement swapping protocol connecting such entangled states. We generate single-photon entanglement and hybrid entanglement between particle-like and wave-like optical qubits, and then demonstrate the heralded creation of hybrid entanglement at a distance by using a specific Bell-state measurement. This ability opens up the prospect of connecting heterogeneous nodes of a network, with the promise of increased integration and novel functionalities.
Extending Multilingual BERT to Low-Resource Languages

Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning.However, this success is focused only on the top 104 languages in Wikipedia it was trained on.In this paper, we propose a simple but effective approach to extend M-BERT (E-MBERT) so it can benefit any new language, and show that our approach aids languages that are already in M-BERT as well.We perform an extensive set of experiments with Named Entity Recognition (NER) on 27 languages, only 16 of which are in M-BERT, and show an average increase of about 6% F 1 on M-BERT languages and 23% F 1 increase on new languages.We release models and code at 1 .
Prebaking Functions to Warm the Serverless Cold Start

Function-as-service (FaaS) platforms promise a simpler programming model for cloud computing, in which the developers concentrate on writing its applications. In contrast, platform providers take care of resource management and administration. As FaaS users are billed based on the execution of the functions, platform providers have a natural incentive not to keep idle resources running at the platform's expense. However, this strategy may lead to the cold start issue, in which the execution of a function is delayed because there is no ready resource to host the execution. Cold starts can take hundreds of milliseconds to seconds and have been a prohibitive and painful disadvantage for some applications. This work describes and evaluates a technique to start functions, which restores snapshots from previously executed function processes. We developed a prototype of this technique based on the CRIU process checkpoint/restore Linux tool. We evaluate this prototype by running experiments that compare its start-up time against the standard Unix process creation/start-up procedure. We analyze the following three functions: i) a "do-nothing" function, ii) an Image Resizer function, and iii) a function that renders Markdown files. The results attained indicate that the technique can improve the start-up time of function replicas by 40% (in the worst case of a "do-nothing" function) and up to 71% for the Image Resizer one. Further analysis indicates that the runtime initialization is a key factor, and we confirmed it by performing a sensitivity analysis based on synthetically generated functions of different code sizes. These experiments demonstrate that it is critical to decide when to create a snapshot of a function. When one creates the snapshots of warm functions, the speed-up achieved by the prebaking technique is even higher: the speed-up increases from 127.45% to 403.96%, for a small, synthetic function; and for a bigger, synthetic function, this ratio increases from 121.07% to 1932.49%.
Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids

This paper proposes a novel adversarial scheme for learning from data under harsh learning conditions of partially labelled samples and skewed class distributions. This novel scheme integrates the generative ability of the state-of-the-art conditional generative adversarial network with the semi-supervised deep ladder network and semi-supervised deep auto-encoder. The proposed generative-adversarial based semi-supervised learning framework, named GBSS, is a triple network that aims to optimize a newly defined objective function to enhance the performance of the semi-supervised learner with the help of a generator and discriminator. The duel between the generator and discriminator results in the generation of more synthetic minority class samples that are very similar to the original minority samples (attacks and faults). Meanwhile, GBSS trains the semi-supervised model to learn the general distribution of the minority class samples including the newly generated samples in contrast to other classes and iteratively adjusts its weights. Moreover, a diagnostic framework is designed, in which GBSS and several state-of-the-art semi-supervised learners are used for learning and diagnosing attacks and faults in power grids. These methods are evaluated and compared for diagnosing attacks and faults in two different power grid cases. The attained results demonstrate the superiority of GBSS in diagnosing attacks and faults under the harsh conditions.
Implementation of adaptive learning at higher education institutions by means of Moodle LMS

Abstract E-learning courses have become popular means of delivering knowledge to students in higher education institutions. Most participants of learning process note that they benefit from the possibility to gain knowledge regardless of time, location and device they use. Among other advantages possibility to return to learnt material several times, divide material into parts, consume information through different types of educational materials (video, infographics, presentations, text, quizzes etc.) are mentioned. At the same time most of the surveyed students chose that they lack personalization both of materials and studying process, limited in terms of fulfillment and would like to have a choice of the level of study. The educational trend that is able to put into practice the above mentioned requirements is blended learning as it has a range of advantages such as usability, consideration of individual abilities, additional materials introduction, activities monitoring. Although it combines offline and online learning, effectiveness of e-learning courses designed for its implementation play a crucial role. To make a learning process correspondent to the students’ needs adaptive learning can be introduced in higher education institutions. Adaptive learning is a methodology that allows to identify level of students’ knowledge and their learning styles and transform materials, tasks and ways of their delivery according to the needs of learning process participants. LMS Moodle offers different solutions for adaptive learning. They provide administrators and teachers with tools to vary all stages of a learning process starting with delivery of information and ending with assessment.
Self-Efficacy, Job Satisfaction and Teacher Well-Being in the K-12 Educational System

Teacher job satisfaction and well-being have a significant impact on educational outcomes, considering that teaching is the main objective of the educational process. The aim of this study is to examine the relationship between teacher job satisfaction and four main categories of determinants: self-efficacy, relational aspects (colleague collaboration, student behavior, school management), work-related aspects (administrative workload, teaching tasks), and working conditions, in order to identify various implications for teachers' well-being. The study employs a survey delivered to 658 K-12 (pre-university) teachers, from the North-West region of Romania. We used factorial analysis and a structural equation model to test eight proposed hypotheses. The results showed that self-efficacy, promotion, positive student behavior, and working conditions have significant effects on job satisfaction. These factors influence job satisfaction and well-being in the teaching profession because they ensure a positive work environment in which teachers and students thrive, thus leading to higher levels of involvement from teachers, students, and parents alike. An efficient work environment decreases attrition, burnout, emotional exhaustion, and teacher turnover, while increasing job satisfaction, well-being, and teacher retention.