Reviewerly/exHarmony huggingface dataset:
Sociolectal Analysis of Pretrained Language Models

Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes.We demonstrate wide performance gaps across demographic groups and show that pretrained language models systematically disfavor young non-white male speakers; i.e., not only do pretrained language models learn social biases (stereotypical associations) -pretrained language models also learn sociolectal biases, learning to speak more like some than like others.We show, however, that, with the exception of BERT models, larger pretrained language models reduce some the performance gaps between majority and minority groups.
Promoting Fairness through Hyperparameter Optimization

Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric- or model-specific, require access to sensitive attributes at inference time, or carry high development or deployment costs. This work explores the unfairness that emerges when optimizing ML models solely for predictive performance, and how to mitigate it with a simple and easily deployed intervention: fairness-aware hyperparameter optimization (HO). We propose and evaluate fairness-aware variants of three popular HO algorithms: Fair Random Search, Fair TPE, and Fairband. We validate our approach on a real-world bank account opening fraud case-study, as well as on three datasets from the fairness literature. Results show that, without extra training cost, it is feasible to find models with 111% mean fairness increase and just 6% decrease in performance when compared with fairness-blind HO.
A Smart contract-based secure data sharing scheme in Healthcare 5.0

In-Patient Monitoring, data sharing is fundamental step to get the comprehension from earlier research-oriented work. Traditional data-sharing policies rely on third-party holders that can compromise the system transparency, trust, and integrity. To solve the aforementioned issues, we have proposed a smart contract-based secure data sharing scheme in healthcare 5.0 by leveraging the advantages of the interplanetary file system (IPFS). The proposed scheme achieves data confidentiality, integrity, and access control rules by implementing active entities’ access control policy written in a smart contract. Entities are first authenticated using digital signatures. Afterwards, the completion of treatment initiated the transaction and transferred the amount to the requested entities. This scheme integrates Ethereum blockchain (BCN), decentralized storage, and incentive schemes. In this scheme, smart contracts are written in solidity language and deployed on the Ethereum-based test network. The proposed scheme achieves data integrity, confidentiality, authentication, and non-repudiation. The result section calculates the communication and computation cost of 1408 bits and 0.112 ms, respectively.
Recognition of dynamic objects from UGVs using Interconnected Neural network-based Computer Vision system

In this study, moving object recognition is performed by using images from a camera mounted on an unmanned ground vehicle. A GPS coordinate-based algorithm has been developed to obtain moving object silhouettes. In order to classify these silhouettes, an interconnected artificial neural network (ICANN) architecture consisting of two stages has been developed. The method consists of two phases. In the first phase, real-time images are converted to binary images at the end of the GPS-assisted image registration process. Then, the silhouettes are extracted from the background of the images using connected component labelling. In the second phase, two interconnected neural networks are used. The first neural network classifies silhouettes as objects or noise. The second neural network divides objects into seven subclasses as pedestrians, potholes, cars, etc. Compared to CNN-based techniques, a simpler NN architecture was employed in the research, and better accuracy rates were achieved with fewer samples. Another contribution of the research is simultaneous localization and mapping (SLAM) applications can be performed in non-GPS environments using pre-recorded images containing GPS information. In experimental studies, maximum success rates of 96,1% in object classification were obtained. The results obtained were compared to YOLO, the recently popular algorithm for object recognition.
Thinking multiculturality in the age of hybrid threats: Converging cyber and physical security in Akkuyu nuclear power plant

Nuclear Power Plants (NPPs) are the most protected facilities among all critical infrastructures (CIs). In addition to physical security, cyber security becomes a significant concern for NPPs since swift digitalization and overreliance on computer-based systems in the facility operations transformed NPPs into targets for cyber/physical attacks. Despite technical competencies, humans are still the central component of a resilient NPP to develop an effective nuclear security culture. Turkey is one of the newcomers in the nuclear energy industry, and Turkish Akkuyu NPP has a unique model owned by an international consortium. Since Turkey has limited experience in nuclear energy industry, specific multinational and multicultural characteristics of Turkish Akkuyu NPP also requires further research in terms of the Facility's prospective nuclear security. Yet, the link between “national cultures” and “nuclear security” is underestimated in nuclear security studies. By relying on Hofstede's national culture framework, our research aims to address this gap and explore possible implications of cross-national cultural differences on nuclear security. To cope with security challenges in the age of hybrid threats, we propose a security management model which addresses the need for cyber-physical security integration to cultivate a robust nuclear security culture in a multicultural working environment.
MOO-DNAS: Efficient Neural Network Design via Differentiable Architecture Search Based on Multi-Objective Optimization

The progress devoted to improving the performance of neural networks has come at a high price in terms of cost and experience.Fortunately, the emergence of Neural Architecture Search improves the speed of network design, but most excellent works only optimize for high accuracy without penalizing the model complexity.In this paper, we propose an efficient CNN architecture search framework, MOO-DNAS, with multi-objective optimization based on differentiable neural architecture search.The main goal is to trade off two competing objectives, classification accuracy and network latency, so that the search algorithm is able to discover an efficient model while maintaining high accuracy.In order to achieve a better implementation, we construct a novel factorized hierarchical search space to support layer variety and hardware friendliness.Furthermore, a robust sampling strategy named ''hard-sampling'' is proposed to obtain final structures with higher average performance by keeping the highest scoring operator.Experimental results on the benchmark datasets MINST, CIFAR10 and CIFAR100 demonstrate the effectiveness of the proposed method.The searched architectures, MOO-DNAS-Nets, achieve advanced accuracy with fewer parameters and FLOPs, and the search cost is less than one GPU-day.
Multiple-Valued Logic Modelling for Agents Controlled via Optical Networks

The methods of data verification are discussed, which are intended for the distant control of autonomous mobile robotic agents via networks, combining optical data links. The problem of trust servers is considered for position verification and position-based cryptography tasks. In order to obtain flexible quantum and classical verification procedures, one should use the collective interaction of agents and network nodes, including some elements of the blockchain. Multiple-valued logic functions defined within discrete k-valued Allen–Givone algebra are proposed for the logically linked list of entries and the distributed ledger, which can be used for distant data verification and breakdown restoration in mobile agents with the help of partner network nodes. A distributed ledger scheme involves the assigning by distant partners of random hash values, which further can be used as keys for access to a set of distributed data storages, containing verification and restoration data. Multiple-valued logic procedures are simple and clear enough for high-dimensional logic modelling and for the design of combined quantum and classical protocols.
Single Exposure Phase-Only Optical Image Encryption and Hiding Method via Deep Learning

Phase-only optical image security technology Based on one-time pad is an encryption and hiding method by introducing random key through phase modulation, which can improve the security of the system to a great extent.The application of traditional phase-only optical image encryption and hiding (POIEH) method is often limited by the amount of data and the quality of decrypted image.In this paper, a single exposure POIEH method using deep learning (DL) is proposed.The original image and corresponding encrypted hidden interferogram acquired with POIEH system are constructed to the train datasets for the learning of an end-to-end designed U-net, then the corresponding relationship between the encrypted hidden interferogram and the reconstructed image is learned and only single-frame encrypted hidden interferogram is needed to perform the reconstruction of POIEH.This method can realize real-time image encryption and hiding and high-quality decryption with only one interferogram.Simulation results and performance analysis show that the proposed method has higher security, stronger generalization ability and robustness.
Efficient privacy-preserving whole-genome variant queries

Abstract Motivation Diagnosis and treatment decisions on genomic data have become widespread as the cost of genome sequencing decreases gradually. In this context, disease–gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives. Many studies have shown that this information can be obtained from the query-response pairs on genomic databases. In this work, we propose a method that uses secure multi-party computation to query genomic databases in a privacy-protected manner. The proposed solution privately outsources genomic data from arbitrarily many sources to the two non-colluding proxies and allows genomic databases to be safely stored in semi-honest cloud environments. It provides data privacy, query privacy and output privacy by using XOR-based sharing and unlike previous solutions, it allows queries to run efficiently on hundreds of thousands of genomic data. Results We measure the performance of our solution with parameters similar to real-world applications. It is possible to query a genomic database with 3 000 000 variants with five genomic query predicates under 400 ms. Querying 1 048 576 genomes, each containing 1 000 000 variants, for the presence of five different query variants can be achieved approximately in 6 min with a small amount of dedicated hardware and connectivity. These execution times are in the right range to enable real-world applications in medical research and healthcare. Unlike previous studies, it is possible to query multiple databases with response times fast enough for practical application. To the best of our knowledge, this is the first solution that provides this performance for querying large-scale genomic data. Availability and implementation https://gitlab.com/DIFUTURE/privacy-preserving-variant-queries. Supplementary information Supplementary data are available at Bioinformatics online.
A Blockchain-Based Key-Revocation Access Control for Open Banking

Open banking allows banks and financial sectors to easily access the customers’ financial data which is revolutionizing. It also provides the customers with excellent cloud access to various providers’ wide range of financial services. The storage of such sensitive services and data on cloud servers is a double-edged sword. It can ease and support fine-grained access to such services/data anywhere and anytime, supporting the open banking system. But, on the other hand, data privacy and secrecy are a challenge. Thus, efficient access control should exist for open banking’s services and data to protect cloud-hosted financial sensitive data from unauthorized customers. This paper proposes a new access control scheme that employs blockchain for the key-revocation process. We implement the smart contract’s functions on the Ethereum platform and test the contract’s code on the Kovan Testnet before deploying it to the Mainnet. Although the customer is authenticated to open banking, his key/s can be revoked according to the status response of the bank branch. Thus, his access to financial services and data is denied. We did comprehensive experiments for the revocation status response time, data exchanged until receiving the revocation status, and the time spent updating the policy. Also, we compared the results of our proposed scheme with two well-known methods—Certificate Revocation List (CRL) and Online Certificate Status Protocol (OCSP). The experimental results show that our proposed scheme (BKR-AC) has a faster response time than Certificate Revocation List (CRL) and Online Certificate Status Protocol (OCSP) in case of nonrevoked keys/certificates and a slower response time in case of revoked keys to avoid nonrevoking a revoked key. But the data exchanged is an average for BKR-AC between CRL and OCSP, which is still a tiny amount and accepted. The security analysis proved that our scheme is secure against some well-known attacks on open banking systems. In addition, it is also secured against the chosen-text attack by employing the challenge-response authentication mechanism.
NSVQ: Noise Substitution in Vector Quantization for Machine Learning

Machine learning algorithms have been shown to be highly effective in solving optimization problems in a wide range of applications.Such algorithms typically use gradient descent with backpropagation and the chain rule.Hence, the backpropagation fails if intermediate gradients are zero for some functions in the computational graph, because it causes the gradients to collapse when multiplying with zero.Vector quantization is one of those challenging functions for machine learning algorithms, since it is a piece-wise constant function and its gradient is zero almost everywhere.A typical solution is to apply the straight through estimator which simply copies the gradients over the vector quantization function in the backpropagation.Other solutions are based on smooth or stochastic approximation.This study proposes a vector quantization technique called NSVQ, which approximates the vector quantization behavior by substituting a multiplicative noise so that it can be used for machine learning problems.Specifically, the vector quantization error is replaced by product of the original error and a normalized noise vector, the samples of which are drawn from a zero-mean, unit-variance normal distribution.We test our proposed NSVQ in three scenarios with various types of applications.Based on the experiments, the proposed NSVQ achieves more accuracy and faster convergence in comparison to the straight through estimator, exponential moving averages, and the MiniBatchKmeans approaches.
Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs. With the increase of the number of courses, it is difficult for learners to find the courses they want quickly and accurately; that is, they encounter the problems of information overload and cognitive maze. Therefore, how to recommend personalized courses for learners according to their preferences has become one of the important problems that need to be solved urgently to improve the service quality of courses in online learning platforms. Therefore, in order to improve the accuracy of course recommendation, it is necessary to build an accurate and complete learner model. In order to improve the application effect of recommendation, this paper focuses on the recommendation method of emotional factors to improve the recommendation efficiency of learning resources. The traditional recommendation model is a method based on the user’s purchase behavior and historical information. However, in the emotional factors, the effect of traditional recommendation is limited. This paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. The experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation.
Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints

Deep learning based medical image registration remains very difficult and often fails to improve over its classical counterparts where comprehensive supervision is not available, in particular for large transformations-including rigid alignment. The use of unsupervised, metric-based registration networks has become popular, but so far no universally applicable similarity metric is available for multimodal medical registration, requiring a trade-off between local contrast-invariant edge features or more global statistical metrics. In this work, we aim to improve over the use of handcrafted metric-based losses. We propose to use synthetic three-way (triangular) cycles that for each pair of images comprise two multimodal transformations to be estimated and one known synthetic monomodal transform. Additionally, we present a robust method for estimating large rigid transformations that is differentiable in end-to-end learning. By minimising the cycle discrepancy and adapting the synthetic transformation to be close to the real geometric difference of the image pairs during training, we successfully tackle intra-patient abdominal CT-MRI registration and reach performance on par with state-of-the-art metric-supervision and classic methods. Cyclic constraints enable the learning of cross-modality features that excel at accurate anatomical alignment of abdominal CT and MRI scans.
Deploying Efficiently Modern Applications on Cloud

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 s even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.
Digital Tools of Universal Music Education

The purpose of this article is to present various solutions concerning music education aided by computer technologies. The article applies public music education. The author attempts to provide an answer to questions concerning the role of music teachers working with new media , which requires them to constantly improve and expand their skills. How are they able to utilise new technological achievements while at the same time blending them with well-proven, traditional methods of music teaching/learning, without falling prey to the dangers of modern media ? Any attempts at using innovative solutions are bound to cause numerous challenges for students, teachers, and the entire education system. However, the effects of such actions could contribute to the improvement of the quality of music education in society, which justifies the efforts. The intention of the author is attempt to look into the future on the basis of the existing data sources, analyses and global pedagogical trends and to search for theoretical and practical solutions, which may influence the formulation of the paradigms in modern music teaching.
Discovering phonetic inventories with crosslingual automatic speech recognition

The high cost of data acquisition makes Automatic Speech Recognition (ASR) model training problematic for most existing languages, including languages that do not even have a written script, or for which the phone inventories remain unknown. Past works explored multilingual training, transfer learning, as well as zero-shot learning in order to build ASR systems for these low-resource languages. While it has been shown that the pooling of resources from multiple languages is helpful, we have not yet seen a successful application of an ASR model to a language unseen during training. A crucial step in the adaptation of ASR from seen to unseen languages is the creation of the phone inventory of the unseen language. The ultimate goal of our work is to build the phone inventory of a language unseen during training in an unsupervised way without any knowledge about the language. In this paper, we (1) investigate the influence of different factors (i.e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language; (2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation; and (3) present different methods to build a phone inventory of an unseen language in an unsupervised way. To that end, we conducted mono-, multi-, and crosslingual experiments on a set of 13 phonetically diverse languages and several in-depth analyses. We found a number of universal phone tokens (IPA symbols) that are well-recognized cross-linguistically. Through a detailed analysis of results, we conclude that unique sounds, similar sounds, and tone languages remain a major challenge for phonetic inventory discovery.